CN112925309A - Intelligent networking automobile data interaction method and system - Google Patents
Intelligent networking automobile data interaction method and system Download PDFInfo
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Abstract
The invention relates to an intelligent networking automobile data interaction method and an intelligent networking automobile data interaction system. The interaction method is applied to an intelligent networking automobile data interaction system, the interaction system comprises a scene library, a simulation cloud platform and a digital twin platform, and the method comprises the following steps: the scene library acquires perception information and then imports the perception information into the simulation cloud platform; the simulation cloud platform receives the perception information, and sends a simulation result to the digital twin platform after simulation; and the digital twin platform compares the simulation result with the vehicle decision information and optimizes the automatic driving decision of the vehicle according to the comparison result. The interaction method can realize the quick and effective expansion of the scene library, and can learn the driving behavior of the driver, thereby training a higher-level automatic driving system and improving the development efficiency of the automatic driving system; the automatic driving grade of the vehicle can be identified on line, an automatic driving system is monitored in real time, and data support is provided for accident identification; and traffic flow can be monitored in real time, and congestion is reduced.
Description
Technical Field
The invention relates to the field of intelligent networking, in particular to an intelligent networking automobile data interaction method and an intelligent networking automobile data interaction system.
Background
In the automatic driving function development process, vehicle functions need to be verified for a large number of scenes before being approved, and a mileage-based method cannot meet the fully-covered functional verification. A large amount of verification work required for safety verification is performed in advance, and safety of an automatic driving function within an Operational Design Domain (ODD) needs to be ensured in product development and Design stages. Automatic driving safety verification and development methods based on scenes are developed.
At present, automatic driving function development is mostly focused on scene library construction and simulation, a scene library is established through real vehicle collection or manual construction of dangerous scenes, and scene library data is imported into a simulation platform for simulation calculation, so that an automatic driving algorithm and a model are optimized. On the one hand, in the development process of the automatic driving system, the data acquisition vehicle is generally only responsible for acquiring data and cannot return information in real time, so that the efficiency is low. On the other hand, mass production vehicles generally do not have an information return system, and cannot upgrade and optimize an automatic driving system by using massive information generated by the vehicles.
In view of the above, the present invention is particularly proposed.
Disclosure of Invention
The invention aims to provide an intelligent networking automobile data interaction method and an intelligent networking automobile data interaction system, so as to realize the effect of efficiently upgrading and optimizing an automatic driving system.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides an intelligent networking automobile data interaction method, which is applied to an intelligent networking automobile data interaction system, wherein the interaction system comprises a scene library, a simulation cloud platform and a digital twin platform, and the method comprises the following steps:
the method comprises the steps that a scene library obtains perception information, and then the perception information is led into a simulation cloud platform; the perception information is a road scene collected by a vehicle;
the simulation cloud platform receives the perception information, and sends a simulation result to the digital twin platform after simulation;
and the digital twin platform compares the simulation result with vehicle decision information and optimizes the vehicle automatic driving decision according to the comparison result, wherein the vehicle decision information is vehicle automatic driving decision information or vehicle driver decision information.
As a further preferred technical solution, the comparing the simulation result with the vehicle decision information by the digital twin platform, and optimizing the vehicle automatic driving decision according to the comparison result, includes:
respectively calculating a simulation result score and a vehicle decision information score according to vehicle safety, vehicle energy consumption, vehicle comfort, vehicle efficiency, vehicle type and vehicle application;
and determining an optimized automatic driving decision of the vehicle according to the simulation result score and the vehicle decision information score.
As a further preferred technical solution, the digital twin platform further comprises a step of monitoring a control system of the vehicle before, during or after optimizing the vehicle automatic driving decision according to the comparison result.
As a further preferred technical solution, the interactive system further includes a computing platform, where the computing platform acquires traffic flow data of a drive test unit, calculates a ratio of green light durations in each direction at the intersection according to the traffic flow data, and sends a calculation result to the drive test unit, so that the drive test unit adjusts the green light durations in each direction in the traffic signal according to the calculation result.
As a further preferred technical solution, the calculating the ratio of the duration of the green light in each direction at the intersection according to the traffic flow data includes:
and calculating the green light time ratio of each direction of the intersection according to the number of vehicles in the single direction of the traffic flow and the total number of vehicles in each direction of the traffic flow.
In a second aspect, the present invention provides an intelligent networked automobile data interaction method, which is applied to an autonomous vehicle or a partially autonomous vehicle, wherein the autonomous vehicle or the partially autonomous vehicle respectively and independently comprises a perception system and a decision system, and the method comprises the following steps:
the method comprises the steps that a perception system collects road scenes to form perception information and sends the perception information to a scene library;
and the decision system generates vehicle automatic driving decision information according to the perception information, sends the vehicle automatic driving decision information to the digital twin platform, and then receives the optimized vehicle automatic driving decision sent by the digital twin platform.
As a further preferable aspect, the autonomous vehicle or the partially autonomous vehicle further includes a vehicle travel information transmission system that transmits vehicle travel information to the surrounding vehicle and the drive test unit, each independently.
In a third aspect, the present invention provides an intelligent networked automobile data interaction system, including:
the scene library is used for acquiring perception information and then importing the perception information into the simulation cloud platform; the perception information is a road scene collected by a vehicle;
the simulation cloud platform is used for receiving the perception information and sending a simulation result to the digital twin platform after simulation;
and the digital twin platform is used for comparing the simulation result with vehicle decision information and optimizing a vehicle automatic driving decision according to the comparison result, wherein the vehicle decision information is vehicle automatic driving decision information or vehicle driver decision information.
As a further preferred technical solution, the digital twin platform comprises:
the simulation result score and vehicle decision information score calculating module is used for respectively calculating a simulation result score and a vehicle decision information score according to vehicle safety, vehicle energy consumption, vehicle comfort, vehicle efficiency, vehicle type and vehicle application;
and the optimized vehicle automatic driving decision determining module is used for determining an optimized vehicle automatic driving decision according to the simulation result score and the vehicle decision information score.
As a further preferred technical solution, the system further includes a calculation platform, configured to obtain traffic flow data of a drive test unit, calculate a ratio of green light durations in each direction at the intersection according to the traffic flow data, and send a calculation result to the drive test unit, so that the drive test unit adjusts the green light durations in each direction in the traffic signal lamp according to the calculation result.
Compared with the prior art, the invention has the beneficial effects that:
the intelligent networked automobile data interaction method provided by the invention is applied to an intelligent networked automobile data interaction system, the scene library is led into a simulation cloud platform after acquiring the sensing information, then the simulation result is sent to a digital twin platform through the simulation of the simulation cloud platform, the digital twin platform compares the simulation result with the vehicle decision information, and the automatic driving decision of the vehicle is optimized according to the comparison result. The perception information can enrich a scene library, and particularly for the perception information which cannot be identified by the vehicle, the scene library can be manually or automatically labeled and then added into the scene library; the simulation cloud platform can calculate in real time and simulate automatic driving; and the digital twin platform compares the simulation result with vehicle decision information (wherein the vehicle decision information can be vehicle automatic driving decision information, namely decision information sent by a vehicle automatic driving system, or vehicle driver decision information, namely decision information sent when a driver takes over the vehicle in a part of automatic driving vehicles), and optimizes the vehicle automatic driving decision according to the comparison result, thereby realizing upgrading and optimization of the vehicle automatic driving system.
The interaction method can realize the quick and effective expansion of the scene library, and can learn the driving behavior of the driver, thereby training a higher-level automatic driving system and greatly improving the development efficiency of the automatic driving system; the automatic driving grade of the vehicle can be identified on line, an automatic driving system is monitored in real time, and data support is provided for accident identification; the traffic flow can be monitored in real time, the traffic signal lamps can be adjusted through the computing platform, congestion is reduced, and travel efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of an intelligent networked automobile data interaction method provided in embodiment 1 of the present invention;
fig. 2 is a flowchart of an intelligent networked automobile data interaction method provided in embodiment 2 of the present invention;
fig. 3 is a flowchart of an intelligent networked automobile data interaction method provided in embodiment 3 of the present invention;
fig. 4 is an interaction schematic diagram of an intelligent networked automobile data interaction method provided in embodiment 3 of the present invention;
fig. 5 is a schematic data flow diagram of an intelligent networked automobile data interaction method provided in embodiment 3 of the present invention;
FIG. 6 is a schematic view of the direction of the intersection in embodiment 3 of the present invention;
fig. 7 is a flowchart of an intelligent networked automobile data interaction method according to embodiment 4 of the present invention;
fig. 8 is a schematic structural diagram of an intelligent networked automobile data interaction system provided in embodiment 5 of the present invention.
Icon: 301-scene library; 302-a simulated cloud platform; 303-digital twinning platform.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Example 1
Fig. 1 is a flowchart of an intelligent networked automobile data interaction method provided in this embodiment, where this embodiment is applicable to data interaction of an intelligent networked automobile so as to at least improve an automatic driving capability of the automobile, and the method may be executed by an intelligent networked automobile data interaction system, where the system includes a scene library, a simulated cloud platform, and a digital twin platform.
As shown in fig. 1, the method comprises the steps of:
s110, acquiring perception information by a scene library, and then importing the perception information into a simulation cloud platform; the perception information is a road scene collected by a vehicle.
The scene library is a database in which a plurality of road scenes are collected. The scene library integrates data cleaning, data marking and data analysis tools, wherein the data cleaning comprises the functions of denoising, invalid data removing, data co-frequency and the like.
The simulation cloud platform is a platform for simulating automatic driving.
The road scene is an organic combination of a vehicle driving occasion and a driving scene, and comprises road environment elements, other traffic participants and vehicle driving tasks, and meanwhile, the elements can last for a certain time and have the characteristic of dynamic change.
The perception information may be collected by a perception system on the vehicle, for example, including a camera, a lidar, a millimeter wave radar, or the like.
And S120, receiving the perception information by the simulation cloud platform, and sending a simulation result to the digital twin platform after simulation.
The digital twin platform is a platform for dynamically realizing data interaction by applying a digital twin technology.
S130, the digital twin platform compares the simulation result with vehicle decision information, and optimizes a vehicle automatic driving decision according to the comparison result, wherein the vehicle decision information is vehicle automatic driving decision information or vehicle driver decision information.
The automatic driving decision information or vehicle driver decision information is information including route planning. The automatic vehicle driving decision information is decision information obtained by self-operation of the vehicle; the vehicle driver decision information refers to decision information sent by a driver, and the decision information can be sent by a decision system of a vehicle.
Specifically, the comparing the simulation result with the vehicle decision information by the digital twin platform, and optimizing the vehicle automatic driving decision according to the comparison result, includes:
respectively inputting the simulation result and the vehicle decision information into a preset calculation model, wherein the calculation model is used for calculating influence factors of the simulation result or the vehicle decision information on vehicle driving; and optimizing the automatic driving decision of the vehicle according to the influence factors obtained by calculation.
Optionally, if the difference between the influence factor of the simulation result and the influence factor of the vehicle decision information is within a preset difference range, taking the vehicle decision information as an optimized vehicle automatic driving decision; and if the difference value between the influence factor of the simulation result and the influence factor of the vehicle decision information is not within the preset difference value range, taking the simulation result or the vehicle decision information as the optimized vehicle automatic driving decision.
The intelligent networked automobile data interaction method is applied to an intelligent networked automobile data interaction system, the scene library is led into a simulation cloud platform after acquiring the sensing information, then the simulation result is sent to a digital twin platform through simulation of the simulation cloud platform, the digital twin platform compares the simulation result with the vehicle decision information, and the automatic driving decision of the vehicle is optimized according to the comparison result. The perception information can enrich a scene library, and particularly for the perception information which cannot be identified by the vehicle, the scene library can be manually or automatically labeled and then added into the scene library; the simulation cloud platform can calculate in real time and simulate automatic driving; and the digital twin platform compares the simulation result with vehicle decision information (wherein the vehicle decision information can be vehicle automatic driving decision information, namely decision information sent by a vehicle automatic driving system, or vehicle driver decision information, namely decision information sent when a driver takes over the vehicle in a part of automatic driving vehicles), and optimizes the vehicle automatic driving decision according to the comparison result, thereby realizing upgrading and optimization of the vehicle automatic driving system.
The interaction method can realize the quick and effective expansion of the scene library, and can learn the driving behavior of the driver, thereby training a higher-level automatic driving system and greatly improving the development efficiency of the automatic driving system; the automatic driving grade of the vehicle can be identified on line, an automatic driving system is monitored in real time, and data support is provided for accident identification; the traffic flow can be monitored in real time, the traffic signal lamps can be adjusted through the computing platform, congestion is reduced, and travel efficiency is improved.
Further, the digital twin platform further comprises a step of monitoring a control system of the vehicle before, during or after optimizing the automatic driving decision of the vehicle according to the comparison result. The digital twin platform can also monitor the control system of the vehicle in real time before optimizing the automatic driving decision of the vehicle according to the comparison result, or while optimizing the automatic driving decision of the vehicle according to the comparison result, or after optimizing the automatic driving decision of the vehicle according to the comparison result, so as to provide data support for accident identification when an accident occurs.
Example 2
Fig. 2 is a flowchart of an intelligent networking automobile data interaction method provided in this embodiment, where S130 in embodiment 1 is further optimized in this embodiment, referring to fig. 2, the method includes the following steps:
s110, acquiring perception information by a scene library, and then importing the perception information into a simulation cloud platform; the perception information is a road scene collected by a vehicle.
And S120, receiving the perception information by the simulation cloud platform, and sending a simulation result to the digital twin platform after simulation.
S110 and S120 are the same as those in embodiment 1, and are not described again in this embodiment.
S131, respectively calculating a simulation result score and a vehicle decision information score according to vehicle safety, vehicle energy consumption, vehicle comfort, vehicle efficiency, vehicle type and vehicle application.
The vehicle safety refers to the performance of avoiding accidents during the running of the vehicle and guaranteeing the safety of pedestrians and passengers, and is generally divided into active safety, passive safety, post-accident safety and ecological safety. Adverse factors to vehicle safety include curve overtaking, right overtaking, violation, accident rate, triggering of emergency video recording, and the like.
The vehicle energy consumption refers to the fuel consumption of a vehicle per hundred kilometers and/or the power consumption of the vehicle per hundred kilometers.
Vehicle comfort refers to the intuitive feel that a vehicle gives to passengers when traveling under special conditions, such as rapid acceleration, rapid deceleration, and high-speed cornering.
Vehicle efficiency refers to the efficiency with which the vehicle engine or vehicle motor operates.
The vehicular use means a use purpose of the vehicle, such as carrying passengers, carrying cargo, competition, fire fighting, medical aid, and the like.
The simulation result score is a score obtained by scoring the simulation result.
The vehicle decision information score means a score obtained by scoring the vehicle decision information, which is the same as in embodiment 1.
Preferably, the step of calculating the simulation result score and the vehicle decision information score respectively according to the vehicle safety, the vehicle energy consumption, the vehicle comfort, the vehicle efficiency, the vehicle type and the vehicle usage comprises the following steps:
respectively determining the weights of vehicle safety, vehicle energy consumption, vehicle comfort and vehicle efficiency according to the vehicle type and the vehicle application;
and calculating the simulation result score according to the vehicle safety, the vehicle energy consumption, the vehicle comfort, the vehicle efficiency and the weight of the simulation result.
The score may be calculated using the following formula: s(simulation results)K1 × S1+ k2 × S2+ k3 × S3+ k4 × S4, k1, k2, k3, and k4 are weights for vehicle safety, vehicle energy consumption, vehicle comfort, and vehicle efficiency, respectively; s1 is the vehicle safety score of the simulation result, S2 is the vehicle energy consumption score of the simulation result, S3 is the vehicle comfort score of the simulation result, and S4 is the vehicle efficiency score of the simulation result.
And calculating a vehicle decision information score according to the vehicle safety, the vehicle energy consumption, the vehicle comfort, the vehicle efficiency and the weight of the vehicle decision information.
The score may be calculated using the following formula: s(vehicle decision information)K1 × S5+ k2 × S6+ k3 × S7+ k4 × S8, k1, k2, k3, and k4 are weights for vehicle safety, vehicle energy consumption, vehicle comfort, and vehicle efficiency, respectively; s5 is a vehicle safety score of the vehicle decision information, S6 is a vehicle energy consumption score of the vehicle decision information, S7 is a vehicle comfort score of the vehicle decision information, and S8 is a vehicle efficiency score of the vehicle decision information.
And S132, determining an optimized automatic driving decision of the vehicle according to the simulation result score and the vehicle decision information score.
Specifically, determining an optimized vehicle automatic driving decision according to the simulation result score and the vehicle decision information score comprises the following steps: and comparing the simulation result score with the vehicle decision information score, and if the simulation result score is smaller than the vehicle decision information score, determining the optimized vehicle automatic driving decision as the vehicle decision information. For example, when the vehicle decision is made by the driver, if the simulation result score is less than the vehicle decision information score, the decision made by the driver is taken as an optimized vehicle automatic driving decision, so that the automatic driving function can be trained to learn the driver skill, the automatic driving strategy is optimized, and the automatic driving strategy is made to approach the driver decision.
In this embodiment, S130 is further optimized based on embodiment 1, and a scientifically and reasonably optimized vehicle automatic driving decision is obtained by calculating and comparing the simulation result score and the vehicle decision information score.
Example 3
Fig. 3 is a flowchart of an intelligent internet automobile data interaction method provided in this embodiment, which is a further optimization of embodiment 2 in this embodiment, where the interaction system further includes a computing platform, fig. 4 is a schematic diagram of interaction of the intelligent internet automobile data interaction system, an autonomous vehicle or a part of an autonomous vehicle, and a road test system in the interaction method of this embodiment, and fig. 5 is a schematic diagram of data flow between the intelligent internet automobile data interaction system, and an autonomous vehicle or a part of an autonomous vehicle in the interaction method of this embodiment.
Referring to fig. 3, the method comprises the steps of:
s110, acquiring perception information by a scene library, and then importing the perception information into a simulation cloud platform; the perception information is a road scene collected by a vehicle.
And S120, receiving the perception information by the simulation cloud platform, and sending a simulation result to the digital twin platform after simulation.
S131, respectively calculating a simulation result score and a vehicle decision information score according to vehicle safety, vehicle energy consumption, vehicle comfort, vehicle efficiency, vehicle type and vehicle application.
And S132, determining an optimized automatic driving decision of the vehicle according to the simulation result score and the vehicle decision information score.
S110, S120, S131, and S132 are the same as those in embodiment 2, and are not described again in this embodiment.
S140, the calculation platform acquires traffic flow data of a road test unit, calculates the green time occupation ratio of each direction of the intersection according to the traffic flow data, and sends the calculation result to the road test unit so that the road test unit can adjust the green time of each direction in the traffic signal lamp according to the calculation result.
The road test unit is a device which is installed on two sides of a road, is used for communicating with an automatic driving vehicle and realizing vehicle identity recognition, can acquire a traffic flow in real time and comprises traffic signal lamps.
The traffic flow means the number of vehicles in a certain area over a period of time.
The ratio of the green time length of each direction at the intersection is the ratio of the green time of each traffic signal lamp at the intersection to the green time of all traffic signal lamps.
Note that, this step S140 may be performed before performing S110 or while performing S110.
Preferably, the calculating the ratio of the green light time duration in each direction of the intersection according to the traffic flow data includes:
and calculating the green light time ratio of each direction of the intersection according to the number of vehicles in the single direction of the traffic flow and the total number of vehicles in each direction of the traffic flow.
Optionally, the ratio of the green light duration in each direction of the intersection is calculated by using the following formula:
p _ si is N _ sn/(N _ s1+ N _ s2+ … + N _ sn), where P _ sn is the ratio of the green time in a certain direction of the intersection, N _ si is the number of vehicles in a certain direction of the intersection, i is greater than or equal to 1 and less than or equal to N, and N _ s1, N _ s2, …, and N _ sn are the number of vehicles in each direction of the intersection, respectively.
As shown in fig. 6, the intersection can be divided into 8 directions of S1, S2, S3, S4, S5, S6, S7 and S8, and if the ratio of the green time duration in the direction of S1 is calculated, the following formula can be used: p _ s1 ═ N _ s1/(N _ s1+ N _ s2+ … + N _ s 8).
According to the embodiment, the traffic flow data of the road test unit is obtained, and the duty ratio of the green light time in each direction is calculated in a certain mode, so that the green light time in each direction of the intersection can be effectively allocated according to the traffic flow condition, the traffic jam is avoided, and the travel efficiency is improved.
It should be noted that, for the communication among the intelligent networked automobile data interaction system, the autonomous Vehicle or a part of the autonomous Vehicle, and the drive test system, V2X-DSRC (Vehicle to event-detected Short Range Communications, Vehicle to Vehicle-specific Short-distance wireless communication), C-V2X (Cellular Vehicle to event), 4G, 5G, or other wireless networks may be used.
Example 4
Fig. 7 is a flowchart of a data interaction method for an intelligent networked automobile according to an embodiment, where the embodiment is applied to data interaction of the intelligent networked automobile so as to at least improve the automatic driving capability of the automobile, and the method may be performed by an automatic driving vehicle or a partially automatic driving vehicle, where the automatic driving vehicle or the partially automatic driving vehicle respectively and independently includes a sensing system and a decision-making system.
The automatic driving vehicle is also called as an unmanned vehicle, and refers to an intelligent vehicle which realizes unmanned driving through a computer system. Some autonomous vehicles are also called semi-autonomous vehicles, which refer to vehicles that need to be driven by people and have certain autonomous driving functions.
As shown in fig. 7, the method comprises the steps of:
s210, a perception system collects road scenes to form perception information and sends the perception information to a scene library.
The perception information formed by the perception system can be recognized by a vehicle and also comprises a part of unrecognizable information, the information can be sent to the scene library, the scene library directly guides the information into the simulation cloud platform for simulation for the information which can be recognized by the vehicle, and the scene library manually or automatically labels the information which cannot be recognized, expands the information into the scene library and guides the information into the simulation cloud platform for simulation.
And S220, generating vehicle automatic driving decision information by the decision system according to the perception information, sending the vehicle automatic driving decision information to the digital twin platform, and receiving an optimized vehicle automatic driving decision sent by the digital twin platform.
The decision system can generate vehicle automatic driving decision information according to the perception information, send the decision information to the digital twin platform, and receive the optimized vehicle automatic driving decision sent by the digital twin platform after the comparison of the digital twin platform.
The vehicle automatic driving decision information can be directly generated by the decision system according to the perception information, or can be the decision information fed back to the decision system after the intervention operation of the driver according to the perception information.
The intelligent networked automobile data interaction method is applied to automatically driven vehicles or partially automatically driven vehicles, the vehicles can automatically send perception information to a scene library after collecting road scenes and forming the perception information, the method is fast and efficient, a decision system can generate vehicle automatic driving decision information, driving decisions of the vehicles are optimized according to the optimized vehicle automatic driving decisions sent by a digital twin platform, and automatic driving is developed towards more intelligent and complete direction through continuous technical iteration.
Further, the autonomous vehicle or the partially autonomous vehicle further includes a vehicle travel information transmission system that transmits vehicle travel information to surrounding vehicles and the drive test unit, each independently. The step can realize the communication between the vehicles and the drive test unit and the communication between different vehicles, thereby being convenient for adjusting the duration of green lights in each direction, being convenient for the surrounding vehicles to know the running state of the vehicle and being convenient for the surrounding vehicles to make a response strategy in time.
The vehicle travel information refers to information related to vehicle travel, and includes a vehicle position, vehicle acceleration, vehicle deceleration, vehicle turning, vehicle lane change, and the like.
Example 5
Fig. 8 is a schematic structural diagram of an intelligent networking automobile data interaction system provided in this embodiment, including:
the scene library 301 is used for acquiring perception information and then importing the perception information into the simulation cloud platform; the perception information is a road scene collected by a vehicle;
the simulation cloud platform 302 is used for receiving the perception information, and after simulation, sending a simulation result to the digital twin platform;
and a digital twin platform 303, configured to compare the simulation result with vehicle decision information, and optimize a vehicle automatic driving decision according to the comparison result, where the vehicle decision information is vehicle automatic driving decision information or vehicle driver decision information.
Further, the digital twin platform 303 includes:
the simulation result score and vehicle decision information score calculating module is used for respectively calculating a simulation result score and a vehicle decision information score according to vehicle safety, vehicle energy consumption, vehicle comfort, vehicle efficiency, vehicle type and vehicle application;
and the optimized vehicle automatic driving decision determining module is used for determining an optimized vehicle automatic driving decision according to the simulation result score and the vehicle decision information score.
Further, the system further includes a computing platform (not shown) configured to obtain traffic flow data of a drive test unit, calculate a ratio of green time durations in each direction at the intersection according to the traffic flow data, and send a calculation result to the drive test unit, so that the drive test unit adjusts the green time durations in each direction of the traffic signal lamp according to the calculation result.
The intelligent networking automobile data interaction system is used for executing the intelligent networking automobile data interaction method of the embodiment, and has functional modules corresponding to the method and beneficial effects.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present invention is not limited herein.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. An intelligent networking automobile data interaction method is applied to an intelligent networking automobile data interaction system, the interaction system comprises a scene library, a simulation cloud platform and a digital twin platform, and the method comprises the following steps:
the method comprises the steps that a scene library obtains perception information, and then the perception information is led into a simulation cloud platform; the perception information is a road scene collected by a vehicle;
the simulation cloud platform receives the perception information, and sends a simulation result to the digital twin platform after simulation;
and the digital twin platform compares the simulation result with vehicle decision information and optimizes the vehicle automatic driving decision according to the comparison result, wherein the vehicle decision information is vehicle automatic driving decision information or vehicle driver decision information.
2. The intelligent networked automobile data interaction method according to claim 1, wherein the digital twin platform compares the simulation result with vehicle decision information and optimizes a vehicle automatic driving decision according to the comparison result, and the method comprises the following steps:
respectively calculating a simulation result score and a vehicle decision information score according to vehicle safety, vehicle energy consumption, vehicle comfort, vehicle efficiency, vehicle type and vehicle application;
and determining an optimized automatic driving decision of the vehicle according to the simulation result score and the vehicle decision information score.
3. The intelligent networked automobile data interaction method according to claim 1, wherein the digital twin platform further comprises a step of monitoring a control system of the vehicle before, during or after optimizing the automatic driving decision of the vehicle according to the comparison result.
4. The intelligent networked automobile data interaction method according to any one of claims 1 to 3, wherein the interaction system further comprises a computing platform, the computing platform acquires traffic flow data of a road test unit, calculates the proportion of the duration of green lights in each direction of the intersection according to the traffic flow data, and sends the calculation result to the road test unit so that the road test unit can adjust the duration of green lights in each direction of a traffic signal lamp according to the calculation result.
5. The intelligent networked automobile data interaction method according to claim 4, wherein the calculating of the proportion of the duration of the green light in each direction at the intersection according to the traffic flow data comprises:
and calculating the green light time ratio of each direction of the intersection according to the number of vehicles in the single direction of the traffic flow and the total number of vehicles in each direction of the traffic flow.
6. An intelligent networked automobile data interaction method is applied to an automatic driving vehicle or a partially automatic driving vehicle, wherein the automatic driving vehicle or the partially automatic driving vehicle respectively and independently comprises a perception system and a decision-making system, and the method comprises the following steps:
the method comprises the steps that a perception system collects road scenes to form perception information and sends the perception information to a scene library;
and the decision system generates vehicle automatic driving decision information according to the perception information, sends the vehicle automatic driving decision information to the digital twin platform, and then receives the optimized vehicle automatic driving decision sent by the digital twin platform.
7. The intelligent networked automobile data interaction method according to claim 6, wherein the autonomous vehicles or the partially autonomous vehicles further comprise vehicle driving information sending systems independently, and the vehicle driving information sending systems send vehicle driving information to surrounding vehicles and a drive test unit.
8. The utility model provides an intelligence networking car data interaction system which characterized in that includes:
the scene library is used for acquiring perception information and then importing the perception information into the simulation cloud platform; the perception information is a road scene collected by a vehicle;
the simulation cloud platform is used for receiving the perception information and sending a simulation result to the digital twin platform after simulation;
and the digital twin platform is used for comparing the simulation result with vehicle decision information and optimizing a vehicle automatic driving decision according to the comparison result, wherein the vehicle decision information is vehicle automatic driving decision information or vehicle driver decision information.
9. The intelligent networked automobile data interaction system of claim 8, wherein the digital twin platform comprises:
the simulation result score and vehicle decision information score calculating module is used for respectively calculating a simulation result score and a vehicle decision information score according to vehicle safety, vehicle energy consumption, vehicle comfort, vehicle efficiency, vehicle type and vehicle application;
and the optimized vehicle automatic driving decision determining module is used for determining an optimized vehicle automatic driving decision according to the simulation result score and the vehicle decision information score.
10. The intelligent networked automobile data interaction system according to claim 8 or 9, wherein the system further comprises a computing platform, which is used for acquiring traffic flow data of a drive test unit, computing the proportion of the duration of green lights in each direction of the intersection according to the traffic flow data, and sending the computed result to the drive test unit, so that the drive test unit can adjust the duration of green lights in each direction of a traffic signal lamp according to the computed result.
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