CN114771539A

CN114771539A - Vehicle lane change decision method, device, storage medium and vehicle

Info

Publication number: CN114771539A
Application number: CN202210682478.1A
Authority: CN
Inventors: 崔志; 刘力文; 张弛
Original assignee: Xiaomi Automobile Technology Co Ltd
Current assignee: Xiaomi Automobile Technology Co Ltd
Priority date: 2022-06-16
Filing date: 2022-06-16
Publication date: 2022-07-22
Anticipated expiration: 2042-06-16
Also published as: CN114771539B

Abstract

The disclosure relates to the field of automatic driving, in particular to a vehicle lane change decision method, a device, a storage medium and a vehicle, wherein the method comprises the following steps: acquiring data to be processed for deciding whether the vehicle changes lanes or not; determining lane change probability through a pre-trained lane change decision model according to data to be processed; whether the vehicle changes lanes is determined according to the preset lane change probability threshold value and the lane change probability, and whether the vehicle changes lanes is determined in an auxiliary mode by setting the safe preset lane change probability threshold value, so that the lane change judgment accuracy of the vehicle is effectively improved, and the driving safety of the vehicle is further improved.

Description

Vehicle lane change decision method and device, storage medium and vehicle

Technical Field

The disclosure relates to the field of automatic driving, and in particular to a vehicle lane change decision method, a vehicle lane change decision device, a storage medium and a vehicle.

Background

The automatic driving automobile depends on the cooperation of artificial intelligence, visual calculation, radar, monitoring device and global positioning system, so that the computer can operate the motor vehicle automatically and safely without any active operation of human.

At present, traffic accidents caused by lane changes of vehicles are quite common. Therefore, for automatic driving, it is important to accurately determine whether the lane of the vehicle can be changed.

Disclosure of Invention

In order to overcome the problems in the related art, the present disclosure provides a vehicle lane change decision method, device, storage medium and vehicle.

According to a first aspect of the embodiments of the present disclosure, there is provided a vehicle lane change decision method, including:

acquiring data to be processed for deciding whether the vehicle changes lanes or not;

determining lane change probability through a pre-trained lane change decision model according to the data to be processed;

and determining whether the vehicle changes lanes or not according to a preset lane change probability threshold value and the lane change probability.

Optionally, the method further includes:

acquiring running data of the vehicle at a plurality of moments in the running process of the vehicle in the manual driving mode, wherein the running data at each moment comprises first data and second data corresponding to the first data;

when the lane change pre-action of the vehicle is detected according to first data in the driving data, determining second data corresponding to the first data as lane change sample data;

when detecting that the lane change preaction does not occur on the host vehicle according to first data in the driving data, determining second data corresponding to the first data as non-lane change sample data;

and determining sample data for training the lane change decision model according to the lane change sample data and the non-lane change sample data.

Optionally, the method further includes:

acquiring a driving scene of the vehicle;

and updating the preset lane change probability threshold value under the condition that the driving scene is changed compared with the acquired last driving scene of the vehicle.

Optionally, the to-be-processed data includes multiple groups, and the acquiring the to-be-processed data for deciding whether the vehicle changes lanes includes:

acquiring data to be processed for deciding whether the vehicle changes lanes or not for multiple times in a preset period;

the determining whether the vehicle changes lanes according to a preset lane change probability threshold and the lane change probability comprises:

and determining whether the vehicle changes lanes or not according to a preset lane change probability threshold value and the lane change probabilities corresponding to all the data to be processed.

Optionally, the sample data includes a training set and a test set, and the method further includes:

training the initial lane change decision model for multiple times through the training set to obtain a plurality of intermediate lane change decision models;

testing the parameters of each intermediate lane change decision model through the test set to obtain test results respectively corresponding to each intermediate lane change decision model;

determining an evaluation result corresponding to each intermediate lane changing decision model according to a test result corresponding to each intermediate lane changing decision model, wherein the evaluation result comprises evaluation index values of a plurality of performance indexes;

and selecting the intermediate lane change decision model with the optimal evaluation result as the lane change decision model according to the evaluation results corresponding to all the intermediate lane change decision models.

Optionally, the method further includes:

acquiring a lane change data set, wherein the lane change data set comprises result data for determining lane change and/or lane non-change through the lane change decision model and the preset lane change threshold probability;

in response to determining that result data characterizing an anomaly in the lane change dataset, determining that the result data is anomalous lane change result data;

and updating the lane change decision model under the condition that the number of the abnormal lane change result data determined in the lane change data set is greater than a preset number threshold.

Optionally, the data to be processed includes vehicle lane type data, adjacent vehicle lane type data, vehicle state data, adjacent vehicle state data, relationship data between the position of the vehicle and the navigation information, and relationship data between the position determined by projecting the vehicle to the adjacent vehicle lane and the navigation information.

According to a second aspect of an embodiment of the present disclosure, there is provided a vehicle lane change decision device, including:

a first acquisition module configured to acquire data to be processed for deciding whether the host vehicle changes lanes;

the first determining module is configured to determine lane change probability through a pre-trained lane change decision model according to the data to be processed;

a second determination module configured to determine whether the host vehicle changes lanes according to a preset lane change probability threshold and the lane change probability.

According to a third aspect of an embodiment of the present disclosure, there is provided a vehicle lane change decision device, including:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to:

According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the steps of the vehicle lane change decision method provided by the first aspect of the present disclosure.

According to a fifth aspect of an embodiment of the present disclosure, there is provided a vehicle including the apparatus described in the third aspect.

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects: acquiring data to be processed for deciding whether the vehicle changes lanes or not; determining lane change probability through a pre-trained lane change decision model according to data to be processed; whether the vehicle changes lanes is determined according to the preset lane change probability threshold value and the lane change probability, and whether the vehicle changes lanes is determined in an auxiliary mode by setting the safe preset lane change probability threshold value, so that the lane change judgment accuracy of the vehicle is effectively improved, and the driving safety of the vehicle is further improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.

FIG. 1 is a flow chart illustrating a vehicle lane change decision method in accordance with an exemplary embodiment.

FIG. 2 is a block diagram illustrating a vehicle lane change decision device in accordance with an exemplary embodiment.

FIG. 3 is another block diagram illustrating a vehicle lane change decision device in accordance with an exemplary embodiment.

FIG. 4 is a functional block diagram schematic of a vehicle, shown in accordance with an exemplary embodiment.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

It should be noted that all actions of acquiring signals, information or data in the present application are performed under the premise of complying with the corresponding data protection regulation policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.

Fig. 1 is a flowchart illustrating a lane change decision method for a vehicle according to an exemplary embodiment, where the lane change decision method for a vehicle is used in a terminal, as shown in fig. 1, and includes the following steps.

In step S11, data to be processed for deciding whether the host vehicle changes lanes is acquired.

In some embodiments, the data to be processed may include data related to the vehicle itself, and the data to be processed may also include data related to navigation information of the vehicle, where the vehicle includes a host vehicle and adjacent vehicles, and the adjacent vehicles may be vehicles adjacent to the host vehicle in front, back, left and right directions. Specific examples of the data to be processed may refer to the following embodiments.

For example, the data to be processed may include the lane type data of the vehicle, the lane type data of the adjacent vehicle, the state data of the adjacent vehicle, the relationship data between the position of the vehicle and the navigation information, and the relationship data between the position determined by projecting the vehicle to the lane of the adjacent vehicle and the navigation information.

The host vehicle lane type data and the adjacent vehicle lane type data are data for characterizing a lane type, which may be, for example, a motor lane type, a non-motor lane type.

The vehicle state data and the adjacent vehicle state data are used for representing the state of the vehicle, and the vehicle state data can be the speed of the vehicle, the acceleration of the vehicle, the distance between the vehicle and the adjacent vehicle, the remaining distance between the vehicle and the current intersection, and the distance between the vehicle and the front solid line; the adjacent vehicle state data may be, for example, the speed of the adjacent vehicle and the acceleration of the adjacent vehicle.

The navigation information may include a navigation road, and the navigation road represents a passage, and in the case that the navigation information is the navigation road, the relationship data between the position of the host vehicle and the navigation information may include whether the road where the host vehicle is located belongs to the passage.

The navigation information may include a navigation end point, the navigation end point characterizing an end point of each navigation sub-path. It should be noted that, in the navigation process, the navigation process is usually divided into a plurality of navigation sub-paths from the departure point to the destination point, where each navigation sub-path corresponds to a navigation end point, and the navigation end point of the last navigation sub-path is the destination point. When the navigation information is a navigation end point, the relationship data between the vehicle lane and the navigation information may be a distance from the position of the vehicle to the navigation end point of the navigation sub-path, that is, the relationship data is used to describe how many meters the vehicle can travel in the vehicle lane, and the vehicle cannot travel any more, otherwise the vehicle cannot reach the navigation end point by the optimal travel path.

Similarly, the relationship data of the position determined by the projection of the host vehicle to the adjacent vehicle lane and the navigation information may refer to the above-described related embodiments.

In step S12, a lane change probability is determined by a pre-trained lane change decision model according to the data to be processed.

The pre-trained lane change decision model is used for outputting lane change probability according to the data to be processed. The training mode of the lane change decision model may refer to related technologies, and this embodiment is not described herein again.

It should be noted that, when the adjacent vehicle lanes of the vehicle lane include a plurality of lanes, the lane change decision model may output lane change probabilities corresponding to the adjacent vehicle lanes.

In step S13, it is determined whether the host vehicle changes lanes according to a preset lane change probability threshold and a lane change probability.

It should be noted that the preset lane change probability threshold is a critical value for determining whether the vehicle changes lanes. For example, in the case where the lane change probability is greater than or equal to a preset lane change probability threshold value, it is determined that the host vehicle changes lanes. And under the condition that the lane change probability is smaller than a preset lane change probability threshold value, determining that the vehicle does not change lanes.

It should be noted that, when the adjacent vehicle lanes of the vehicle lane include a plurality of lanes, the lane change decision model may output lane change probabilities corresponding to the adjacent vehicle lanes. In this case, the lane change probability corresponding to each adjacent vehicle lane may be compared with a preset lane change probability threshold to determine whether the vehicle changes the lane, and in the case that the lane change probability of at least two adjacent vehicle lanes is greater than or equal to the preset lane change probability threshold, a target lane may be selected for lane change according to a preset condition. The condition here may be, for example, a traffic lane congestion situation, and the vehicle may be changed by selecting a traffic lane with a better congestion situation as the target traffic lane.

By the mode, the safe preset lane change probability threshold is set to assist in determining whether the vehicle changes lanes or not, so that the lane change judgment accuracy of the vehicle is effectively improved, and the driving safety of the vehicle is further improved.

In some embodiments, the real driving data of the host vehicle in the manual driving mode may be collected as sample data to perform training of the lane change decision model. By way of example, the method comprises the following steps: collecting driving data of a vehicle at a plurality of moments in the driving process of the vehicle in an artificial driving mode, wherein the driving data at each moment comprise first data and second data corresponding to the first data; when the lane change pre-action of the vehicle is detected according to first data in the driving data, determining second data corresponding to the first data as lane change sample data; when the situation that lane change pre-action of the vehicle is not generated is detected according to first data in the driving data, second data corresponding to the first data are determined as non-lane change sample data; and determining sample data for training the lane change decision model according to the lane change sample data and the non-lane change sample data.

The first data is used for judging whether the vehicle generates lane change preaction. For example, the first data may be turn signal status data, and in the case that the turn signal status data indicates that the driver triggers the turn signal, it is determined that the lane change pre-action occurs in the host vehicle; the first data may be a distance between the host vehicle and a lane line of the host vehicle lane, and the lane change pre-action may be determined to be generated when the distance between the host vehicle and the lane line of the host vehicle lane gradually decreases. In order to avoid misoperation, at least two kinds of first data can be set for judging whether the vehicle generates lane changing preaction.

Determining the second data corresponding to the first data as lane change sample data and determining the second data corresponding to the first data as non-lane change sample data can be achieved by tagging the data. For example, the label of the lane change sample data may be set to 1, and the label of the non-lane change sample data may be set to 0, so as to facilitate the sample data use of the subsequent training lane change decision model.

According to the mode, after a large amount of real driving data of the vehicle is collected, second data corresponding to first data are determined to be lane change sample data or non-lane change sample data according to the first data representing whether lane change pre-action occurs on the vehicle, and a label is marked on the second data at each moment to obtain sample data of a training lane change decision model, so that a lane change decision of a human is simulated through the real driving data of the vehicle, and then training of the lane change decision model is carried out, so that the trained lane change decision model can simulate and output the lane change decision of the human according to data to be processed in the actual driving process; and no manual participation is needed in the sample generation process.

In the training process of the lane change decision model, the determined sample data can be divided into a training set and a test set, and the initial lane change decision model is trained for multiple times through the training set to obtain a plurality of intermediate lane change decision models; testing the parameters of each intermediate lane change decision model through a test set to obtain test results respectively corresponding to each intermediate lane change decision model; determining an evaluation result corresponding to each intermediate lane changing decision model according to the test result corresponding to each intermediate lane changing decision model, wherein the evaluation result comprises evaluation index values of a plurality of performance indexes; and selecting the intermediate lane change decision model with the optimal evaluation result as the lane change decision model according to the evaluation results corresponding to all the intermediate lane change decision models.

The test result may include a predicted lane change probability corresponding to each sample data in the test set, and the predicted lane change result (including lane change or lane non-change) corresponding to each sample data may be determined according to the predicted lane change probability. And determining the evaluation result of each intermediate lane change decision model according to the predicted lane change result corresponding to each sample data and the label corresponding to each sample.

For example, the performance index may include accuracy, meaning the ratio of the number of samples predicted to be correct to the total number of samples; the performance indicators may include precision, meaning how many tags are actually the number of samples of lane change, among the samples predicted to be lane change; performance indicators may include recall, meaning how many of the actual correct samples in the total number of samples were picked by the intermediate lane change decision model; the performance indicators may include a reconciliation indicator of accuracy rate with recall rate, the reconciliation indicator being a measure of the ability of the model to find the positive case, which is calculated from the accuracy rate and the recall rate, for example, the reconciliation indicator = (2 × Q1 × Q2)/(Q1 + Q2), where Q1 is the accuracy rate and Q2 is the recall rate.

It should be noted that, when the performance indexes include a plurality of performance indexes, a middle lane change decision model with a high performance index may be selected as the optimal evaluation result according to the actual model requirement. For example, with more attention to accuracy, the middle lane-changing decision model with the highest accuracy can be selected as the one with the best evaluation result; or normalizing a plurality of performance indexes to the same magnitude, and taking the intermediate lane change decision model with the highest sum of all the normalized performance indexes as the optimal evaluation result. The present embodiment is not limited thereto.

Through the mode, the training set is used for training the plurality of intermediate lane change decision models, the test set is used for practical application according to the selected intermediate lane change decision model with the optimal performance index, and the lane change judgment accuracy of the vehicle is effectively improved.

In some embodiments, when the test set and the training set are divided, the repetition degree of the test set and the training set can be ensured to be lower than a preset degree, so that the recall rate can be better checked.

In practical application, in order to continuously optimize the model, a lane change data set which comprises lane change and/or non-lane change result data and is determined by a lane change decision model and a preset lane change threshold probability can be obtained; in response to determining that the lane change data set represents abnormal result data, determining the result data as abnormal lane change result data; and updating the lane change decision model under the condition that the number of the abnormal lane change result data determined in the lane change data set is greater than a preset number threshold.

It should be noted that the result data characterizing the anomaly is determined by an expert. And the characterization abnormity characterizes that an expert considers that the lane is not changed, but the lane changing decision model and the preset lane changing threshold probability are determined as lane changing, or the expert considers that the lane is changed but the lane changing decision model and the preset lane changing threshold probability are determined as the result data of the lane changing.

The preset number threshold may be set manually, and this embodiment is not limited herein.

Through the mode, the expert data is used for determining the data actually predicted by the lane change decision model and the preset lane change probability threshold, and when the data of the results of abnormal prediction of the lane change decision model and the preset lane change probability threshold are more, the lane change decision model is updated, so that the accuracy of the lane change decision model is further improved.

In practical application, driving scenes with different risk degrees have different requirements on lane change safety. For example, for a high-speed driving scene, in the case that all vehicles are driven at high speed, a lane change accident will be caused, and therefore, based on the foregoing, the method may further include: acquiring a driving scene of the vehicle; and updating the preset lane change probability threshold value under the condition that the driving scene is changed compared with the acquired last driving scene of the vehicle.

For example, the corresponding relationship between different driving scenes and the preset lane change probability threshold corresponding to the different driving scenes may be set. And according to the acquired driving scene of the vehicle, determining a target preset lane change probability threshold value corresponding to the driving scene in the corresponding relation, and replacing the preset lane change probability threshold value with the target preset lane change probability threshold value to update the preset lane change probability threshold value.

Illustratively, the driving scenarios may include high speed driving scenarios, rural driving scenarios, urban driving scenarios, and the like. The present embodiment is not limited thereto. The preset lane change probability threshold corresponding to different driving scenes may be manually set, and this embodiment is not limited herein.

By the method, different preset lane change probability threshold values are set for different driving scenes, so that lane change flexibility is improved; and the preset lane change probability threshold is updated only when the driving scene changes compared with the last driving scene of the vehicle, so that the power consumption caused by control instructions generated by updating and the like is reduced.

In some embodiments, to improve the accuracy of the lane change prediction, the data to be processed may include multiple sets, and step S11 shown in fig. 1 may include: acquiring data to be processed for deciding whether the vehicle changes lanes or not for multiple times in a preset period; in this case, step S13 shown in fig. 1 may include: and determining whether the vehicle changes lanes or not according to a preset lane change probability threshold value and the lane change probabilities corresponding to all the data to be processed.

The preset period may be set according to actual conditions, and this embodiment is not limited herein.

For example, the step of determining whether the host vehicle changes lanes according to the preset lane change probability threshold and the lane change probabilities corresponding to all the data to be processed may include: determining whether the vehicle changes lanes or not according to a preset lane change probability threshold value and the lane change probability corresponding to each data to be processed; and determining that the lane of the vehicle is changed when the number of times of determining that the lane of the vehicle is changed is larger than the number of times of determining that the lane of the vehicle is not changed.

By the mode, whether the vehicle changes the lane or not is determined by combining lane changing probabilities of multiple groups of data to be processed, so that the accuracy of a lane changing decision model is further improved.

FIG. 2 is a block diagram illustrating a vehicle lane change decision-making device 200, according to an exemplary embodiment. Referring to fig. 2, the vehicle lane change decision apparatus 200 includes:

a first obtaining module 201 configured to obtain data to be processed for deciding whether the host vehicle changes lanes;

a first determining module 202, configured to determine a lane change probability according to the data to be processed through a pre-trained lane change decision model;

a second determining module 203 configured to determine whether the host vehicle changes lanes according to a preset lane change probability threshold and the lane change probability.

Optionally, the vehicle lane change decision device 200 further includes:

the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is configured to acquire running data of the host vehicle at a plurality of moments in the running process of an artificial driving mode, and the running data at each moment comprises first data and second data corresponding to the first data;

a third determination module configured to determine second data corresponding to first data as lane change sample data when detecting that the lane change preaction of the host vehicle occurs according to the first data in the driving data;

a fourth determination module configured to determine second data corresponding to first data as non-lane change sample data when it is detected that the lane change pre-action does not occur on the host vehicle according to the first data in the driving data;

a fifth determining module configured to determine sample data for training the lane change decision model according to the lane change sample data and the non-lane change sample data.

Optionally, the vehicle lane change decision device 200 further includes:

a second acquisition module configured to acquire a driving scene of the host vehicle;

a first updating module configured to update the preset lane change probability threshold in a case where the driving scene changes from the acquired last driving scene of the host vehicle.

Optionally, the data to be processed includes multiple sets, and the first obtaining module 201 is specifically configured to obtain the data to be processed for deciding whether the vehicle changes lanes multiple times within a preset period;

the second determining module 203 is specifically configured to determine whether the host vehicle changes lanes according to a preset lane change probability threshold and the lane change probabilities corresponding to all the data to be processed.

Optionally, the sample data includes a training set and a test set, and the vehicle lane-changing decision apparatus 200 further includes:

the training module is configured to train the initial lane change decision model for multiple times through the training set to obtain multiple intermediate lane change decision models;

the testing module is configured to test the parameters of each intermediate lane changing decision model through the testing set to obtain testing results corresponding to each intermediate lane changing decision model;

a sixth determining module, configured to determine, according to the test result corresponding to each of the intermediate lane-changing decision models, an evaluation result corresponding to each of the intermediate lane-changing decision models, wherein the evaluation result includes evaluation index values of a plurality of performance indexes;

and the selection module is configured to select the intermediate lane changing decision model with the optimal evaluation result as the lane changing decision model according to the evaluation results corresponding to all the intermediate lane changing decision models.

Optionally, the vehicle lane change decision device 200 further includes:

a third obtaining module, configured to obtain a lane change data set, where the lane change data set includes result data of determining lane change and/or lane non-change through the lane change decision model and the preset lane change threshold probability;

a seventh determination module configured to determine, in response to determining that result data characterizing an anomaly in the lane-change dataset, that the result data is anomalous lane-change result data;

a second updating module configured to update the lane-change decision model in case that the number of the abnormal lane-change result data determined in the lane-change data set is greater than a preset number threshold.

With regard to the apparatus in the above-mentioned embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the lane change decision method for the vehicle, and will not be elaborated herein.

The present disclosure also provides a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the vehicle lane change decision method provided by the present disclosure.

FIG. 3 is another block diagram of a vehicle lane change decision-making device 300, according to an exemplary embodiment. For example, the apparatus 300 may be a mobile phone, tablet device, or the like.

Referring to fig. 3, the apparatus 300 may include one or more of the following components: a processing component 302, a first memory 304, a power component 306, a multimedia component 308, an audio component 310, an input/output interface 312, a sensor component 314, and a communication component 316.

The processing component 302 generally controls overall operation of the device 300, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 302 may include one or more first processors 320 to execute instructions to perform all or a portion of the steps of the vehicle lane change decision method described above. Further, processing component 302 may include one or more modules that facilitate interaction between processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate interaction between the multimedia component 308 and the processing component 302.

The first memory 304 is configured to store various types of data to support operations at the apparatus 300. Examples of such data include instructions for any application or method operating on device 300, contact data, phonebook data, messages, pictures, videos, and so forth. The first memory 304 may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

The power supply component 306 provides power to the various components of the device 300. The power components 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the apparatus 300.

The multimedia component 308 includes a screen that provides an output interface between the device 300 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 308 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 300 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 310 is configured to output and/or input audio signals. For example, audio component 310 includes a Microphone (MIC) configured to receive external audio signals when apparatus 300 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may further be stored in the first memory 304 or transmitted via the communication component 316. In some embodiments, audio component 310 also includes a speaker for outputting audio signals.

The input/output interface 312 provides an interface between the processing component 302 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 314 includes one or more sensors for providing various aspects of status assessment for the device 300. For example, sensor assembly 314 may detect the open/closed status of device 300, the relative positioning of components, such as a display and keypad of device 300, the change in position of device 300 or a component of device 300, the presence or absence of user contact with device 300, the orientation or acceleration/deceleration of device 300, and the change in temperature of device 300. Sensor assembly 314 may include a proximity sensor configured to detect the presence of a nearby object in the absence of any physical contact. The sensor assembly 314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 316 is configured to facilitate wired or wireless communication between the apparatus 300 and other devices. The device 300 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the apparatus 300 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors, or other electronic components for performing the above-described vehicle lane-change decision method.

In an exemplary embodiment, a non-transitory computer readable storage medium comprising instructions, such as the first memory 304 comprising instructions, executable by the first processor 320 of the apparatus 300 to perform the vehicle lane change decision method described above is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

The apparatus 300 may be a part of a stand-alone electronic device, besides a stand-alone electronic device, for example, in an embodiment, the apparatus 300 may be an Integrated Circuit (IC) or a chip, where the IC may be one IC or a collection of ICs; the chip may include, but is not limited to, the following categories: a GPU (Graphics Processing Unit), a CPU (Central Processing Unit), an FPGA (Field Programmable Gate Array), a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an SOC (System on Chip, SOC, System on Chip, or System on Chip), and the like. The integrated circuit or chip may be configured to execute executable instructions (or code) to implement the vehicle lane-change decision method. Where the executable instructions may be stored in the integrated circuit or chip or may be retrieved from another apparatus or device, for example where the integrated circuit or chip includes a second processor, a second memory, and an interface for communicating with the other apparatus. The executable instructions may be stored in the second memory, and when executed by the second processor, implement the vehicle lane-change decision method described above; alternatively, the integrated circuit or chip may receive the executable instructions through the interface and transmit the executable instructions to the second processor for execution, so as to implement the vehicle lane change decision method.

Referring to fig. 4, fig. 4 is a functional block diagram of a vehicle 400 according to an exemplary embodiment. The vehicle 400 may be configured in a fully or partially autonomous driving mode. For example, the vehicle 400 may acquire environmental information of its surroundings through the perception system 420 and derive an automatic driving strategy based on an analysis of the surrounding environmental information to implement full automatic driving, or present the analysis results to the user to implement partial automatic driving.

The vehicle 400 may include various subsystems, such as an infotainment system 410, a perception system 420, a decision control system 430, a drive system 440, and a computing platform 450. Alternatively, vehicle 400 may include more or fewer subsystems, and each subsystem may include multiple components. In addition, each of the sub-systems and components of the vehicle 400 may be interconnected by wire or wirelessly.

In some embodiments, the infotainment system 410 may include a communication system 411, an entertainment system 412, and a navigation system 413.

The communication system 411 may comprise a wireless communication system that may wirelessly communicate with one or more devices, either directly or via a communication network. For example, the wireless communication system may use 3G cellular communication, such as CDMA, EVD0, GSM/GPRS, or 4G cellular communication, such as LTE. Or 5G cellular communication. The wireless communication system may communicate with a Wireless Local Area Network (WLAN) using WiFi. In some embodiments, the wireless communication system may utilize an infrared link, bluetooth, or ZigBee to communicate directly with the device. Other wireless protocols, such as various vehicular communication systems, for example, a wireless communication system may include one or more Dedicated Short Range Communications (DSRC) devices that may include public and/or private data communications between vehicles and/or roadside stations.

The entertainment system 412 may include a display device, a microphone, and a sound, and a user may listen to a radio in the car, play music, based on the entertainment system; or the mobile phone is communicated with the vehicle, the screen projection of the mobile phone is realized on the display equipment, the display equipment can be in a touch control mode, and a user can operate the display equipment by touching the screen.

In some cases, the voice signal of the user may be acquired through a microphone, and certain control of the vehicle 400 by the user, such as adjusting the temperature in the vehicle, etc., may be implemented according to the analysis of the voice signal of the user. In other cases, music may be played to the user through a sound.

The navigation system 413 may include a map service provided by a map provider to provide navigation of a travel route for the vehicle 400, and the navigation system 413 may be used in conjunction with a global positioning system 421 and an inertial measurement unit 422 of the vehicle. The map service provided by the map provider can be a two-dimensional map or a high-precision map.

The perception system 420 may include several sensors that sense information about the environment surrounding the vehicle 400. For example, the sensing system 420 may include a global positioning system 421 (the global positioning system may be a GPS system, a compass system, or other positioning system), an Inertial Measurement Unit (IMU) 422, a laser radar 423, a millimeter wave radar 424, an ultrasonic radar 425, and a camera 426. The sensing system 420 may also include sensors of internal systems of the monitored vehicle 400 (e.g., an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, etc.). Sensor data from one or more of these sensors may be used to detect the object and its corresponding characteristics (position, shape, orientation, velocity, etc.). Such detection and identification is a critical function of the safe operation of the vehicle 400.

Global positioning system 421 is used to estimate the geographic location of vehicle 400.

The inertial measurement unit 422 is used to sense the pose change of the vehicle 400 based on the inertial acceleration. In some embodiments, the inertial measurement unit 422 may be a combination of an accelerometer and a gyroscope.

Lidar 423 utilizes laser light to sense objects in the environment in which vehicle 400 is located. In some embodiments, lidar 423 may include one or more laser sources, laser scanners, and one or more detectors, among other system components.

Millimeter-wave radar 424 utilizes radio signals to sense objects within the surrounding environment of vehicle 400. In some embodiments, in addition to sensing objects, the millimeter-wave radar 424 may also be used to sense the speed and/or heading of objects.

The ultrasonic radar 425 may sense objects around the vehicle 400 using ultrasonic signals.

The camera 426 is used to capture image information of the surroundings of the vehicle 400. The camera 426 may include a monocular camera, a binocular camera, a structured light camera, a panoramic camera, and the like, and the image information acquired by the camera 426 may include still images and may also include video stream information.

Decision control system 430 includes a computing system 431 for making analytical decisions based on information obtained by sensing system 420, and decision control system 430 further includes a vehicle control unit 432 for controlling the powertrain of vehicle 400, and a steering system 433, throttle 434, and braking system 435 for controlling vehicle 400.

The computing system 431 may be operable to process and analyze various information acquired by the perception system 420 in order to identify objects, and/or features in the environment surrounding the vehicle 400. The target may comprise a pedestrian or an animal and the objects and/or features may comprise traffic signals, road boundaries and obstacles. The computing system 431 may use object recognition algorithms, Motion from Motion (SFM) algorithms, video tracking, and the like. In some embodiments, the computing system 431 may be used to map an environment, track objects, estimate the speed of objects, and so forth. The computing system 431 may analyze the various information obtained and derive a control strategy for the vehicle.

The vehicle control unit 432 may be used to perform coordinated control on the power battery and the engine 441 of the vehicle to improve the power performance of the vehicle 400.

The steering system 433 is operable to adjust the heading of the vehicle 400. For example, in one embodiment, a steering wheel system.

The throttle 434 is used to control the operating speed of the engine 441 and, in turn, the speed of the vehicle 400.

The braking system 435 is used to control the deceleration of the vehicle 400. The braking system 435 may use friction to slow the wheels 444. In some embodiments, the braking system 435 may convert the kinetic energy of the wheels 444 into electrical current. The braking system 435 may take other forms to slow the rotational speed of the wheels 444 to control the speed of the vehicle 400.

The drive system 440 may include components that provide powered motion to the vehicle 400. In one embodiment, drive system 440 may include an engine 441, an energy source 442, a transmission 443, and wheels 444. The engine 441 may be an internal combustion engine, an electric motor, an air compression engine, or other types of engine combinations, such as a hybrid engine consisting of a gasoline engine and an electric motor, a hybrid engine consisting of an internal combustion engine and an air compression engine. The engine 441 converts the energy source 442 into mechanical energy.

Examples of energy source 442 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electrical power. The energy source 442 may also provide energy to other systems of the vehicle 400.

The transmission system 443 may transmit mechanical power from the engine 441 to the wheels 444. The driveline 443 may include a gearbox, a differential, and a driveshaft. In one embodiment, the transmission system 443 may also include other devices, such as clutches. Wherein the drive shaft may include one or more axles that may be coupled to one or more wheels 444.

Some or all of the functions of the vehicle 400 are controlled by the computing platform 450. The computing platform 450 may include at least one third processor 451, and the third processor 451 may execute instructions 453 stored in a non-transitory computer readable medium, such as the third memory 452. In some embodiments, the computing platform 450 may also be a plurality of computing devices that control individual components or subsystems of the vehicle 400 in a distributed manner.

The third processor 451 may be any conventional processor, such as a commercially available CPU. Alternatively, the third processor 451 may also include, for example, a Graphics Processor (GPU), a Field Programmable Gate Array (FPGA), a System On Chip (SOC), an Application Specific Integrated Circuit (ASIC), or a combination thereof. Although fig. 4 functionally illustrates processors, memories, and other elements of the computer in the same block, one of ordinary skill in the art will appreciate that the processors, computers, or memories may actually comprise multiple processors, computers, or memories that may or may not be stored within the same physical housing. For example, the memory may be a hard drive or other storage medium located in a different enclosure than the computer. Thus, reference to a processor or computer will be understood to include reference to a collection of processors or computers 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 component and the retarding component, may each have their own processor that performs only computations related to the component-specific functions.

In the disclosed embodiment, the third processor 451 may perform the vehicle lane change decision method described above.

In various aspects described herein, the third processor 451 may be located remotely from the vehicle and in wireless communication with the vehicle. In other aspects, some of the processes described herein are executed on a processor disposed within the vehicle and others are executed by a remote processor, including taking the steps necessary to execute a single maneuver.

In some embodiments, the third memory 452 may include instructions 453 (e.g., program logic), the instructions 453 being executable by the third processor 451 to perform various functions of the vehicle 400. The third memory 452 may also contain additional instructions, including instructions to send data to, receive data from, interact with, and/or control one or more of the infotainment system 410, the perception system 420, the decision control system 430, the drive system 440.

In addition to instructions 453, the third memory 452 may also store data such as road maps, route information, the position, direction, speed of the vehicle, and other such vehicle data, among other information. Such information may be used by the vehicle 400 and the computing platform 450 during operation of the vehicle 400 in autonomous, semi-autonomous, and/or manual modes.

Computing platform 450 may control the functions of vehicle 400 based on inputs received from various subsystems, such as drive system 440, perception system 420, and decision control system 430. For example, computing platform 450 may utilize input from decision control system 430 in order to control steering system 433 to avoid obstacles detected by perception system 420. In some embodiments, the computing platform 450 is operable to provide control over many aspects of the vehicle 400 and its subsystems.

Optionally, one or more of these components described above may be mounted or associated separately from the vehicle 400. For example, the third memory 452 may exist partially or completely separate from the vehicle 400. The aforementioned components may be communicatively coupled together in a wired and/or wireless manner.

Optionally, the above components are only an example, in an actual application, components in the above modules may be added or deleted according to an actual need, and fig. 4 should not be construed as limiting the embodiment of the present disclosure.

An autonomous automobile traveling on a roadway, such as vehicle 400 above, may identify objects within its surrounding environment to determine an adjustment to the current speed. The object may be another vehicle, a traffic control device, or another type of object. In some examples, each identified object may be considered independently, and based on the respective characteristics of the object, such as its current speed, acceleration, separation from the vehicle, etc., may be used to determine the speed at which the autonomous vehicle is to be adjusted.

Optionally, the vehicle 400 or a sensory and computing device (e.g., computing system 431, computing platform 450) associated with the vehicle 400 may predict behavior of the identified object based on characteristics of the identified object and the state of the surrounding environment (e.g., traffic, rain, ice on the road, etc.). Optionally, each identified object depends on the behavior of each other, so it is also possible to predict the behavior of a single identified object taking all identified objects together into account. The vehicle 400 is able to adjust its speed based on the predicted behavior of the identified object. In other words, the autonomous vehicle is able to determine what steady state the vehicle will need to adjust to (e.g., 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 vehicle 400, such as the lateral position of the vehicle 400 in the road being traveled, the curvature of the road, the proximity of static and dynamic objects, and so forth.

In addition to providing instructions to adjust the speed of the autonomous vehicle, the computing device may provide instructions to modify the steering angle of the vehicle 400 to cause the autonomous vehicle to follow a given trajectory and/or to maintain a safe lateral and longitudinal distance from objects in the vicinity of the autonomous vehicle (e.g., vehicles in adjacent lanes on the road).

The vehicle 400 may be any type of vehicle, such as a car, a truck, a motorcycle, a bus, a boat, an airplane, a helicopter, a recreational vehicle, a train, etc., and the disclosed embodiment is not particularly limited.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A vehicle lane change decision method is characterized by comprising the following steps:

2. The method of claim 1, further comprising:

when detecting that the lane change pre-action does not occur on the vehicle according to first data in the driving data, determining second data corresponding to the first data as non-lane change sample data;

3. The method of claim 1, further comprising:

acquiring a driving scene of the vehicle;

4. The method of claim 1, wherein the data to be processed comprises a plurality of sets, and the obtaining the data to be processed for deciding whether the host vehicle changes lanes comprises:

the determining whether the vehicle changes lanes according to a preset lane change probability threshold and the lane change probability comprises the following steps:

5. The method of claim 2, wherein the sample data comprises a training set and a test set, the method further comprising:

determining an evaluation result corresponding to each intermediate lane change decision model according to a test result corresponding to each intermediate lane change decision model, wherein the evaluation result comprises evaluation index values of a plurality of performance indexes;

6. The method of claim 1, further comprising:

obtaining a lane change data set, wherein the lane change data set comprises result data of determining lane change and/or lane non-change through the lane change decision model and the preset lane change threshold probability;

and under the condition that the number of the abnormal lane change result data determined in the lane change data set is larger than a preset number threshold, updating the lane change decision model.

7. The method of claim 1, wherein the data to be processed comprises lane type data of the vehicle, lane type data of neighboring vehicles, state data of the vehicle, state data of neighboring vehicles, relationship data between the position of the vehicle and navigation information, and relationship data between the position determined by projecting the vehicle to the lane of neighboring vehicles and the navigation information.

8. A vehicle lane change decision-making device, comprising:

9. A vehicle lane change decision device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to:

10. A computer-readable storage medium, on which computer program instructions are stored, which program instructions, when executed by a processor, carry out the steps of the method according to any one of claims 1 to 7.

11. A vehicle, characterized in that it comprises the device of claim 9.