WO2022016901A1

WO2022016901A1 - Method for planning driving route of vehicle, and intelligent vehicle

Info

Publication number: WO2022016901A1
Application number: PCT/CN2021/084330
Authority: WO
Inventors: 古强; 庄雨铮; 王志涛; 刘武龙
Original assignee: 华为技术有限公司
Priority date: 2020-07-20
Filing date: 2021-03-31
Publication date: 2022-01-27
Also published as: CN113954858A

Abstract

A method for planning a driving route of a vehicle, which can be applied to an intelligent vehicle and an intelligent and connected vehicle. The method may comprise: acquiring first information, wherein the first information comprises one or more pieces of position information, lane information, navigation information and obstacle information of a vehicle; when the vehicle is in an autonomous driving mode, the first information being input data of a first model, and output data of the first model being used for planning a driving route for the vehicle, wherein the first model is a model obtained by means of taking a driving trajectory of the vehicle in a manual driving mode as a training target and training second information acquired in the manual driving mode, the type of information included in the second information is consistent with the type of information included in the first information, and the similarity between the first information and the second information meets a preset condition; and when the vehicle is in the manual driving mode, the first information being used for training the first model. By means of the provided solution, the takeover rate of a driver can be reduced for a large number of long tail scenarios.

Description

A method for planning a driving route of a vehicle and a smart car

This application claims the priority of the Chinese patent application filed on July 20, 2020 with the application number 202010698231.X and titled "A method for planning a vehicle driving route and a smart car", the entire contents of which are approved by Reference is incorporated in this application.

technical field

The present application relates to the field of automatic driving, and in particular, to a method for planning a driving route of a vehicle and a smart car.

Background technique

Artificial intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that responds in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that the machines have the functions of perception, reasoning and decision-making. Research in the field of artificial intelligence includes robotics, natural language processing, computer vision, decision-making and reasoning, human-computer interaction, recommendation and search, and basic AI theory.

Autopilot is a mainstream application in the field of artificial intelligence. Autopilot technology relies on the cooperation of computer vision, radar, monitoring devices and global positioning systems to allow motor vehicles to achieve autonomous driving without the need for human active operation. Autonomous vehicles use various computing systems to help transport passengers from one location to another. Some autonomous vehicles may require some initial or continuous input from an operator, such as a pilot, driver, or passenger. An autonomous vehicle permits the operator to switch from a manual mode of operation to an autonomous driving mode or a mode in between. Since automatic driving technology does not require humans to drive motor vehicles, it can theoretically effectively avoid human driving errors, reduce the occurrence of traffic accidents, and improve the efficiency of highway transportation. Therefore, autonomous driving technology is getting more and more attention.

In the field of autonomous driving technology, the route planning of the autonomous vehicle can realize the route selection and route optimization of the autonomous vehicle (the route is also called a path or trajectory), and further realize a better control strategy. However, the actual driving conditions of autonomous vehicles are very complex. Affected by weather, temperature and humidity, special obstacles, and road conditions, a large number of long-tail scenarios will be formed. Long-tail scenarios refer to the scenarios faced by autonomous vehicles. There are a large number of atypical and different scenarios, which are difficult to handle with a unified rule. Therefore, how to plan the driving routes of vehicles for a large number of long-tail scenarios needs to be solved urgently. The present application provides a method for a vehicle driving route and related equipment, which can reduce the driver's takeover rate for a large number of long-tail scenarios.

SUMMARY OF THE INVENTION

The present application provides a method for a vehicle driving route and related equipment, which can reduce the driver's takeover rate for a large number of long-tail scenarios.

In order to solve the above-mentioned technical problems, the application provides the following technical solutions:

A first aspect of the present application provides a method for planning a driving route of a vehicle, which can be used in the field of automatic driving in the field of artificial intelligence. It may include: acquiring first information, where the first information may include one or more of vehicle position information, lane information, navigation information, and obstacle information. For example, the first information may include a type of information, for example, the first information includes position information of the vehicle. Or the first information may include two kinds of information, for example, the first information includes position information and lane information of the vehicle. Or the first information may include three kinds of information, for example, the first information includes position information of the vehicle, lane information, and navigation information. Or the first information may include four kinds of information, for example, the first information includes vehicle position information, lane information, navigation information and obstacle information. The lane information is used to determine the relative position of the vehicle and the lane line, the navigation information is used to predict the driving direction of the vehicle, and the obstacle information is used to determine the relative position of the vehicle and the obstacle. Environment information around the vehicle may be determined through the acquired first information, and the environment information around the vehicle may be used to determine whether the vehicle has ever entered the same or similar scene. In some scenarios with simple road conditions, it can be considered that as long as the position information of the vehicles is consistent, the same scenario can be determined. In some scenes with complex road conditions, it may be necessary to determine the same scene only when the vehicle's position information, lane information, navigation information and obstacle information are consistent. When the vehicle is in the automatic driving mode, the first information is the input data of the first model, the output data of the first model is used to plan the driving route for the vehicle, and the first model takes the driving trajectory of the vehicle in the manual driving mode as the training target. A model obtained by training the second information obtained in the manual driving mode, the types of information that the second information can include is consistent with the types of information that the first information can include, and the similarity between the first information and the second information satisfies a preset condition , the similarity between the first information and the second information meets a preset condition to indicate that the first scene and the second scene are the same or similar, the first scene is the scene where the vehicle is when the vehicle obtains the first information, and the second scene is the vehicle The scene in which the vehicle is located when the second information is acquired. For example, the first information includes the location information of the vehicle as an example for description, where the location information may include the latitude and longitude information of the location where the vehicle is located. For example, the first information includes first longitude and latitude information, and the second information includes second longitude and latitude information. If the difference between the two is less than a preset threshold, it can be considered that the similarity between the first information and the second information meets the preset condition. If If the difference between the two is greater than the preset threshold, it can be considered that the similarity between the first information and the second information does not meet the preset condition. When the vehicle is in the manual driving mode, the first information is used to train the first model, and the output data of the first model is used to plan a driving route for the vehicle. It can be known from the first aspect that the solution provided by the present application acquires the environmental information around the vehicle in real time, such as the first information and the second information. When the vehicle is in the manual driving mode, the model is trained with the environment information around the vehicle as the training data, so that the trajectory output by the model can be close to the driving trajectory of the vehicle in the manual driving mode. When the vehicle is in the automatic driving mode, if it is determined that the similarity between the environmental information of the vehicle and the surrounding environmental information of the vehicle used for model training meets the preset conditions, the environmental information around the vehicle is used as the input data of the model, and the output of the model The data plans the driving route for the vehicle. The frequency of taking over by the driver can be reduced by the solution provided by this application.

Optionally, in combination with the above-mentioned first aspect, in a first possible implementation manner, the method may further include: evaluating the similarity between the first information and M groups of information, where each group of information in the M groups of information is a vehicle For the information obtained in the manual driving mode, the similarity of any two groups of information in the M groups of information does not meet the preset condition, and each group of information in the M groups of information is used to train a model respectively. When the vehicle is in the automatic driving mode, the first information is the input data of the first model, which may include: the similarity between the first information and the second information in the M groups of information satisfies a preset condition, and the second information in the M groups of information is similar to the first information. When the similarity of one piece of information is the largest, the first piece of information is the input data of the first model. It can be seen from the first possible implementation of the first aspect that when the vehicle is in the automatic driving mode, the similarity of the scene (surrounding environment information) where the vehicle is located can be evaluated by evaluating the similarity between the first information and the M groups of information. The similarity between the information and the M groups of information satisfies the preset condition, that is, it is considered that the similarity between the current scene of the vehicle and the scene corresponding to the existing available model exceeds the threshold, and the available model with the highest similarity can be selected from the M models. The acquired periodic environment information (eg, the first information) is used as the input of the available model with the highest similarity (eg, the first model), and the output data of the available model with the highest similarity can plan a driving route for the vehicle.

Optionally, in combination with the above-mentioned first aspect, in a second possible implementation manner, the method may further include: evaluating the similarity between the first information and M groups of information, where each group of information in the M groups of information is a vehicle For the information obtained in the manual driving mode, the similarity of any two groups of information in the M groups of information does not meet the preset conditions, each group of information in the M groups of information is used to train a model, and the second group of information in the M groups of information is used to train a model. The information is used to train the first model, and M is a positive integer. When the vehicle is in the manual driving mode, the first information is used to train the first model, which may include: the similarity between the first information and the second information in the M groups of information satisfies a preset condition, and the second information in the M groups of information is similar to the first information. When the similarity of the information is the largest, the first information is used to train the first model to obtain the updated first model. It can be seen from the second possible implementation of the first aspect that if the similarity between the current scene and the scene corresponding to the existing available model exceeds the threshold, the available model with the highest similarity is selected, for example, the first model has the highest similarity. The first model is updated and trained by using the first information, and the available model for the scene is updated, that is, the first model is updated.

Optionally, in combination with the above-mentioned first aspect, in a third possible implementation manner, the method may further include: evaluating the similarity between the first information and M groups of information, where each group of information in the M groups of information is a vehicle For the information obtained in the manual driving mode, the similarity of any two groups of information in the M groups of information does not meet the preset condition, and each group of information in the M groups of information is used to train a model, and M is a positive integer. When the vehicle is in the manual driving mode, the first information is used to train the first model, which may include: when the similarity between the first information and each group of information in the M groups of information does not meet the preset condition, the first information is used for training the first model. The first model is trained for the first time. When the similarity between the first information and each of the M groups of information does not meet the preset condition, it can be considered that there is no scene identical or similar to the scene where the vehicle is currently located in the historical manual driving mode. There is no output data from the model available at this time to plan a driving route for the vehicle. Then, using the first information, a temporary model is constructed for training, and a temporary model for the scene is formed, that is, the first model is trained for the first time by using the first information. At this time, the first model may not meet the training target, that is, the distribution of the output trajectory of the first model and the driving trajectory of the vehicle in the manual driving mode is not within the preset range. Then the first model trained for the first time is a temporary model, a non-available model. When in the manual driving mode, the information of the similarity with the first information is obtained for many times, and the first model is iteratively trained until the first model. If the model meets the training target, the first model is an available model, and the first model that is an available model at this time can plan a driving route for the vehicle.

Optionally, in combination with the first aspect or the third possible implementation manner of the first aspect to the first aspect, in a fourth possible implementation manner, when the vehicle is in an automatic driving mode, the method may further include: : When it is determined that the current position information of the vehicle is consistent with the position information of the vehicle obtained in the manual driving mode, a prompt message is sent, and the prompt message is used to instruct the vehicle to plan a driving route for the vehicle according to the output data of the first model. The model obtained by training the position information of the vehicle obtained in the driving mode.

Optionally, in combination with the first aspect or the third possible implementation manner of the first aspect to the first aspect, in a fifth possible implementation manner, when the vehicle is in a manual driving mode, the method may further include : Send a prompt message, where the prompt message is used to instruct to train the first model according to the current position information of the vehicle.

Optionally, in combination with the first aspect or the fifth possible implementation manner of the first aspect to the first aspect, in a sixth possible implementation manner, the method may further include: determining the first information and the first The similarity of the same type of information in the two pieces of information, and the linear weighted sum of the similarity of the same type of information is used to determine the similarity of the first information and the second information.

Optionally, in combination with the first aspect or the sixth possible implementation manner of the first aspect to the first aspect, in the seventh possible implementation manner, the first model may include a convolutional neural network CNN or a loop. Neural network RNN. It can be known from the seventh possible implementation manner of the first aspect that two possible first models are provided, which increases the diversity of solutions.

A second aspect of the present application provides an apparatus for planning a driving route of a vehicle, which may include: an acquisition module configured to acquire first information, where the first information may include one of vehicle location information, lane information, navigation information, and obstacle information. One or more, the lane information is used to determine the relative position of the vehicle and the lane line, the navigation information is used to predict the driving direction of the vehicle, and the obstacle information is used to determine the relative position of the vehicle and the obstacle. The regulation and control module is used to plan a driving route for the vehicle according to the output data of the first model when the vehicle is in the automatic driving mode, and the first information obtained by the acquisition module is the input data of the first model, and the first model is based on the manual driving mode. The driving trajectory of the vehicle is the training target, and the model is obtained by training the second information obtained in the manual driving mode. The types of information that the second information can include are consistent with the types of information that the first information can include. The similarity of the two information satisfies the preset condition. The training module is used for training the first model according to the first information obtained by the obtaining module when the vehicle is in the manual driving mode.

Optionally, in combination with the above second aspect, in a first possible implementation manner, an evaluation module may further include: an evaluation module configured to evaluate the similarity between the first information and the M groups of information, and each group of information in the M groups of information Both are information obtained when the vehicle is in the manual driving mode, the similarity of any two groups of information in the M groups of information does not meet the preset condition, and each group of information in the M groups of information is used to train a model respectively. When the similarity between the first information and the second information in the M groups of information satisfies a preset condition, and the similarity between the second information and the first information in the M groups of information is the largest, the first information is the input data of the first model.

Optionally, in combination with the above-mentioned second aspect, in a second possible implementation manner, it may further include: an evaluation module, configured to evaluate the similarity between the first information and the M groups of information, each group of information in the M groups of information are the information obtained when the vehicle is in manual driving mode. The similarity of any two groups of information in the M group of information does not meet the preset conditions. Each group of information in the M group of information is used to train a model, and the M group of information The second information of is used to train the first model, M is a positive integer. When the similarity between the first information and the second information in the M groups of information satisfies a preset condition, and the similarity between the second information and the first information in the M groups of information is the largest, the first information is used to train the first model to obtain an update After the first model.

Optionally, in combination with the above-mentioned second aspect, in a third possible implementation manner, an evaluation module may further include: an evaluation module configured to evaluate the similarity between the first information and the M groups of information, and each group of information in the M groups of information Both are information obtained when the vehicle is in manual driving mode. The similarity of any two groups of information in the M groups of information does not meet the preset conditions. Each group of information in the M groups of information is used to train a model, and M is a positive integer. . When the similarity between the first information and each of the M groups of information does not meet the preset condition, the first information is used to train the first model for the first time.

Optionally, in combination with the second aspect or the third possible implementation manner of the first to the second aspect, the fourth possible implementation manner may further include: a sending module, configured for the evaluation module to determine When the current position information of the vehicle is consistent with the position information of the vehicle obtained in the manual driving mode, a prompt message is sent. The prompt message is used to instruct the vehicle to plan a driving route for the vehicle according to the output data of the first model. The first model is for manual driving. The model obtained by training the position information of the vehicle obtained in the mode.

Optionally, in combination with the above-mentioned second aspect or the third possible implementation manner of the first to the second aspect, in a fifth possible implementation manner, it may further include: a sending module, configured to send a prompt message , the prompt message is used to instruct to train the first model according to the current position information of the vehicle.

Optionally, in combination with the second aspect or the fifth possible implementation manner of the first to the second aspect, the sixth possible implementation manner may further include: a processing module configured to determine the first The similarity of the same type of information in the information and the second information, and the linear weighted sum of the similarity of the same type of information is used to determine the similarity of the first information and the second information.

Optionally, in combination with the second aspect or the sixth possible implementation manner of the second aspect or the first to the second aspect, in the seventh possible implementation manner, the first model may include a convolutional neural network CNN or a loop. Neural network RNN.

A third aspect of the present application provides a system for planning a driving route of a vehicle. The system may include a vehicle and a cloud-side device, and the vehicle is used to obtain first information, and the first information may include location information, lane information, navigation information, obstacles of the vehicle. The lane information is used to determine the relative position of the vehicle and the lane line, the navigation information is used to predict the driving direction of the vehicle, and the obstacle information is used to determine the relative position of the vehicle and the obstacle. The vehicle is also used to send the first information and the driving mode of the vehicle to the cloud-side device. The cloud-side device is used to determine that when the vehicle is in the automatic driving mode, plan the driving route for the vehicle according to the output data of the first model. The first information is the input data of the first model, and the first model is the driving route of the vehicle in the manual driving mode. The trajectory is the training target, and the model is obtained by training the second information obtained in the manual driving mode. The type of information that the second information can include is consistent with the type of information that the first information can include, and the difference between the first information and the second information is the same. The similarity satisfies the preset condition. The cloud-side device is further configured to train the first model according to the first information when it is determined that the vehicle is in the manual driving mode.

Optionally, in combination with the above third aspect, in a first possible implementation manner, the cloud-side device is further configured to: evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is is the information obtained when the vehicle is in the manual driving mode, the similarity of any two groups of information in the M groups of information does not meet the preset condition, and each group of information in the M groups of information is used to train a model respectively. When the similarity between the first information and the second information in the M groups of information satisfies a preset condition, and the similarity between the second information and the first information in the M groups of information is the largest, the first information is the input data of the first model.

Optionally, in combination with the above third aspect, in a first possible implementation manner, the cloud-side device is further configured to: evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is It is the information obtained when the vehicle is in the manual driving mode. The similarity of any two groups of information in the M groups of information does not meet the preset conditions, and each group of information in the M groups of information is used to train a model, respectively. The second information is used to train the first model, and M is a positive integer. When the similarity between the first information and the second information in the M groups of information satisfies a preset condition, and the similarity between the second information and the first information in the M groups of information is the largest, the first information is used to train the first model to obtain an update After the first model.

Optionally, in combination with the above third aspect, in a third possible implementation manner, the cloud-side device is further configured to evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is For the information obtained when the vehicle is in manual driving mode, the similarity of any two groups of information in the M groups of information does not meet the preset conditions, and each group of information in the M groups of information is used to train a model, and M is a positive integer. When the similarity between the first information and each of the M groups of information does not meet the preset condition, the first information is used to train the first model for the first time.

Optionally, in combination with the third aspect or the third possible implementation manners of the first to third aspects of the third aspect, in a fourth possible implementation manner, the cloud-side device is also used to determine the current position of the vehicle. When the information is consistent with the position information of the vehicle obtained in the manual driving mode, a prompt message is sent. The prompt message is used to instruct the vehicle to plan a driving route for the vehicle according to the output data of the first model. The first model is for the vehicle obtained in the manual driving mode. The model obtained by training the location information.

Optionally, in combination with the third aspect or the third possible implementation manners of the first to third aspects of the third aspect, in a fifth possible implementation manner, the cloud-side device is further configured to send a prompt message, prompting The message is used to instruct to train the first model according to the current position information of the vehicle.

Optionally, in combination with the third aspect or the fifth possible implementation manner of the third aspect or the first to the third aspect, in the sixth possible implementation manner, the cloud-side device is further configured to determine the first information and The similarity of the same type of information in the second information and the linear weighted sum of the similarity of the same type of information are used to determine the similarity between the first information and the second information.

Optionally, in conjunction with the third aspect or the sixth possible implementation manner of the third aspect or the first to the third aspect, in the seventh possible implementation manner, the first model may include a convolutional neural network (convolutional neural network). network, CNN) or recurrent neural network (RNN).

A fourth aspect of the present application provides an apparatus for planning a driving route of a vehicle, which may include a processor, the processor is coupled with a memory, the memory stores program instructions, and the first aspect or the first aspect is executed when the program instructions stored in the memory are executed by the processor. On the one hand, the method for planning a driving route of a vehicle described in any possible implementation manner.

A fifth aspect of the present application provides a computer-readable storage medium, which may include a program that, when executed on a computer, causes the computer to execute the planning vehicle described in the first aspect or any possible implementation manner of the first aspect. The method of driving the route.

A sixth aspect of the present application provides a smart car. The smart car may include a processing circuit and a storage circuit. The processing circuit and the storage circuit are configured to execute the planning described in the first aspect or any possible implementation manner of the first aspect. The method of vehicle travel route.

A seventh aspect of the present application provides a circuit system. The circuit system may include a processing circuit, and the processing circuit is configured to execute the method for planning a vehicle travel route described in the first aspect or any possible implementation manner of the first aspect.

An eighth aspect of the present application provides a computer program that, when running on a computer, causes the computer to execute the method for planning a vehicle travel route described in the first aspect or any possible implementation manner of the first aspect.

A ninth aspect of the present application provides a chip system, the chip system may include a processor for supporting a cloud-side device or an apparatus for planning a vehicle driving route to implement the functions involved in the above aspects, for example, sending or processing the above methods. the data and/or information involved. In a possible design, the chip system may further include a memory for storing necessary program instructions and data of a server or a communication device. The chip system may be composed of chips, or may include chips and other discrete devices.

For the specific implementation steps of the second to ninth aspects of the present application and various possible implementations, as well as the beneficial effects brought by each possible implementation, reference may be made to the descriptions in the various possible implementations in the first aspect , and will not be repeated here.

Description of drawings

FIG. 1a is a schematic diagram of an application scenario for planning a vehicle driving route provided by the application;

FIG. 1b is a schematic diagram of another application scenario of planning a vehicle driving route provided by the application;

FIG. 1c is a schematic diagram of another application scenario of planning a vehicle driving route provided by the present application;

2a is a schematic diagram of another application scenario of planning a vehicle driving route provided by the application;

FIG. 2b is a schematic diagram of another application scenario of planning a vehicle driving route provided by the application;

FIG. 2c is a schematic diagram of another application scenario of planning a vehicle driving route provided by the present application;

3 is a schematic structural diagram of an automatic driving vehicle provided by an embodiment of the present application;

4 is a schematic flowchart of a method for planning a vehicle travel route provided by the application;

5a is a schematic diagram of another application scenario of planning a vehicle driving route provided by the application;

FIG. 5b is a schematic diagram of another application scenario of planning a vehicle driving route provided by the application;

FIG. 5c is a schematic diagram of another application scenario of planning a vehicle driving route provided by the present application;

FIG. 5d is a schematic diagram of another application scenario of planning a vehicle driving route provided by the application;

6 is a schematic flowchart of a method for planning a vehicle driving route provided by an embodiment of the present application;

7 is a schematic flowchart of another method for planning a vehicle driving route provided by an embodiment of the present application;

FIG. 8a is a schematic diagram of another application scenario of planning a vehicle driving route provided by the present application;

FIG. 8b is a schematic diagram of another application scenario of planning a vehicle driving route provided by the application;

9 is a schematic diagram of a scenario of another method for planning a vehicle travel route provided by the application;

10 is a schematic structural diagram of an apparatus for planning a driving route of a vehicle provided by an embodiment of the application;

FIG. 11 is a schematic structural diagram of an automatic driving vehicle provided by an embodiment of the present application;

FIG. 12 is a schematic structural diagram of a chip provided by an embodiment of the present application.

detailed description

The embodiment of the present application provides a method and related equipment for planning a driving route of a vehicle. Taking the driving trajectory of the vehicle in the manual driving mode as the training target, the motion information and the surrounding environment information of the vehicle obtained in the manual driving mode are trained to obtain The first model, when the vehicle passes through the same or similar scene again, can plan a driving route for the vehicle according to the output data of the first model.

The terms "first", "second", etc. in the description and claims of the present application and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way Can be interchanged under appropriate circumstances, this is only the way of distinction adopted when describing the objects of the same property in the embodiments of the present application. In addition, the terms "comprising" and "having" and any of their deformations are intended to be Non-exclusive inclusion is covered so that a process, method, system, product or device comprising a series of units is not necessarily limited to those units, but may include other units not expressly listed or inherent to those processes, methods, products or devices .

The embodiments of the present application may be applied to scenarios in which routes are planned for various autonomous driving agents. As an example, for example, the embodiments of the present application may be applied to scenarios of route planning for autonomous vehicles. Specifically, in autonomous driving In the scenario of , many complex working conditions do not have significant common characteristics (such as related to specific road sections and specific road conditions), and it is difficult to use a unified control method to deal with these special working conditions. When driving in such a scenario, the takeover rate often cannot be reduced, that is, the driver is always required to take over the vehicle. In addition, different scenarios require different strategies, and different users have different driving styles. The current autonomous driving regulation is difficult to customize for specific scenarios and specific users, and to provide personalized and customized regulation strategies. When the autonomous vehicle passes through a road environment with many obstacles that hinder the perception of information, such as a large number of vehicles parked on the roadside, traffic congestion, passing a bus stop, roadside guardrails during road construction, encountering bad weather, or other large numbers of vehicles When the road environment of an obstacle that hinders information perception, the autonomous vehicle will require the driver to take over the vehicle. Exemplarily, as shown in FIG. 1a to FIG. 1c, the present application is a schematic diagram of an application scenario of planning a vehicle driving route. The scene shown in Figure 1a to Figure 1c is a parking lot scene. As shown in Figure 1a, there is an obstacle in the front right of the parking space where the vehicle is located. Assuming that the trajectory of automatic parking out of the parking space planned by automatic driving, only supports forwarding to the right The situation of the parking space. Under the current working conditions, there is not enough space to exit the parking space to the right, as shown in Figure 1b, there may be multiple collisions, and the current trajectory planning algorithm cannot plan a reasonable trajectory. At this time, the driver intervenes to take over the vehicle. As shown in Figure 1c, the driver can drive out of the parking lot after the vehicle is perpendicular to the parking space in a left-forward manner. As shown in FIG. 2a to FIG. 2c, another application scenario schematic diagram of planning a vehicle driving route provided by the present application. The scenarios shown in Figure 2a to Figure 2c are scenarios where vehicles are driving on the road. The autonomous vehicle is driving on a road with two lanes in the same direction. As shown in Figure 2a, the left lane has a continuous manhole cover. The trajectory planned by the module is to drive along the centerline of the left lane, resulting in poor passenger comfort during driving. After the driver takes over the vehicle manually, as shown in Figure 2b, the driver can drive with the centerline of the left lane slightly to the right, and the vehicle remains in the left lane, but the left wheel will no longer press the manhole cover continuously. Or as shown in Figure 2c, when there are no other vehicles in the right lane, the driver can also manually switch to the right lane for driving.

When the driver takes over, in order to reduce the automatic driving takeover rate, the log of this scenario can be analyzed. The conclusion of the analysis is that the current version of the regulation and control module (the regulation and control module will be introduced below) cannot support such scenarios. Trajectory planning. For example, in the scenarios shown in Figures 1a to 1c, the current version of the regulation and control module cannot support the route planning of parking spaces in such scenarios. In the scenarios shown in Figures 2a to 2c, the current version of the regulation and control module cannot support Trajectory planning for such scenarios is supported. Therefore, it is necessary to re-analyze the design in the current regulation and control module, and add trajectory planning logic for this scenario (eg, similar to the driver's driving trajectory). Then, when passing through this scene later, the trajectory of the parking space can be automatically realized, or the trajectory of avoiding the continuous manhole cover can be automatically realized. Every time such a scheme is taken over, it is necessary to analyze the scene separately and formulate a revised scheme. The scheme is cumbersome, inefficient, and costly. In addition, similar to the above example, there are many long-tail scenarios, which are difficult to handle with a unified regulation method. As in the above scenarios of FIGS. 1 a to 1 c , it is necessary to borrow part of the space on the left side of the parking space. In other parking space scenarios, if the newly added planning rules in this scenario are also used, it may lead to unsafe situations such as collisions, dangers, and manual takeovers in other scenarios, which still cannot reduce the takeover rate of automatic driving.

Therefore, it is necessary to plan routes for autonomous vehicles through a vehicle route planning scheme to cope with a large number of long-tail scenarios. As another example, for example, the embodiments of the present application can also be applied to scenarios of route planning for various types of robots, such as freight robots, detection robots, sweeping robots, or other types of robots. Here, a freight robot is used as an example for the application The scenario is further described. When multiple cargo robots work at the same time, or when the cargo robots shuttle between the shelves, other cargo robots and shelves will become obstacles that hinder the cargo robots from information perception. In the environment, in order to reduce the collision between the cargo robots and between the cargo robots and people, the route planning of the cargo robots can be performed through the solution provided in this application. It should be understood that the examples here are only for the convenience of understanding the application scenarios of the embodiments of the present application, and the application scenarios of the embodiments of the present application are not exhaustive. for a detailed introduction.

The embodiments of the present application will be described below with reference to the accompanying drawings. Those of ordinary skill in the art know that with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

In order to facilitate the understanding of this solution, the structure of the automatic driving vehicle is first introduced in the embodiment of the present application with reference to FIG. 3 . Please refer to FIG. 3 first. FIG. 3 is a schematic structural diagram of the automatic driving vehicle provided by the embodiment of the The vehicle 100 is configured in a fully or partially autonomous driving mode, for example, the autonomous vehicle 100 may control itself while in the autonomous driving mode, and may determine the current state of the vehicle and its surrounding environment through human operation, determine the possible behavior of at least one other vehicle, and determine a confidence level corresponding to the possibility that the other vehicle performs the possible behavior, and control the autonomous vehicle 100 based on the determined information. The autonomous vehicle 100 may also be placed to operate without human interaction when the autonomous vehicle 100 is in the autonomous driving mode.

Autonomous vehicle 100 may include various subsystems, such as travel system 102 , sensor system 104 , control system 106 , one or more peripherals 108 and power supply 110 , computer system 112 , and user interface 116 . Alternatively, the autonomous vehicle 100 may include more or fewer subsystems, and each subsystem may include multiple components. Additionally, each of the subsystems and components of the autonomous vehicle 100 may be wired or wirelessly interconnected.

The travel system 102 may include components that provide powered motion for the autonomous vehicle 100 . In one embodiment, travel system 102 may include engine 118 , energy source 119 , transmission 120 , and wheels 121 .

The engine 118 may be an internal combustion engine, an electric motor, an air compression engine, or other types of engine combinations, such as a hybrid engine composed of a gasoline engine and an electric motor, and a hybrid engine composed of an internal combustion engine and an air compression engine. Engine 118 converts energy source 119 into mechanical energy. Examples of energy sources 119 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity. The energy source 119 may also provide energy to other systems of the autonomous vehicle 100 . Transmission 120 may transmit mechanical power from engine 118 to wheels 121 . Transmission 120 may include a gearbox, a differential, and a driveshaft. In one embodiment, transmission 120 may also include other devices, such as clutches. Among other things, the drive shaft may include one or more axles that may be coupled to one or more wheels 121 .

The sensor system 104 may include several sensors that sense information about the environment surrounding the autonomous vehicle 100 . For example, the sensor system 104 may include a global positioning system 122 (the positioning system may be a global positioning GPS system, a Beidou system or other positioning systems), an inertial measurement unit (IMU) 124, a radar 126, a laser ranging instrument 128 and camera 130. The sensor system 104 may also include sensors that monitor the internal systems of the autonomous vehicle 100 (eg, an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, etc.). Sensing data from one or more of these sensors can be used to detect objects and their corresponding properties (position, shape, orientation, velocity, etc.). This detection and identification is a critical function for the safe operation of the autonomous autonomous vehicle 100 .

Among others, the positioning system 122 may be used to estimate the geographic location of the autonomous vehicle 100 . The IMU 124 is used to sense position and orientation changes of the autonomous vehicle 100 based on inertial acceleration. In one embodiment, IMU 124 may be a combination of an accelerometer and a gyroscope. The radar 126 can use radio signals to perceive objects in the surrounding environment of the autonomous vehicle 100 , and can be embodied as a millimeter-wave radar or a lidar. In some embodiments, in addition to sensing objects, radar 126 may be used to sense the speed and/or heading of objects. The laser rangefinder 128 may utilize the laser light to sense objects in the environment in which the autonomous vehicle 100 is located. In some embodiments, the laser rangefinder 128 may include one or more laser sources, laser scanners, and one or more detectors, among other system components. Camera 130 may be used to capture multiple images of the surrounding environment of autonomous vehicle 100 . Camera 130 may be a still camera or a video camera.

The control system 106 controls the operation of the autonomous vehicle 100 and its components. Control system 106 may include various components including steering system 132 , throttle 134 , braking unit 136 , computer vision system 140 , line control system 142 , and obstacle avoidance system 144 .

Among other things, the steering system 132 is operable to adjust the heading of the autonomous vehicle 100 . For example, in one embodiment it may be a steering wheel system. The throttle 134 is used to control the operating speed of the engine 118 and thus the speed of the autonomous vehicle 100 . The braking unit 136 is used to control the deceleration of the autonomous vehicle 100 . The braking unit 136 may use friction to slow the wheels 121 . In other embodiments, the braking unit 136 may convert the kinetic energy of the wheels 121 into electrical current. The braking unit 136 may also take other forms to slow the wheels 121 to control the speed of the autonomous vehicle 100 . Computer vision system 140 may be operable to process and analyze images captured by camera 130 in order to identify objects and/or features in the environment surrounding autonomous vehicle 100 . The objects and/or features may include traffic signals, road boundaries and obstacles. Computer vision system 140 may use object recognition algorithms, Structure from Motion (SFM) algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system 140 may be used to map the environment, track objects, estimate the speed of objects, and the like. The route control system 142 is used to determine the travel route and travel speed of the autonomous vehicle 100 . In some embodiments, the route control system 142 may include a lateral planning module 1421 and a longitudinal planning module 1422, respectively, for combining information from the obstacle avoidance system 144, the GPS 122, and one or more predetermined maps The data for the autonomous vehicle 100 determines the driving route and driving speed. Obstacle avoidance system 144 is used to identify, evaluate and avoid or otherwise traverse obstacles in the environment of autonomous vehicle 100 , which may be embodied as actual obstacles and virtual moving objects that may collide with autonomous vehicle 100 . In one example, the control system 106 may additionally or alternatively include components in addition to those shown and described. Alternatively, some of the components shown above may be reduced.

The autonomous vehicle 100 interacts with external sensors, other vehicles, other computer systems, or users through peripheral devices 108 . Peripherals 108 may include a wireless communication system 146 , an onboard computer 148 , a microphone 150 and/or a speaker 152 . In some embodiments, peripherals 108 provide a means for a user of autonomous vehicle 100 to interact with user interface 116 . For example, the onboard computer 148 may provide information to a user of the autonomous vehicle 100 . User interface 116 may also operate on-board computer 148 to receive user input. The onboard computer 148 can be operated via a touch screen. In other cases, peripherals 108 may provide a means for autonomous vehicle 100 to communicate with other devices located within the vehicle. For example, microphone 150 may receive audio (eg, voice commands or other audio input) from a user of autonomous vehicle 100 . Similarly, speaker 152 may output audio to a user of autonomous vehicle 100 . Wireless communication system 146 may wirelessly communicate with one or more devices, either directly or via a communication network. For example, wireless communication system 146 may use 3G cellular communications, such as, for example, code division multiple access (CDMA), EVDO, global system for mobile communications (GSM), general packet radio service technology (general packet radio service, GPRS), or 4G cellular communications, such as long term evolution (LTE) or 5G cellular communications. The wireless communication system 146 may communicate using a wireless local area network (WLAN). In some embodiments, the wireless communication system 146 may communicate directly with the device using an infrared link, Bluetooth, or ZigBee. Other wireless protocols, such as various vehicle communication systems, for example, wireless communication system 146 may include one or more dedicated short range communications (DSRC) devices, which may include communication between vehicles and/or roadside stations public and/or private data communications.

The power source 110 may provide power to various components of the autonomous vehicle 100 . In one embodiment, the power source 110 may be a rechargeable lithium-ion or lead-acid battery. One or more battery packs of such batteries may be configured as a power source to provide power to various components of the autonomous vehicle 100 . In some embodiments, power source 110 and energy source 119 may be implemented together, such as in some all-electric vehicles.

Some or all of the functions of autonomous vehicle 100 are controlled by computer system 112 . Computer system 112 may include at least one processor 113 that executes instructions 115 stored in a non-transitory computer-readable medium such as memory 114 . Computer system 112 may also be a plurality of computing devices that control individual components or subsystems of autonomous vehicle 100 in a distributed fashion. The processor 113 may be any conventional processor, such as a commercially available central processing unit (CPU). Alternatively, the processor 113 may be a dedicated device such as an application specific integrated circuit (ASIC) or other hardware-based processor. Although FIG. 3 functionally illustrates the processor, memory, and other components of the computer system 112 in the same block, one of ordinary skill in the art will understand that the processor, or memory, may actually include not stored in the same Multiple processors, or memories, within a physical enclosure. For example, memory 114 may be a hard drive or other storage medium located within a different enclosure than computer system 112 . Accordingly, references to processor 113 or memory 114 will be understood to include references to sets of processors or memories that may or may not operate in parallel. Rather than using a single processor to perform the steps described herein, some components such as the steering and deceleration components may each have their own processor that only performs computations related to component-specific functions . In various aspects described herein, the processor 113 may be located remotely from the autonomous vehicle 100 and in wireless communication with the autonomous vehicle 100 . In other aspects, some of the processes described herein are performed on the processor 113 disposed within the autonomous vehicle 100 while others are performed by the remote processor 113, including taking the necessary steps to perform a single maneuver. In some embodiments, memory 114 may include instructions 115 (eg, program logic) executable by processor 113 to perform various functions of autonomous vehicle 100 , including those described above. Memory 114 may also contain additional instructions, including instructions to send data to, receive data from, interact with, and/or control one or more of travel system 102 , sensor system 104 , control system 106 , and peripherals 108 . instruction. For example, taking changing lanes to the right as an example, the human driver needs to perform the following operations: Step 1: Consider safety factors and traffic regulations to determine the timing of changing lanes; Step 2: Plan a driving trajectory; Step 3 : Control the accelerator, brakes and steering wheel to drive the vehicle along a predetermined trajectory. The above operations correspond to autonomous vehicles and can be performed by the behavior planner (BP), motion planner (MoP) and motion controller (Control) of the autonomous vehicle, respectively. Among them, BP is responsible for issuing high-level decisions, MoP is responsible for planning the expected trajectory and speed, and Control is responsible for operating the accelerator and braking steering wheel, so that the autonomous vehicle can reach the target speed according to the target trajectory. It should be understood that the related operations performed by the behavior planner, the motion planner and the motion controller may be that the processor 113 as shown in FIG. In this embodiment of the present application, the behavior planner, the motion planner, and the motion controller are sometimes collectively referred to as a regulation module.

In addition to instructions 115, memory 114 may store data such as road maps, route information, vehicle location, direction, speed, and other such vehicle data, among other information. Such information may be used by the autonomous vehicle 100 and the computer system 112 during operation of the autonomous vehicle 100 in autonomous, semi-autonomous, and/or manual modes. A user interface 116 for providing information to or receiving information from a user of the autonomous vehicle 100 . Optionally, user interface 116 may include one or more input/output devices within the set of peripheral devices 108 , such as wireless communication system 146 , onboard computer 148 , microphone 150 and speaker 152 .

Computer system 112 may control functions of autonomous vehicle 100 based on input received from various subsystems (eg, travel system 102 , sensor system 104 , and control system 106 ) and from user interface 116 . For example, computer system 112 may utilize input from control system 106 to control steering system 132 to avoid obstacles detected by sensor system 104 and obstacle avoidance system 144. In some embodiments, computer system 112 is operable to provide control over many aspects of autonomous vehicle 100 and its subsystems.

Optionally, one or more of these components described above may be installed or associated with the autonomous vehicle 100 separately. For example, memory 114 may exist partially or completely separate from autonomous vehicle 100 . The above-described components may be communicatively coupled together in a wired and/or wireless manner.

Optionally, the above component is just an example. In practical applications, components in each of the above modules may be added or deleted according to actual needs, and FIG. 3 should not be construed as a limitation on the embodiments of the present application. An autonomous vehicle traveling on a road, such as autonomous vehicle 100 above, can identify objects within its surroundings to determine adjustments to current speed. The objects may be other vehicles, traffic control equipment, or other types of objects. In some examples, each identified object may be considered independently, and based on the object's respective characteristics, such as its current speed, acceleration, distance from the vehicle, etc., may be used to determine the speed at which the autonomous vehicle is to adjust.

Optionally, autonomous vehicle 100 or a computing device associated with autonomous vehicle 100 such as computer system 112, computer vision system 140, memory 114 of FIG. traffic, rain, ice on the road, etc.) to predict the behavior of the identified objects. Optionally, each identified object is dependent on the behavior of the other, so it is also possible to predict the behavior of a single identified object by considering all identified objects together. The autonomous vehicle 100 can adjust its speed based on the predicted behavior of the identified object. In other words, the autonomous vehicle 100 can determine what steady state the vehicle will need to adjust to (eg, accelerate, decelerate, or stop) based on the predicted behavior of the object. In this process, other factors may also be considered to determine the speed of the autonomous vehicle 100, such as the lateral position of the autonomous vehicle 100 in the road being traveled, the curvature of the road, the proximity of static and dynamic objects, and the like. In addition to providing instructions to adjust the speed of the autonomous vehicle, the computing device may also provide instructions to modify the steering angle of the autonomous vehicle 100 so that the autonomous vehicle 100 follows a given trajectory and/or maintains a close proximity to the autonomous vehicle 100 safe lateral and longitudinal distances for objects that are not in use (for example, cars in adjacent lanes on the road).

The above-mentioned self-driving vehicle 100 can be a car, a truck, a motorcycle, a bus, a boat, an airplane, a helicopter, a lawn mower, an amusement vehicle, an amusement park vehicle, a construction equipment, a tram, a golf cart, a train, etc. The present application The embodiment is not particularly limited.

In combination with the above description, the embodiments of the present application provide a method for planning a vehicle driving route, which can be applied to the autonomous driving vehicle 100 shown in FIG. 3 . The solution provided by the present application may include at least two working modes, one mode is an automatic driving mode, and the other mode is a manual driving mode. The following descriptions will be given separately for the vehicle in this working mode.

FIG. 4 is a schematic flowchart of a method for planning a driving route of a vehicle provided by the present application.

Referring to FIG. 4 , a method for planning a vehicle driving route provided by an embodiment of the present application may include the following steps:

401. Obtain first information.

The first information includes one or more of the vehicle's position information, lane information, navigation information, and obstacle information. The lane information is used to determine the relative position of the vehicle and the lane line, and the navigation information is used to predict the driving direction of the vehicle. The object information is used to determine the relative position of the vehicle and the obstacle.

Among them, the position information of the vehicle can be completed by means of global positioning system (GPS), real-time kinematic (RTK), camera and lidar. In a possible implementation, the pre-stored map, GPS location information and millimeter wave measurement information can be combined to determine the possible location of the vehicle, and calculate the possible location occurrence probability of the vehicle, so as to determine the specific location of the vehicle. It should be noted that the solution provided by the present application can obtain the position information of the vehicle in various ways, and the method of obtaining the position information of the vehicle in the related art can be adopted in the embodiments of the present application.

The lane lines can be detected by processing the images collected by the image collection equipment installed on the vehicle. For example, a large number of pictures including lane lines can be used as training data to train the target detection model. After the training is completed, the target detection model can recognize the lane lines in the images collected by the image acquisition device, and determine the position of the lane lines in the image. . In a possible implementation, road condition detection can be performed by the installed millimeter wave radar and image acquisition device, and the road condition detection result can be used as lane information to determine the relative position of the vehicle and the lane line. It should be noted that the solution provided by the present application can obtain the lane line information of the vehicle in various ways, and the methods of obtaining the lane line information of the vehicle in the related art can all be adopted in the embodiments of the present application.

Navigation information can be obtained through a navigation request. The navigation request may include location information of the origin of the vehicle and location information of the destination. For example, the vehicle can obtain a navigation request through the user clicking or touching the screen of the in-vehicle navigation, or obtain the navigation request through the user's voice command. The vehicle GPS is used in conjunction with the electronic map to carry out path planning, and then the navigation information of the vehicle can be obtained, and the driving direction of the vehicle can be predicted according to the navigation information. It should be noted that the solution provided in the present application can obtain the navigation information of the vehicle in various ways, and the method of obtaining the navigation information of the vehicle in the related art can be adopted in the embodiments of the present application.

For the obstacle information, in a possible implementation, the vehicle may scan the sector area in front of the vehicle for obstacles through a millimeter-wave radar, and collect images of the area in front of the vehicle through an image acquisition device. According to the field of view of the millimeter-wave radar and the image acquisition device, the fan-shaped area is gridded, so that the obstacles detected by the millimeter-wave radar will be in grid cells. After that, the obstacles detected in the images collected by the image acquisition device are converted into grid cells in the polar coordinate system, and the obstacles detected by the millimeter wave radar in the grid cells and the obstacles detected by the image acquisition device are converted into grid cells. The objects are strictly matched to obtain the final obstacle information. It should be noted that, the solution provided by the present application can obtain obstacle information in various ways, and the method of obtaining obstacle information in the related art can be adopted in the embodiments of the present application.

The first information may include one or more of vehicle location information, lane information, navigation information, and obstacle information. For example, the first information may include a type of information, for example, the first information includes position information of the vehicle. Or the first information may include two kinds of information, for example, the first information includes position information and lane information of the vehicle. Or the first information may include three kinds of information, for example, the first information includes position information of the vehicle, lane information, and navigation information. Or the first information may include four kinds of information, for example, the first information includes vehicle position information, lane information, navigation information and obstacle information. It should be noted that the types of the types of first information listed above are only for illustrative purposes and do not represent limitations. For example, the first information may include location information and obstacle information, or the first information may also include information other than those mentioned above. The location information of the arriving vehicle, lane information, navigation information, and other information other than obstacle information.

Environment information around the vehicle may be determined through the acquired first information, and the environment information around the vehicle may be used to determine whether the vehicle has ever entered the same or similar scene. In some scenarios with simple road conditions, it can be considered that as long as the position information of the vehicles is consistent, the same scenario can be determined. In some scenes with complex road conditions, it may be necessary to determine the same scene only when the vehicle's position information, lane information, navigation information and obstacle information are consistent.

1. The vehicle is in automatic driving mode

402. When the vehicle is in the automatic driving mode, the first information is the input data of the first model, the output data of the first model is used to plan a driving route for the vehicle, and the first model takes the driving trajectory of the vehicle in the manual driving mode as the training target , a model obtained by training the second information obtained in the manual driving mode, the type of information included in the second information is consistent with the type of information included in the first information, and the similarity between the first information and the second information satisfies a preset condition.

The first information mentioned in the above step 401 may include one or more of vehicle position information, lane information, navigation information, and obstacle information. The type of information included in the second information is consistent with the type of information included in the first information, and the second information may include one or more of vehicle location information, lane information, navigation information, and obstacle information. Specifically, for example, if the first information includes the position information of the vehicle, the second information includes the position information of the vehicle; the first information includes the position information and lane information of the vehicle, and the second information includes the position information and lane information of the vehicle; the first information includes the position information and lane information of the vehicle; The information includes the position information, lane information and navigation information of the vehicle, the second information includes the position information, lane information and navigation information of the vehicle; the first information includes the position information, lane information, navigation information and obstacle information of the vehicle, then the first information includes the vehicle position information, lane information, navigation information and obstacle information. The second information includes vehicle position information, lane information, navigation information and obstacle information.

The similarity between the first information and the second information satisfies the preset condition in order to determine whether the vehicle enters the same scene. For example, the surrounding environment information obtained by the vehicle passing through the first road section is the first information, and the surrounding environment information obtained by the vehicle passing through the second road section is the second information. If the similarity between the first information and the second information meets the preset conditions, it is considered that the first The first road segment and the second road segment are the same or similar scenes, for example, the first road segment and the second road segment may be the same road segment.

Regarding how to determine the similarity of the two pieces of information, any means that can be used in the related art can be used in the embodiments of the present application. For example, the first information includes the location information of the vehicle as an example for description, where the location information may include the latitude and longitude information of the location where the vehicle is located. For example, the first information includes first longitude and latitude information, and the second information includes second longitude and latitude information. If the difference between the two is less than a preset threshold, it can be considered that the similarity between the first information and the second information meets the preset condition. If If the difference between the two is greater than the preset threshold, it can be considered that the similarity between the first information and the second information does not meet the preset condition. In other words, whether the similarity of the two pieces of information satisfies the preset condition can be judged by means of whether the deviation of the two pieces of information is smaller than the preset threshold. For another example, the first information includes lane information. Specifically, the first information includes first lane information, and the second information includes second lane information. A road section includes a total of 2 left-turn lanes, of which the first information is the right lane in the 2 left-turn lanes, and the second information is the left-turn lane in the left-turn lane. If the preset conditions are the first information and the second The second information is exactly the same, and the similarity between the first information and the second information satisfies the preset condition, then it is considered that the similarity between the first information and the second information does not meet the preset condition. If the preset condition is that the vehicle is in the left-turn lane, the first If the similarity between the first information and the second information satisfies the preset condition, it can be considered that the similarity between the first information and the second information meets the preset condition. For another example, the first information includes navigation information. Specifically, the first information includes first navigation information, and the second information includes second navigation information. Wherein, if the first information is that the next driving direction of the vehicle is going straight, and the second information is that the next driving direction of the vehicle is turning left, it can be considered that the similarity between the first information and the second information does not meet the preset condition, and the first If the information is that the next traveling direction of the vehicle is straight, and the second information is that the next traveling direction of the vehicle is straight, it can be considered that the similarity between the first information and the second information satisfies the preset condition. For another example, the first information includes obstacle information, where the obstacle information may include static obstacle information and dynamic obstacle information, and the first information may include only static obstacle information, or only dynamic obstacle information, or may include both. Static obstacle information also includes dynamic obstacle information. The information about the obstacle can include the relative position relationship between the obstacle and the vehicle, and about the dynamic obstacle, the obstacle information can also include the speed of the dynamic obstacle. When the dynamic obstacle is other vehicles, the dynamic obstacle information can also Including the direction of the head of other vehicles. It is assumed that the first information includes first obstacle information, and the second information includes second obstacle information. The first obstacle information includes first relative position information between the obstacle and the vehicle, the first speed of the obstacle, and the first vehicle head direction of the obstacle, and the second obstacle information includes the distance between the obstacle and the vehicle. The second relative position information, the second speed of the obstacle, and the direction of the second vehicle head of the obstacle. The preset condition may be that the deviation between the first relative position information and the second relative position information is less than the first threshold, the deviation between the first speed and the second speed is less than the second threshold, and the first head direction and the third If the deviation between the vehicle head directions is less than the third threshold, it is considered that the similarity between the first information and the second information satisfies the preset condition. Alternatively, the preset condition may be that the deviation between the first relative position information and the second relative position information is less than the first threshold, and the deviation between the first speed and the second speed is less than the second threshold, then it is considered that the first information and The similarity of the second information satisfies a preset condition. It should be noted that, in the actual application process, the preset conditions can be set according to the requirements according to the scene.

In a possible implementation, the similarity of the same type of information in the first information and the second information is determined, and a linear weighted sum of the similarity of the same type of information is used to determine the similarity of the first information and the second information. For example, for example, the first information includes the first position information of the vehicle, the first lane information, the first navigation information and the first obstacle information, and the second information includes the second position information of the vehicle, the second lane information, and the second navigation information. information and second obstacle information. Assume that the weight of the similarity of the location information is 3/8, the weight of the similarity of the lane information is 3/8, the weight of the similarity of the navigation information is 1/8, and the weight of the similarity of the obstacle information is 1. /8. Assuming that the similarity of the two pieces of information satisfies the preset condition, the similarity is set to 1, and the similarity of the two pieces of information does not meet the preset condition, then the similarity is set to 0. If the similarity between the first position information and the second position information is similar The degree of similarity satisfies the first preset condition, the similarity between the first lane information and the second lane information does not meet the second preset condition, the similarity between the first navigation information and the second navigation information does not meet the third preset condition, the first The similarity between the obstacle information and the second obstacle information satisfies the fourth preset condition, then (3/8*1+3/8*0+1/8*0+1/8*1) is the same type of information The linear weighted sum of the similarity, that is, the linear weighted sum is 1/2. If the preset condition sets the similarity to be not less than 1/2, it can be considered that the similarity between the first information and the second information satisfies the preset condition. Assuming that the similarity is set to be greater than 1/2, it can be considered that the similarity between the first information and the second information does not meet the preset condition. It should be noted that the values of the weights of the similarity degrees of different information listed above, and the values that satisfy the similarity condition are set to 1, and those that do not meet the similarity condition are set to 0, are all examples, and do not mean that the value of the application is correct. In practical application scenarios, the weights of different types of information can be selected according to the requirements of the scenario, and the linear weighted sum of the similarity of the same type of information can be determined.

In a possible implementation manner, the similarity between the first information and the second information may also be determined in other manners. For example, the first information and the second information include a total of 4 kinds of information, and it can be set that 3 kinds of information meet the preset conditions, and it can be considered that the similarity between the first information and the second information satisfies the preset conditions. It should be noted that the three types of information here satisfy the preset conditions, which means that the three types of information respectively satisfy their respective preset conditions, which will not be repeated in this article. In addition, how to determine whether each kind of information satisfies the preset condition has been described above.

If the similarity between the first information and the second information satisfies the preset condition, it can be considered that the vehicle has entered two similar driving scenarios. When the vehicle is in the automatic driving mode, the first information is the input data of the first model, and the first model The output data is used to plan a driving route for the vehicle. The first model is a model obtained by training the second information obtained in the manual driving mode with the driving trajectory of the vehicle in the manual driving mode as the training target. In order to better understand the solution provided by this application, the following describes the automatic driving mode, the manual driving mode, and the scenarios corresponding to the automatic driving mode after entering the same scene with reference to FIG. 5a to FIG. 5c.

As shown in FIG. 5a , it is a schematic flowchart of the process when the vehicle is in the automatic driving mode. The sensor system sends the perceived surrounding environment information (such as the first information) to the behavior planner. The behavior planner issues high-level decisions based on the surrounding environment obtained by the sensor system, and the motion planner plans expectations based on the high-level decisions issued by the behavior planner. Trajectory and speed, the motion controller is responsible for operating the accelerator and braking steering wheel, allowing the autonomous vehicle to follow the target trajectory and reach the target speed. In the automatic driving mode, the driver does not intervene, as shown in Figure 5a with a dashed line to indicate the process of the driver's manipulation.

As shown in Fig. 5b, when the vehicle enters a similar scene again, it is embodied in that the similarity between the first information and the second information satisfies a preset condition, and the second information is the training data of the first model. If the vehicle is in the automatic driving mode, a driving route can be planned for the vehicle according to the output data of the first model according to the first information as the input data of the first model.

2. The vehicle is in manual driving mode

403. When the vehicle is in the manual driving mode, the first information is used to train the first model.

As shown in Figure 5c, when the vehicle may be in danger or does not drive as expected by the driver (it should be noted that the description of the driver in this solution refers to a human driver), the driver can operate the steering wheel or step on the The way the brake pedal intervenes in the driving of the vehicle. When the driver takes over, the sensor system is kept in normal operation and the surrounding environment information is output. When the driver takes over, the behavior planner, motion planner, and motion controller do not intervene in the control of the vehicle. The first model is trained by using the data obtained by the sensor system (ie the surrounding environment information) in the manual driving mode as training data. Among them, the data obtained by the sensor in the manual driving mode can be understood as: the data obtained by the sensor during the period from when the driver drives the vehicle to when the sensor switches back to the automatic driving mode, or the driver drives the vehicle to a preset distance. Or the data acquired by the sensor during a preset duration. The training target of the first model is the driving trajectory of the vehicle in the manual driving mode, that is, the training target is to make the output data of the first model closer to the driving trajectory of the driver. In other words, the goal of training is to make the trajectory distribution output by the trained first model match the trajectory distribution of the vehicle in the manual driving mode, or the deviation is within a preset range. It should be noted that any imitation learning algorithm in the related art can be used in the embodiments of the present application.

It can be seen from the embodiment corresponding to FIG. 4 that the solution provided by the present application acquires the environmental information around the vehicle, such as the first information and the second information, in real time. When the vehicle is in the manual driving mode, the model is trained with the environment information around the vehicle as the training data, so that the trajectory output by the model can be close to the driving trajectory of the vehicle in the manual driving mode. When the vehicle is in the automatic driving mode, if it is determined that the similarity between the environmental information of the vehicle and the surrounding environmental information of the vehicle used for model training meets the preset conditions, the environmental information around the vehicle is used as the input data of the model, and the output of the model is The data plans the driving route for the vehicle. The solution provided by this application can reduce the frequency of manual takeover. For example, in a typical scenario, as shown in Figure 2a to Figure 2c, when passing through this road section, it is not necessary to take over by the driver every time, and the vehicle can take over according to the The data output by the model plans the driving route for the vehicle and avoids continuous manhole covers. In addition, the solution provided in this application can greatly reduce the data required for training. In the existing technology, without considering a large number of long-tail scenarios, aiming at solving the planning problems in the most common scenarios, focusing on optimizing the more common scenarios in the automatic driving process, the automatic driving data in these scenarios are also easier to collect and have more commonality , such as lane changing, lane keeping, high-speed cruise and other scenarios. Due to the need to consider more common scenarios and make its solutions more generalized, the existing technologies often require a large amount of driving data, which cannot be met by general manufacturers or research institutes. The solution provided in this application is mainly optimized for a specific scenario, regardless of the generalization of the model, so it is only necessary to collect data for the scenario, and the amount of data required is much smaller than that in the prior art.

In addition, for a large number of long-tail scenarios, the existing technology usually requires researchers to analyze the scenarios one by one. The analysis process is complex, the solution often requires special processing, and often affects the logic of other existing scenarios, which cannot be automated. incremental optimization. The present application can also solve this problem, which will be specifically described below.

As shown in Figure 5d, when the vehicle is in the automatic driving mode, the similarity of the scene (surrounding environment information) where the vehicle is located can be evaluated by evaluating the similarity between the first information and the M groups of information. The similarity satisfies the preset condition, that is, it is considered that the similarity between the current scene of the vehicle and the scene corresponding to the existing available model exceeds the threshold, and the available model with the highest similarity can be selected from the M models, and the currently obtained periodic environment information (such as The first information) is used as the input of the available model with the highest similarity (such as the first model), and the output data of the available model with the highest similarity can plan a driving route for the vehicle. This scenario is described in detail below.

Please refer to FIG. 6. FIG. 6 is a schematic flowchart of a method for planning a driving route of a vehicle provided by an embodiment of the present application. The method for planning a driving route for a vehicle provided by an embodiment of the present application may include:

601. Obtain first information.

Step 601 can be understood with reference to step 401 in the embodiment corresponding to FIG. 4 , and details are not repeated here.

602. Evaluate the similarity between the first information and the M groups of information.

Each group of information in the M groups of information is the information obtained when the vehicle is in the manual driving mode. The similarity of any two groups of information in the M groups of information does not meet the preset conditions. for training a model. The similarity of any two groups of information in the M groups of information does not satisfy the preset condition, so that each information in the M groups of information can correspond to a different scenario, and different scenarios correspond to different models. For example, if M is 2, the M groups of information are M1 group information and M2 group information respectively. The M1 group information and the M2 group information are both information obtained in the manual driving mode. For example, the M1 group information is the information obtained in the scenario of Figure 1c, and the M2 group information is the information obtained in the scenario of Figure 2b. For example, the type of information included in the M group of information is the location information of the vehicle. In the scenarios shown in Figure 1c and Figure 2b, the geographical position of the vehicle has a large deviation, and it can be considered that the similarity between the M1 group information and the M2 group information does not meet the preset conditions. . Of course, the above-mentioned M groups of information including two groups of information are only for illustration, and in an actual scenario, the M groups of information may include more than two groups of information. For example, if the type information included in the M groups of information is the position information of the vehicle, each group of information in the M groups of information may correspond to a different road segment respectively. How to evaluate the similarity of two pieces of information has been introduced above. For details, please refer to step 402 in the embodiment corresponding to FIG. 4 for an understanding of how to determine the similarity of two pieces of information, which will not be repeated here.

1. The vehicle is in automatic driving mode

603. When the similarity between the first information and the second information in the M groups of information satisfies a preset condition, and the similarity between the second information and the first information in the M groups of information is the largest, the first information is the input data of the first model.

For example, it is assumed that the M groups of information are respectively the M1 group information and the M2 group information, the M1 group information is the information obtained in the scenario of Fig. 1c, and the M2 group information is the information obtained in the scenario of Fig. 2b. It is assumed that the model corresponding to the M1 group information is the A model, and the model corresponding to the M2 group information is the B model. Taking model A as an example to illustrate, if the vehicle is in the manual driving mode in the scene shown in Figure 1c, the driving trajectory of the vehicle is obtained, and the surrounding environment information in the scene of Figure 1c is obtained as training data to train the A model. , when the trajectory of the output of the A model and the distribution of the vehicle trajectory in the manual driving mode in the scene of Figure 1c are within the preset range, the A model is considered to have completed the training, and the output data of the A model can be used for the vehicle to plan the driving. route. If the similarity between the first information and the M1 group information satisfies the preset condition, and the similarity between the first information and the M1 group information is greater than the similarity between the first information and the M2 group information, the first information can be used as the input of the A model At this time, the output data of the A model can be used to plan the driving route for the vehicle. At this time, the planned driving route is similar to the driving trajectory of the vehicle in the manual driving mode, and will not collide with the obstacles at the exit of the parking space. It should be noted that in this application, the trajectory output by the model is sometimes referred to as the data output by the model, and when the difference between the two is not emphasized, the two have the same meaning. Taking the B model as an example to illustrate, if the vehicle is in the manual driving mode in the scene shown in Figure 2b, the driving trajectory of the vehicle is obtained, and the surrounding environment information in the scene shown in Figure 2b is obtained as training data to train the B model. , when the trajectory of the output of the B model and the distribution of the vehicle trajectory in the manual driving mode in the scene of Figure 2b are within the preset range, the B model is considered to have completed the training, and the output data of the B model can be used for the vehicle to plan the driving. route. If the similarity between the first information and the M2 group information satisfies the preset condition, and the similarity between the first information and the M1 group information is smaller than the similarity between the first information and the M2 group information, the first information can be used as the input of the B model At this time, the output data of the B model can be used to plan the driving route for the vehicle. At this time, the planned driving route is similar to the driving trajectory of the vehicle in the manual driving mode, which can avoid the continuous manhole cover on the road.

The similarity of the scene (surrounding environment information) where the vehicle is located can be evaluated by evaluating the similarity between the first information and the M groups of information. If the similarity between the first information and the M groups of information meets the preset conditions, it is considered that the current scene of the vehicle is similar. If the similarity with the scene corresponding to the existing available model exceeds the threshold, the available model with the highest similarity can be selected, and the currently obtained periodic environment information (such as the first information) is used as the available model with the highest similarity (such as the first model) The output data of the available model with the highest similarity can plan the driving route for the vehicle. Wherein, it can be considered that the model is a usable model when the distribution of vehicle trajectories in the manual driving mode is within a preset range. The available models are described further below.

2. The vehicle is in manual driving mode

604. When the similarity between the first information and the second information in the M groups of information satisfies a preset condition, and when the similarity between the second information and the first information in the M groups of information is the largest, the first information is used to train the first model to Get the updated first model.

How to determine whether the similarity between the first information and each of the M groups of information satisfies the similarity condition can be understood with reference to the above description of how to determine the similarity of the two pieces of information, and details are not repeated here.

If the similarity between the first information and a certain group of information in the M groups of information satisfies the preset condition, for example, the similarity between the first information and the second information in the M group of information satisfies the preset condition, it can be considered that the vehicle is currently located in the The scene is the same or similar to a scene in the historical manual driving mode.

If the similarity between the current scene and the scene corresponding to the existing available model exceeds the threshold, the available model with the highest similarity is selected, such as the first model with the highest similarity, and the first model is updated and trained by using the first information. The available models for this scene are updated, ie the first model is updated.

605. When the similarity between the first information and each of the M groups of information does not meet the preset condition, the first information is used to train the first model for the first time.

When the similarity between the first information and each of the M groups of information does not meet the preset condition, it can be considered that there is no scene identical or similar to the scene where the vehicle is currently located in the historical manual driving mode. There is no output data from the model available at this time to plan a driving route for the vehicle. Then, using the first information, a temporary model is constructed for training, and a temporary model for the scene is formed, that is, the first model is trained for the first time by using the first information. At this time, the first model may not meet the training target, that is, the distribution of the output trajectory of the first model and the driving trajectory of the vehicle in the manual driving mode is not within the preset range. Then the first model trained for the first time is a temporary model, a non-available model. When in the manual driving mode, the information of the similarity with the first information is obtained for many times, and the first model is iteratively trained until the first model. If the model meets the training target, the first model is an available model, and the first model that is an available model at this time can plan a driving route for the vehicle. The following is an example, assuming that the similarity between the first information and each of the M groups of information does not meet the preset condition, the first training is performed on the first model by using the first information. In the subsequent manual driving mode, if the acquired surrounding environment information, such as the acquired third information, the similarity between the third information and the first information satisfies the preset condition, and all the currently stored surrounding information In the environmental information, the similarity between the third information and the first information is the largest, then the first model is trained for the second time through the third information, and so on, and the first model is continuously iteratively trained until the output of the first model is If the distribution of the trajectory and the driving trajectory of the vehicle in the manual driving mode is within a preset range, the first model is considered to be a usable model. In a possible implementation, before the first model is an available model, if the vehicle is in the automatic driving mode, even if the acquired similarity between the fourth information and the first information satisfies a preset condition, the fourth information is not used as the first information. The input of a model does not use the output of the first model at this time as the planned driving route of the vehicle. Because the distribution of the output trajectory of the first model and the driving trajectory of the vehicle in the manual driving mode is not within the preset range at this time, if the output of the first model is used to plan a driving route for the vehicle, danger may occur.

In order to better understand the technical solution provided by the present application, the solution provided by the present application will be introduced below in combination with a specific process, and the temporary model and the available model will be introduced.

As shown in FIG. 7 , another schematic flowchart of a method for planning a driving route of a vehicle provided by an embodiment of the present application. As shown in FIG. 7 , the vehicle acquires surrounding environment information (for example, the vehicle acquires the first message). To determine whether the vehicle is taken over by the driver (driver), for example, it can be determined whether the driver intervenes in the driving of the vehicle by operating the steering wheel or depressing the brake pedal. If it is determined that the vehicle is not taken over by the driver, the vehicle is in an automatic driving mode, and the similarity of the scenarios is evaluated, for example, according to step 602 in the embodiment corresponding to FIG. 6 . It is judged whether there is an available model of a similar scenario, if there is an available model of a similar scenario, it can be understood by referring to 603 in the corresponding embodiment of FIG. 6 , and use the output data corresponding to the scenario to plan a driving route for the vehicle. If there is no available model of a similar scenario, the vehicle is controlled according to the normal automatic driving mode, that is, the vehicle is controlled according to the established automatic driving strategy. The available models that do not have similar scenarios can understand the models that have similar scenarios, but the models in this scenario have not yet reached the training goal, or they can be understood as models that do not have similar scenarios. If it is determined that the vehicle is taken over by the driver, the vehicle is in the manual driving mode, and the vehicle trajectory in the manual driving mode, or the driving trajectory of the vehicle, is obtained, and the similarity of the scenes is evaluated. If there is a model corresponding to a similar scene, and the model is already an available model, the model is trained through the currently obtained surrounding environment information to obtain an updated model. If there is no model corresponding to a similar scene, the model is trained based on the currently obtained surrounding environment information. The model at this time is referred to as a temporary model in this application, and is used to distinguish it from the available models. The temporary model indicates that the model has not reached the training target, that is, the distribution of the trajectory output by the model and the vehicle's trajectory in the manual driving mode is not within the preset range, and the vehicle's trajectory in the manual driving mode cannot be simulated. Whether a model is a usable model or a temporary model can be determined by evaluating whether the model is safe and stable. Regarding whether the evaluation model is safe and stable, in addition to the above-mentioned way of judging whether the output data of the model and the distribution of the vehicle's driving trajectory in the manual driving mode are within the preset range, there are other ways. For example, it is possible to add disturbances based on existing scenes, and build more scene verifications to ensure that there is no collision within the drivable range, and then confirm that the model meets the needs of safety and stability.

The solutions provided by the embodiments of the present application are introduced below with reference to several typical application scenarios. As shown in FIG. 8a , it is a schematic diagram of a scenario of a method for planning a driving route of a vehicle provided by the present application. As shown in Figure 8a, the geographic location of the vehicle, static obstacle information (such as the street lights on both sides of the road shown in Figure 8a), and dynamic obstacle information (such as other vehicles on the road, not shown in the figure) can be obtained. When the vehicle is in the manual driving mode, the driving track of the vehicle is obtained, and a prompt message can be sent to remind the user that the vehicle is learning the current driving mode. The prompting manner may include text prompting or voice prompting, which is not limited in this embodiment of the present application. It should be noted that the information about the surrounding environment may be embodied in different ways. For example, in a possible implementation, the surrounding environment information may only include the geographic location. When the vehicle is in the manual driving mode, a prompt message is sent, and the prompt message uses for instructing the vehicle to train the first model according to the current position information of the vehicle. As shown in FIG. 8b , it is a schematic diagram of a scenario of a method for planning a driving route of a vehicle provided by the present application. As shown in Figure 8b, the geographic location of the vehicle, static obstacle information (such as the street lights on both sides of the road shown in Figure 8b), and dynamic obstacle information (such as other vehicles on the road, not shown in the figure) can be obtained. When the vehicle is in the automatic driving mode, if it is determined that the similarity between the surrounding environment information of the vehicle and the environmental information around the vehicle in the manual driving mode meets the preset condition, for example, the surrounding environment information of the vehicle shown in FIG. 8b and the surrounding environment information shown in FIG. 8a If the similarity of the surrounding environmental information of the vehicle satisfies the preset condition, a prompt message is sent, prompting to plan a driving route for the vehicle according to the data output from the model. For example, it can be prompted that the scene matching is successful, and the driving route is being planned for the vehicle according to the model output data.

It should be noted that, the solution provided in this application can be jointly completed by multiple devices. For example, as shown in FIG. 9 , it is a schematic diagram of a scenario of another method for planning a driving route of a vehicle provided by the present application. In this scenario, vehicle A, vehicle B and vehicle C all enter the manual driving mode on the same road section, vehicle A, vehicle B and vehicle C can use the surrounding environment information (such as the first information) obtained by each and the The driving trajectory is sent to the cloud-side device, and the cloud-side device trains the first model according to the obtained first information sent by multiple vehicles, and sends the trained first model to vehicle A, vehicle B, and vehicle C. In a possible implementation, the cloud-side device may also send the first model to vehicles that do not participate in sending the first information, for example, to other vehicles other than vehicle A, vehicle B, and vehicle C. When other vehicles pass through the road section, they can plan a driving route for the vehicles according to the output data of the first model, and avoid continuous manhole covers.

In a possible implementation manner, the vehicle is used to obtain first information, and the first information may include one or more of vehicle location information, lane information, navigation information, and obstacle information, and the lane information is used to determine the vehicle The relative position of the lane line, the navigation information is used to predict the driving direction of the vehicle, and the obstacle information is used to determine the relative position of the vehicle and the obstacle. The vehicle is also used to send the first information and the driving mode of the vehicle to the cloud-side device. The cloud-side device is used to determine that when the vehicle is in the automatic driving mode, plan the driving route for the vehicle according to the output data of the first model. The first information is the input data of the first model, and the first model is the driving route of the vehicle in the manual driving mode. The trajectory is the training target, and the model is obtained by training the second information obtained in the manual driving mode. The types of information that the second information can include are consistent with the types of information that the first information can include, and the first information and the second information are similar. meet the preset conditions. The cloud-side device is further configured to train the first model according to the first information when it is determined that the vehicle is in the manual driving mode.

In a possible implementation manner, the cloud-side device is further configured to: evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is information obtained when the vehicle is in the manual driving mode, and the M The similarity of any two groups of information in the group information does not meet the preset condition, and each group of information in the M groups of information is used to train a model respectively. When the similarity between the first information and the second information in the M groups of information satisfies a preset condition, and the similarity between the second information and the first information in the M groups of information is the largest, the first information is the input data of the first model.

In a possible implementation manner, the cloud-side device is further configured to: evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is information obtained when the vehicle is in the manual driving mode, and the M The similarity of any two groups of information in the group information does not meet the preset conditions, each group of information in the M groups of information is used to train a model, the second information in the M groups of information is used to train the first model, and M is positive integer. When the similarity between the first information and the second information in the M groups of information satisfies a preset condition, and the similarity between the second information and the first information in the M groups of information is the largest, the first information is used to train the first model to obtain an update After the first model.

In a possible implementation, the cloud-side device is further configured to evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is information obtained when the vehicle is in the manual driving mode, and the M groups of information are The similarity of any two groups of information in the information does not meet the preset condition, and each group of information in the M groups of information is used to train a model, and M is a positive integer. When the similarity between the first information and each of the M groups of information does not meet the preset condition, the first information is used to train the first model for the first time.

In a possible implementation, the cloud-side device is further configured to send a prompt message when it is determined that the current position information of the vehicle is consistent with the position information of the vehicle obtained in the manual driving mode, and the prompt message is used to instruct the vehicle according to the first model. The output data is the planned driving route of the vehicle, and the first model is a model obtained by training the position information of the vehicle obtained in the manual driving mode.

In a possible implementation manner, the cloud-side device is further configured to send a prompt message, where the prompt message is used to instruct to train the first model according to the current position information of the vehicle.

In a possible implementation manner, the cloud-side device is further configured to determine the similarity of the same type of information in the first information and the second information, and the linear weighted sum of the similarity of the same type of information is used to determine the first information and the second information similarity of information.

In one possible implementation, the first model may include a convolutional neural network CNN or a recurrent neural network RNN.

On the basis of the embodiments corresponding to FIGS. 4 to 9 , in order to better implement the above solutions of the embodiments of the present application, related equipment for implementing the above solutions is also provided below. Referring specifically to FIG. 10 , FIG. 10 is a schematic structural diagram of an apparatus for planning a driving route of a vehicle provided by an embodiment of the present application. The apparatus for planning the driving route of the vehicle may include an acquisition module 1001 , a regulation module 1002 , a training module 1003 , an evaluation module 1004 , a transmission module 1006 and a processing module 1005 .

In a possible implementation, it may include: an acquisition module 1001, configured to acquire first information, where the first information may include one or more of vehicle location information, lane information, navigation information, obstacle information, lane information The information is used to determine the relative position of the vehicle and the lane line, the navigation information is used to predict the driving direction of the vehicle, and the obstacle information is used to determine the relative position of the vehicle and the obstacle. The regulation and control module 1002 is used for planning the driving route for the vehicle according to the output data of the first model when the vehicle is in the automatic driving mode, and the first information obtained by the obtaining module 1001 is the input data of the first model, and the first model is based on manual driving. The driving trajectory of the vehicle in the mode is the training target, and the model is obtained by training the second information obtained in the manual driving mode. The types of information that the second information can include are consistent with the types of information that the first information can include. The first information and the The similarity of the second information satisfies a preset condition. The training module 1003 is used for training the first model according to the first information obtained by the obtaining module 1001 when the vehicle is in the manual driving mode.

In a possible implementation, it may further include: an evaluation module 1004, configured to evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is information obtained when the vehicle is in a manual driving mode , the similarity of any two groups of information in the M groups of information does not meet the preset condition, and each group of information in the M groups of information is used to train a model respectively. When the similarity between the first information and the second information in the M groups of information satisfies a preset condition, and the similarity between the second information and the first information in the M groups of information is the largest, the first information is the input data of the first model.

In a possible implementation, it may further include: an evaluation module 1004, configured to evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is information obtained when the vehicle is in a manual driving mode , the similarity of any two groups of information in the M groups of information does not meet the preset conditions, each group of information in the M groups of information is used to train a model, and the second information in the M groups of information is used to train the first model, M is a positive integer. When the similarity between the first information and the second information in the M groups of information satisfies a preset condition, and the similarity between the second information and the first information in the M groups of information is the largest, the first information is used to train the first model to obtain an update After the first model.

In a possible implementation, it may further include: an evaluation module 1004, configured to evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is information obtained when the vehicle is in a manual driving mode , the similarity of any two groups of information in the M groups of information does not meet the preset condition, and each group of information in the M groups of information is respectively used to train a model, and M is a positive integer. When the similarity between the first information and each of the M groups of information does not meet the preset condition, the first information is used to train the first model for the first time.

In a possible implementation, it may further include: a sending module 1006, configured to send a prompt message when the evaluation module 1004 determines that the current position information of the vehicle is consistent with the position information of the vehicle obtained in the manual driving mode, and the prompt message is used to indicate The vehicle plans a driving route for the vehicle according to the output data of the first model, and the first model is a model obtained by training the position information of the vehicle obtained in the manual driving mode.

In a possible implementation, it may further include: a sending module 1006, configured to send a prompt message, where the prompt message is used to instruct to train the first model according to the current position information of the vehicle.

In a possible implementation, the processing module 1005 is configured to determine the similarity of the same type of information in the first information and the second information, and the linear weighted sum of the similarity of the same type of information is used to determine the first information and the second information similarity.

It should be noted that the information exchange and execution process among the modules in the device for planning the driving route of the vehicle are based on the same concept as the method embodiments corresponding to FIG. 4 to FIG. 9 in this application. For details, please refer to this application. The descriptions in the foregoing method embodiments are not repeated here.

An embodiment of the present application also provides an automatic driving vehicle. With reference to the above description of FIG. 3 , please refer to FIG. 11 . FIG. 11 is a schematic structural diagram of the automatic driving vehicle provided by the embodiment of the application, wherein the automatic driving vehicle 100 The apparatus for planning a vehicle driving route described in the embodiment corresponding to FIG. 10 may be deployed on the above-mentioned device, so as to realize the functions of the automatic driving vehicle in the corresponding embodiment of FIG. 4 to FIG. 9 . Since in some embodiments, the autonomous driving vehicle 100 may further include a communication function, the autonomous driving vehicle 100 may further include a receiver 1201 and a transmitter 1202 in addition to the components shown in FIG. 3 , wherein the processor 113 may An application processor 1131 and a communication processor 1132 are included. In some embodiments of the present application, the receiver 1201, the transmitter 1202, the processor 113, and the memory 114 may be connected by a bus or otherwise.

The processor 113 controls the operation of the autonomous vehicle. In a specific application, various components of the autonomous vehicle 100 are coupled together through a bus system, where the bus system may include a power bus, a control bus, a status signal bus, and the like in addition to a data bus. However, for the sake of clarity, the various buses are referred to as bus systems in the figures.

The receiver 1201 can be used to receive input numerical or character information, and generate signal input related to the relevant settings and function control of the autonomous vehicle. The transmitter 1202 can be used to output digital or character information through the first interface; the transmitter 1202 can also be used to send instructions to the disk group through the first interface to modify the data in the disk group; the transmitter 1202 can also include a display device such as a display screen .

In this embodiment of the present application, the application processor 1131 is configured to execute the method for planning a vehicle driving route performed by the autonomous driving vehicle in the embodiment corresponding to FIG. 4 . Specifically, the application processor 1131 is configured to perform the following steps:

When the vehicle is in the automatic driving mode, the driving route is planned for the vehicle according to the output data of the first model. The first information obtained by the sensor system is the input data of the first model, and the first model is trained on the driving trajectory of the vehicle in the manual driving mode. The target is a model obtained by training the second information obtained in the manual driving mode. The types of information that the second information can include are consistent with the types of information that the first information can include, and the similarity between the first information and the second information satisfies the predetermined level. Set conditions. When the vehicle is in the manual driving mode, the first model is trained according to the first information obtained by the sensor system.

In a possible implementation manner, the similarity between the first information and the M groups of information is evaluated, each group of information in the M groups of information is information obtained when the vehicle is in a manual driving mode, and any two groups of information in the M groups of information The similarity does not meet the preset conditions, and each group of information in the M groups of information is used to train a model. When the similarity between the first information and the second information in the M groups of information satisfies a preset condition, and the similarity between the second information and the first information in the M groups of information is the largest, the first information is the input data of the first model.

In a possible implementation manner, the similarity between the first information and the M groups of information is evaluated, each group of information in the M groups of information is information obtained when the vehicle is in a manual driving mode, and any two groups of information in the M groups of information The similarity does not meet the preset condition, each of the M groups of information is used to train a model, the second information of the M groups of information is used to train the first model, and M is a positive integer. When the similarity between the first information and the second information in the M groups of information satisfies a preset condition, and the similarity between the second information and the first information in the M groups of information is the largest, the first information is used to train the first model to obtain an update After the first model.

In a possible implementation manner, the similarity between the first information and the M groups of information is evaluated, each group of information in the M groups of information is information obtained when the vehicle is in a manual driving mode, and any two groups of information in the M groups of information The similarity does not meet the preset conditions, and each group of information in the M groups of information is used to train a model, and M is a positive integer. When the similarity between the first information and each of the M groups of information does not meet the preset condition, the first information is used to train the first model for the first time.

In a possible implementation manner, the transmitter is configured to send the first message to the cloud-side device.

In a possible implementation manner, the receiver is configured to receive the first model sent by the cloud-side device.

It should be noted that, for the specific implementation of the method for planning a vehicle driving route executed by the application processor 1131 and the beneficial effects brought about, reference may be made to the descriptions in the respective method embodiments corresponding to FIG. 4 to FIG. 9 , which are not repeated here. Repeat them one by one.

Embodiments of the present application also provide a computer-readable storage medium, where a program for planning a vehicle's driving route is stored in the computer-readable storage medium, and when the computer is running on a computer, the computer is made to execute the operations shown in FIGS. 4 to 9 above. The steps performed by the autonomous vehicle (or the apparatus for planning the vehicle's travel route) in the method described in the illustrated embodiment.

Embodiments of the present application also provide a computer program product that, when driving on a computer, causes the computer to execute the steps performed by the autonomous vehicle in the methods described in the embodiments shown in FIGS. 4 to 9 .

An embodiment of the present application further provides a circuit system, the circuit system includes a processing circuit, and the processing circuit is configured to execute the steps performed by the autonomous driving vehicle in the method described in the embodiments shown in the foregoing FIG. 4 to FIG. 9 .

The device for planning a vehicle driving route or the autonomous driving vehicle provided in the embodiment of the present application may specifically be a chip, and the chip includes: a processing unit and a communication unit, the processing unit may be, for example, a processor, and the communication unit may be, for example, an input/output interface, pin or circuit, etc. The processing unit can execute the computer-executable instructions stored in the storage unit, so that the chip in the server executes the method for planning a driving route of a vehicle described in the embodiments shown in FIG. 4 to FIG. 9 . Optionally, the storage unit is a storage unit in the chip, such as a register, a cache, etc., and the storage unit may also be a storage unit located outside the chip in the wireless access device, such as only Read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (RAM), etc.

Specifically, please refer to FIG. 12. FIG. 12 is a schematic structural diagram of a chip provided by an embodiment of the application. The chip may be represented as a neural network processor NPU 130, and the NPU 130 is mounted as a co-processor to the main CPU (Host CPU), tasks are allocated by the Host CPU. The core part of the NPU is the arithmetic circuit 1303, which is controlled by the controller 1304 to extract the matrix data in the memory and perform multiplication operations.

In some implementations, the arithmetic circuit 1303 includes multiple processing units (Process Engine, PE). In some implementations, the arithmetic circuit 1303 is a two-dimensional systolic array. The arithmetic circuit 1303 may also be a one-dimensional systolic array or other electronic circuitry capable of performing mathematical operations such as multiplication and addition. In some implementations, arithmetic circuit 1303 is a general-purpose matrix processor.

For example, suppose there is an input matrix A, a weight matrix B, and an output matrix C. The operation circuit fetches the data corresponding to the matrix B from the weight memory 1302 and buffers it on each PE in the operation circuit. The arithmetic circuit fetches the data of matrix A and matrix B from the input memory 1301 to perform matrix operation, and stores the partial result or final result of the matrix in the accumulator 1308 .

Unified memory 1306 is used to store input data and output data. The weight data is directly passed through the storage unit access controller (direct memory access controller, DMAC) 1305, and the DMAC is transferred to the weight memory 1302. Input data is also moved to unified memory 1306 via the DMAC.

A bus interface unit (BIU) 1310 is used for the interaction between the AXI bus and the DMAC and an instruction fetch buffer (instruction fetch buffer, IFB) 1309.

The BIU 1310 is used for the instruction fetch memory 1309 to obtain instructions from the external memory, and is also used for the storage unit access controller 1305 to obtain the original data of the input matrix A or the weight matrix B from the external memory.

The DMAC is mainly used to transfer the input data in the external memory DDR to the unified memory 1306 , the weight data to the weight memory 1302 , or the input data to the input memory 1301 .

The vector calculation unit 1307 includes a plurality of operation processing units, and if necessary, further processes the output of the operation circuit, such as vector multiplication, vector addition, exponential operation, logarithmic operation, size comparison and so on. It is mainly used for non-convolutional/fully connected layer network computations in neural networks, such as batch normalization, pixel-level summation, and upsampling of feature planes.

In some implementations, vector computation unit 1307 can store the processed output vectors to unified memory 1306 . For example, the vector calculation unit 1307 may apply a linear function and/or a non-linear function to the output of the operation circuit 1303, such as performing linear interpolation on the feature plane extracted by the convolution layer, such as a vector of accumulated values, to generate activation values. In some implementations, the vector computation unit 1307 generates normalized values, pixel-level summed values, or both. In some implementations, the vector of processed outputs can be used as an activation input to the arithmetic circuit 1303, such as for use in subsequent layers in a neural network.

An instruction fetch buffer 1309 connected to the controller 1304 is used to store the instructions used by the controller 1304 .

The unified memory 1306, the input memory 1301, the weight memory 1302 and the instruction fetch memory 1309 are all On-Chip memories. External memory is private to the NPU hardware architecture.

The operation of each layer in the recurrent neural network can be performed by the operation circuit 1303 or the vector calculation unit 1307 .

Wherein, the processor mentioned in any one of the above may be a general-purpose central processing unit, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the method in the first aspect.

In addition, it should be noted that the device embodiments described above are only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be A physical unit, which can be located in one place or distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment. In addition, in the drawings of the device embodiments provided in the present application, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines.

From the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary general-purpose hardware. Special components, etc. to achieve. Under normal circumstances, all functions completed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structures used to implement the same function can also be various, such as analog circuits, digital circuits or special circuit, etc. However, a software program implementation is a better implementation in many cases for this application. Based on this understanding, the technical solutions of the present application can be embodied in the form of software products in essence, or the parts that make contributions to the prior art. The computer software products are stored in a readable storage medium, such as a floppy disk of a computer. , U disk, mobile hard disk, ROM, RAM, magnetic disk or optical disk, etc., including several instructions to enable a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present application .

In the above-mentioned embodiments, it may be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented in software, it can be implemented in whole or in part in the form of a computer program product.

The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general purpose computer, special purpose computer, computer network, or other programmable device. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be downloaded from a website site, computer, server, or data center Transmission to another website site, computer, server, or data center by wire (eg, coaxial cable, optical fiber, digital subscriber line) or wireless (eg, infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be stored by a computer, or a data storage device such as a server, data center, etc., which includes one or more available media integrated. The usable media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVDs), or semiconductor media (eg, solid state disks (SSDs)), and the like.

Claims

A method for planning a driving route of a vehicle, comprising:

acquiring first information, where the first information includes one or more of vehicle location information, lane information, navigation information, and obstacle information;

When the vehicle is in the automatic driving mode, the first information is the input data of the first model, the output data of the first model is used to plan a driving route for the vehicle, and the first model is in the manual driving mode The driving trajectory of the vehicle is the training target, and the model obtained by training the second information obtained in the manual driving mode, the type of information included in the second information is consistent with the type of information included in the first information, The similarity between the first information and the second information satisfies a preset condition, and the similarity between the first information and the second information meets the preset condition to indicate that the first scene and the second scene are the same or similar , the first scene is the scene where the vehicle is located when the vehicle acquires the first information, and the second scene is the scene where the vehicle is located when the vehicle acquires the second information;

When the vehicle is in the manual driving mode, the first information is used to train the first model, and the output data of the first model is used to plan a driving route for the vehicle.
The method for planning a vehicle driving route according to claim 1, wherein the method further comprises:

Evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is information obtained when the vehicle is in a manual driving mode, and any two groups of information in the M groups of information The similarity does not meet the preset condition, and each group of information in the M groups of information is respectively used to train a model;

When the vehicle is in the automatic driving mode, the first information is the input data of the first model, including:

When the similarity between the first information and the second information in the M groups of information satisfies the preset condition, and the similarity between the second information and the first information in the M groups of information is the largest , the first information is the input data of the first model.
The method for planning a vehicle driving route according to claim 1, wherein the method further comprises:

Evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is information obtained when the vehicle is in a manual driving mode, and any two groups of information in the M groups of information The similarity does not meet the preset condition, each group of information in the M groups of information is used to train a model, the second information in the M groups of information is used to train the first model, the M is a positive integer;

When the vehicle is in the manual driving mode, the first information is used to train the first model, including:

When the similarity between the first information and the second information in the M groups of information satisfies the preset condition, and the similarity between the second information and the first information in the M groups of information is the largest , the first information is used to train the first model to obtain the updated first model.
The method for planning a vehicle driving route according to claim 1, wherein the method further comprises:

Evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is information obtained when the vehicle is in a manual driving mode, and any two groups of information in the M groups of information The similarity does not meet the preset condition, and each group of information in the M groups of information is used to train a model, and the M is a positive integer;

When the vehicle is in the manual driving mode, the first information is used to train the first model, including:

When the similarity between the first information and each of the M groups of information does not satisfy the preset condition, the first information is used to perform the first training on the first model.
The method for planning a vehicle travel route according to any one of claims 1 to 4, wherein, when the vehicle is in an automatic driving mode, the method further comprises:

When it is determined that the current position information of the vehicle is consistent with the position information of the vehicle obtained in the manual driving mode, a prompt message is sent, and the prompt message is used to instruct the vehicle to be in the desired state according to the output data of the first model. The vehicle plans a driving route, and the first model is a model obtained by training the position information of the vehicle obtained in the manual driving mode.
The method for planning a vehicle driving route according to any one of claims 1 to 4, wherein when the vehicle is in a manual driving mode, the method further comprises:

Sending a prompt message, where the prompt message is used to instruct to train the first model according to the current position information of the vehicle.
The method for planning a vehicle travel route according to any one of claims 1 to 6, wherein the method further comprises:

The similarity of the same type of information in the first information and the second information is determined, and the linear weighted sum of the similarity of the same type of information is used to determine the similarity of the first information and the second information.
The method according to any one of claims 1 to 7, wherein the first model comprises a convolutional neural network CNN or a recurrent neural network RNN.
A device for planning a driving route of a vehicle, comprising:

an acquisition module, configured to acquire first information, where the first information includes one or more of vehicle location information, lane information, navigation information, and obstacle information;

The regulation and control module is used for planning a driving route for the vehicle according to the output data of the first model when the vehicle is in the automatic driving mode, and the first information obtained by the obtaining module is the input data of the first model, so the The first model is a model obtained by training the second information obtained in the manual driving mode by taking the driving trajectory of the vehicle in the manual driving mode as the training target, the type of information included in the second information and the first information including The types of the information are consistent with each other, the similarity between the first information and the second information satisfies a preset condition, and the similarity between the first information and the second information satisfies the preset condition and is used to represent the first scene Same as or similar to the second scene, the first scene is the scene where the vehicle is located when the vehicle obtains the first information, and the second scene is the scene when the vehicle obtains the second information. The scene in which the vehicle is located;

A training module for training the first model according to the first information obtained by the obtaining module when the vehicle is in the manual driving mode, and the output data of the first model is used to plan a driving route for the vehicle .
The device for planning a vehicle travel route according to claim 9, further comprising:

An evaluation module, configured to evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is information obtained when the vehicle is in a manual driving mode, and the M groups of information The similarity of any two groups of information does not meet the preset condition, and each group of information in the M groups of information is respectively used to train a model;

When the similarity between the first information and the second information in the M groups of information satisfies the preset condition, and the similarity between the second information and the first information in the M groups of information is the largest , the first information is the input data of the first model.
The device for planning a vehicle travel route according to claim 9, further comprising:

An evaluation module, configured to evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is information obtained when the vehicle is in a manual driving mode, and the M groups of information The similarity of any two groups of information does not meet the preset condition, each group of information in the M groups of information is used to train a model, and the second information in the M groups of information is used to train the first model. a model, the M is a positive integer;

When the similarity between the first information and the second information in the M groups of information satisfies the preset condition, and the similarity between the second information and the first information in the M groups of information is the largest , the first information is used to train the first model to obtain the updated first model.
The device for planning a vehicle travel route according to claim 9, further comprising:

An evaluation module, configured to evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is information obtained when the vehicle is in a manual driving mode, and the M groups of information The similarity of any two groups of information does not meet the preset condition, and each group of information in the M groups of information is respectively used to train a model, and the M is a positive integer;

When the similarity between the first information and each of the M groups of information does not satisfy the preset condition, the first information is used to perform the first training on the first model.
The device for planning a vehicle travel route according to any one of claims 9 to 12, further comprising:

The sending module is configured to send a prompt message when the evaluation module determines that the current position information of the vehicle is consistent with the position information of the vehicle obtained in the manual driving mode, and the prompt message is used to instruct the vehicle to The output data of the first model is the planned driving route of the vehicle, and the first model is a model obtained by training the position information of the vehicle obtained in the manual driving mode.
The device for planning a vehicle travel route according to any one of claims 9 to 12, further comprising:

A sending module, configured to send a prompt message, where the prompt message is used to instruct to train the first model according to the current position information of the vehicle.
The device for planning a vehicle travel route according to any one of claims 9 to 14, further comprising:

A processing module, configured to determine the similarity of the same type of information in the first information and the second information, and the linear weighted sum of the similarity of the same type of information is used to determine the first information and the second information similarity of information.
The apparatus according to any one of claims 9 to 15, wherein the first model comprises a convolutional neural network CNN or a recurrent neural network RNN.
A system for planning a driving route of a vehicle, characterized in that the system includes a vehicle and a cloud-side device,

the vehicle, for acquiring first information, where the first information includes one or more of the vehicle's position information, lane information, navigation information, and obstacle information;

the vehicle, further configured to send the first information and the driving mode of the vehicle to the cloud-side device;

The cloud-side device is configured to plan a driving route for the vehicle according to the output data of the first model when it is determined that the vehicle is in the automatic driving mode, the first information is the input data of the first model, and the The first model takes the driving trajectory of the vehicle in the manual driving mode as the training target, and is a model obtained by training the second information obtained in the manual driving mode. The type of information included in the second information is the same as the information included in the first information. The types of information are the same, the similarity between the first information and the second information meets a preset condition, and the similarity between the first information and the second information meets the preset condition and is used to represent the first scene and The second scene is the same or similar, the first scene is the scene where the vehicle is when the vehicle acquires the first information, and the second scene is the vehicle when the vehicle acquires the second information the scene;

The cloud-side device is further configured to train the first model according to the first information when it is determined that the vehicle is in the manual driving mode, and the output data of the first model is used to plan a driving route for the vehicle.
The system for planning vehicle travel routes according to claim 17, wherein the cloud-side device is further used for:

Evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is information obtained when the vehicle is in a manual driving mode, and any two groups of information in the M groups of information The similarity does not meet the preset condition, and each group of information in the M groups of information is respectively used to train a model;

When the similarity between the first information and the second information in the M groups of information satisfies the preset condition, and the similarity between the second information and the first information in the M groups of information is the largest , the first information is the input data of the first model.
The system for planning vehicle travel routes according to claim 17, wherein the cloud-side device is further used for:

Evaluate the similarity between the first information and the M groups of information, where each group of information in the M groups of information is information obtained when the vehicle is in a manual driving mode, and any two groups of information in the M groups of information The similarity does not meet the preset condition, each group of information in the M groups of information is used to train a model, the second information in the M groups of information is used to train the first model, the M is a positive integer;

When the similarity between the first information and the second information in the M groups of information satisfies the preset condition, and the similarity between the second information and the first information in the M groups of information is the largest , the first information is used to train the first model to obtain the updated first model.
A device for planning a driving route of a vehicle, characterized in that it comprises a processor, the processor is coupled to a memory, the memory stores program instructions, and when the program instructions stored in the memory are executed by the processor, a right is realized The method of any one of claims 1 to 8.
A computer-readable storage medium comprising a program which, when run on a computer, causes the computer to perform the method of any one of claims 1 to 8.
A smart car, characterized in that the smart car includes a processing circuit and a storage circuit, the processing circuit and the storage circuit being configured to perform the method of any one of claims 1 to 8 .