Automatic driving vehicle high-speed cruise control method and electronic equipment

Abstract

The invention discloses an automatic driving vehicle high-speed cruise control method and electronic equipment, and the method comprises the steps: firstly obtaining real-time vehicle state information and surrounding environment information through a sensor and vehicle networking communication equipment equipped on an automatic driving vehicle; secondly, establishing a vehicle dynamics model; then establishing a vehicle lane keeping or lane changing layered control strategy, and determining an action needing to be executed by the vehicle according to surrounding traffic information; a reference path for the vehicle to execute the lane changing operation is constructed based on a polynomial method, and the lane changing operation is executed while the vehicle keeps a safe distance; and finally, a deep reinforcement learning method is adopted, the optimal cruise path is determined by learning the lane changing behavior of a skilled driver in combination with the reference path, and lane changing and cruise control are completed based on a vehicle motion control module. The cruise control stability and safety of the autonomous vehicle during high-speed driving are improved, and autonomous cruise control of the vehicle is achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent vehicle driving, and in particular to a high-speed cruise control method and electronic equipment for an autonomous driving vehicle. Background Art

[0002] With the development of automobile electrification and intelligent technology, autonomous driving, as the most important application of automobile intelligence, is the focus of the development of smart cars. Vehicle cruise control technology can effectively reduce the burden on drivers, relieve driver fatigue, improve traffic conditions and reduce environmental pollution. However, complex traffic scenarios greatly challenge the safety and comfort of autonomous driving vehicles. At present, vehicle cruise control technologies include proportional-integral-differential (PID) control algorithms, adaptive control algorithms, etc., which are mainly aimed at cruise control during longitudinal driving of vehicles. However, these two control algorithms often rely on accurate models and have the disadvantage of low cruise control accuracy in complex environments.

[0003] The cruise control technology of autonomous vehicles on highways mainly focuses on three aspects: (1) vehicle behavior decision-making; (2) vehicle path planning; and (3) vehicle motion control. Therefore, the technical research on the above three aspects is the focus of autonomous vehicles' high-speed cruise control, and also provides a theoretical basis and technical support for the research on cruise control of future connected fleets. However, how to ensure the economy of vehicle cruise control, as well as the safety and comfort of the vehicle during lane change, is still extremely challenging. Summary of the invention

[0004] The present invention proposes a high-speed cruise control method and electronic equipment for autonomous vehicles, which can realize the economical and safe driving of unmanned vehicles on highways. It is proposed to learn the cruise control and vehicle lane change strategies from experienced drivers through deep reinforcement learning algorithms, guide the autonomous vehicle to make effective decisions on high-speed cruise strategies, and maintain the stability of the vehicle in high-speed cruise scenarios.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions: A method for high-speed cruise control of an autonomous driving vehicle is provided, comprising the following steps: Step S1: Acquire the real-time status information and road environment information of the autonomous driving vehicle, and accurately locate the autonomous driving vehicle in real time in combination with the positioning system; Step S2: uploading the real-time status information of the autonomous driving vehicle and the road environment information obtained in step S1 to the vehicle control unit; Step S3: establishing a vehicle dynamics model, which can characterize the longitudinal, lateral and yaw characteristics of the vehicle; The details are as follows: The vehicle dynamics model is established as: (Formula 1) In the formula, the state vector , , , , , , The control vectors respectively represent the longitudinal velocity of the vehicle, the lateral velocity of the vehicle, the yaw angle of the vehicle, the yaw rate of the vehicle, the ordinate of the vehicle geodetic coordinate system, and the abscissa of the vehicle geodetic coordinate system. , and They represent the vehicle longitudinal acceleration and the front wheel steering angle respectively; C is the state parameter matrix, D is the control parameter matrix; Step S4: establishing a cruise control decision module for the autonomous driving vehicle, formulating a hierarchical control strategy for lane keeping or lane changing of the autonomous driving vehicle by judging whether there is a followable vehicle in front of the autonomous driving vehicle, the speed of the followable vehicle, and the relative distance between the autonomous driving vehicle and the followable vehicle; Step S5: Establish an autonomous driving vehicle motion control method, formulate a vehicle motion control strategy for the vehicle cruise control decision module established in step S4, design a vehicle optimal path planning method, determine the optimal reference path, and perform vehicle motion control to achieve autonomous cruising of the vehicle.

[0006] Further, in step S1, the real-time status information of the vehicle includes the speed, acceleration and position of the autonomous driving vehicle; the road environment information includes road width, road slope, road speed limit, traffic environment information, and the position and speed information of surrounding vehicles.

[0007] Furthermore, in step S2, the real-time information of the autonomous driving vehicle and the road environment information are uploaded to the control unit. To improve information storage and processing efficiency, only data within a 200-meter communication range of the autonomous driving vehicle and the surrounding environment (including road information, other vehicle status information, speed limit information, etc.) are uploaded.

[0008] Further, the step S4 of establishing the autonomous driving vehicle cruise control decision module mainly includes the following steps: First, determine whether there is a vehicle that can be followed in front; If there is no following vehicle, the vehicle will cruise at a preset economic speed according to the road speed limit. If there is a following vehicle, the need to maintain the lane and cruise behind the vehicle in front or to change lanes will be determined based on the relative speed and relative distance between the vehicle in front and the autonomous driving vehicle.

[0009] The detailed steps are as follows: Step S41: based on the acquired real-time status information of the autonomous driving vehicle and the road environment information, detecting whether there is a vehicle that can be followed in front of the autonomous driving vehicle, and if there is no vehicle that can be followed in front, performing lane keeping and cruising at an economic speed designed for the autonomous driving vehicle; Step S42: Based on the acquired real-time status information and road environment information of the autonomous driving vehicle, detect whether there is a following vehicle in front of the autonomous driving vehicle. If there is a following vehicle in front, first perform lane keeping, cruise at the economic speed designed for the autonomous driving vehicle, and then determine whether a lane change is required by measuring the relative speed of the vehicle in front and the autonomous driving vehicle, and the relative distance between the vehicle in front and the autonomous driving vehicle. Step S43: Based on the acquired real-time status information of the autonomous driving vehicle and the road environment information, if the speed of the following vehicle is lower than the economic cruising speed set by the autonomous driving vehicle, determine whether there is a lane change condition based on the acquired real-time status information of the autonomous driving vehicle and the surrounding environment information; if so, perform a lane change operation; if not, exit the cruise control and decelerate to avoid collision; Step S44: if the distance between the autonomous driving vehicle and the following vehicle is lower than the set safety threshold, determine whether there is a lane change condition based on the real-time autonomous driving vehicle status information and surrounding environment information; if so, perform a lane change operation; if not, exit cruise control and slow down to avoid collision; Further, the cruise mode switching strategy proposed in step S4 is as follows: Step S411: Set the economic cruising speed to , the speed of the vehicle ahead is , in km / h. If there is no following vehicle in front, the vehicle will maintain the lane and cruise at a constant speed. The designed economic speed here is 100 km / h. Step S412: If there is a vehicle ahead that can be followed, Then lane keeping, cruise control; Step S413: If there is a vehicle ahead that can be followed, , then determine whether the distance between the two vehicles exceeds the set safety threshold. If not, then keep the lane; , the distance between the two vehicles will continue to decrease. If the distance between the two vehicles exceeds the set safety threshold, the real-time information of the autonomous driving vehicle and the road environment information obtained will be used to determine whether there is a lane change condition. If not, the relative distance between the two vehicles will be used as the control target, and the vehicle will cruise at a constant speed; Step S414: Based on step S413, if there are lane changing conditions, the automatic driving vehicle is controlled to perform lane changing operations.

[0010] Furthermore, the automatic driving vehicle motion control method proposed in step S5 comprises the following steps: Step S51: According to the characteristic that the stability of lane changing behavior is crucial when the autonomous driving vehicle is cruising at high speed, a method based on inverse reinforcement learning is proposed to learn the steering behavior of experienced drivers to improve the feasibility of the vehicle's planned path and the stability of the vehicle; Step S52: Based on the driving characteristics of the vehicle in the high-speed lane, a candidate path expressed in a polynomial is constructed as follows: (Formula 2) In the formula, is the vehicle lateral reference position, X is the longitudinal coordinate of the vehicle, , , , , and are the polynomial coefficients, The initial position of the vehicle is defined as , the lateral distance and longitudinal distance involved in the lane change process are respectively L and M Indicates that if =0, =0, then the terminal position The following relationship exists: (Formula 3) In the formula, L is the lane width, M is the longitudinal distance of lane change. During the lane change process, the longitudinal speed of the vehicle remains unchanged. The lane change time is , the longitudinal speed of the vehicle is ,but: (Formula 4) Step S53: Use the reverse deep learning algorithm to evaluate the overall performance of the vehicle during the lane change process, including vehicle safety and driving comfort. Vehicle safety is quantified by vehicle stability and potential collision risk.

[0011] Further, the vehicle stability state quantitative index in step S53 is: (Formula 5) In the formula, is a stability indicator, is the vehicle lateral velocity, is the vehicle longitudinal velocity.

[0012] Furthermore, the quantitative indicators of vehicle potential collision risk are: (Formula 6) In the formula, is the collision risk indicator, and is the weight factor, and are the lateral and longitudinal coordinates of the autonomous vehicle, and are the lateral and longitudinal coordinates of the collision risk vehicle.

[0013] Furthermore, the following vector is constructed to define the comprehensive performance index of vehicle control: (Formula 7) (Formula 8) In the formula, As a comprehensive performance indicator, is a stability indicator, is the collision risk indicator, is the total number of sample points collected for each candidate path, n is the total number of vehicles at risk of collision.

[0014] Formula 7 is normalized, and the normalized function is expressed as: (Formula 9) The reward function in the inverse deep learning algorithm is expressed as: (Formula 10) In the formula, is the reward function, is the weight coefficient, , set to a constant.

[0015] Furthermore, the method further comprises step S54: optimizing the weight coefficient in step S53 by taking the driving behavior of experienced drivers in lane change as expert experience. and , trained in an inverse deep learning algorithm so that the driving vehicle exhibits lane-changing behavior similar to that of an experienced driver.

[0016] Furthermore, an electronic device for an autonomous driving vehicle is provided for implementing the above-mentioned method for high-speed cruise control of an autonomous driving vehicle, comprising a memory and a navigator; the memory stores computer programs, and the software programs in the memory can be run on the central processing unit of the vehicle; the navigator is used to determine the current position of the vehicle, the vehicle speed and the road speed limit.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention proposes a method and electronic device for high-speed cruise control of an autonomous driving vehicle, and proposes an overall framework for realizing autonomous cruise control of an autonomous driving vehicle on a highway. The framework inherits the decision-making, path planning and motion control modules, and the proposed framework can ensure the performance of the high-speed cruise vehicle.

[0018] 2. The present invention proposes a vehicle lane changing and motion control strategy based on a deep reinforcement learning algorithm. The strategy is to learn the ability of vehicle path planning and vehicle control from skilled drivers. This method takes into account the driving safety and economy of the vehicle, and ensures smooth and safe lane changing behavior of the vehicle when performing lane changing.

[0019] 3. The technical solution of the present invention solves the technical problems of low control accuracy and poor stability of cruise control in related technologies, enhances the adaptability of vehicle cruise control to complex working conditions, and provides a technical solution for studying the application of collaborative cruise control between multiple vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a framework diagram of a high-speed cruise control method for an autonomous vehicle; Figure 2 is the vehicle dynamics model diagram; Figure 3 This is a diagram of the training framework for skilled drivers’ lane-changing behavior based on deep reinforcement learning. DETAILED DESCRIPTION

[0021] The specific implementation modes of the present invention are described below in conjunction with the accompanying drawings so that those skilled in the art can better understand the present invention.

[0022] It should be noted that: Figure 1 The figure shows a framework diagram of the high-speed cruise control method for an autonomous driving vehicle proposed in the present invention, which includes an environmental information acquisition module, a vehicle lane change decision module, a path planning module and a motion control module.

[0023] The environmental information acquisition module mainly includes the real-time status information of the autonomous driving vehicle and the road environment information. The real-time status information of the autonomous driving vehicle specifically includes: vehicle speed, vehicle acceleration and vehicle position information; the road environment information specifically includes: road width, road slope, road section speed limit and traffic environment information.

[0024] The vehicle lane changing decision module includes two aspects: lane keeping and vehicle lane changing.

[0025] The path planning module includes three aspects: reference path, actual path and path tracker design.

[0026] The vehicle motion control module refers to the development of a path tracker to ensure the feasibility and stability of the vehicle traveling according to the planned path.

[0027] Figure 2 Shown is the vehicle dynamics model, which uses a general bicycle model to describe the vehicle's dynamic characteristics.

[0028] The present application proposes a cruise control method for an autonomous driving vehicle, comprising the following steps: Step S1: Acquire the real-time status information and road environment information of the autonomous driving vehicle, and accurately locate the autonomous driving vehicle in real time in combination with the positioning system; the real-time status information of the vehicle includes the speed, acceleration and position of the autonomous driving vehicle; the road environment information includes road width, road slope, road speed limit, traffic environment information, and the position and speed information of surrounding vehicles.

[0029] Step S2: Upload the real-time status information and road environment information of the autonomous driving vehicle obtained in step S1 to the on-board control unit; the real-time information and road environment information of the autonomous driving vehicle are uploaded to the control unit. In order to improve information storage and processing efficiency, only data within a 200-meter communication range of the autonomous driving vehicle and the surrounding environment (including road information, other vehicle status information, speed limit information, etc.) are uploaded.

[0030] Step S3: establishing a vehicle dynamics model, which can characterize the longitudinal, lateral and yaw characteristics of the vehicle; Step S4: Establishing an autonomous driving vehicle cruise control decision module; Step S5: Establish an autonomous driving vehicle motion control method; formulate a vehicle motion control strategy for the vehicle cruise control decision module established in step S4, design a vehicle optimal path planning method, determine the optimal reference path, and perform vehicle motion control to achieve autonomous cruising of the vehicle.

[0031] In step S3, the vehicle dynamics model is established as follows: (Formula 1) In the formula, the state vector , , , , , , The control vectors respectively represent the longitudinal velocity of the vehicle, the lateral velocity of the vehicle, the yaw angle of the vehicle, the yaw rate of the vehicle, the ordinate of the vehicle geodetic coordinate system, and the abscissa of the vehicle geodetic coordinate system. , and They represent the vehicle longitudinal acceleration and the front wheel steering angle respectively; C is the state parameter matrix, D is the control parameter matrix.

[0032] In step S4: establishing an autonomous driving vehicle cruise control decision module mainly includes the following steps: The detailed steps are as follows: Step S41: Detect whether there is a following vehicle in front of the autonomous driving vehicle based on the acquired real-time status information of the autonomous driving vehicle and the road environment information. If there is no following vehicle in front, perform lane keeping and cruise at an economic speed designed for the autonomous driving vehicle. Step S42: Based on the acquired real-time status information and road environment information of the autonomous driving vehicle, detect whether there is a following vehicle in front of the autonomous driving vehicle. If there is a following vehicle in front, first perform lane keeping, cruise at the economic speed designed for the autonomous driving vehicle, and then determine whether a lane change is required by comparing the relative speed of the vehicle in front and the autonomous driving vehicle, and the relative distance between the vehicle in front and the autonomous driving vehicle. Step S43: Based on the obtained real-time status information of the autonomous driving vehicle and the road environment information, if the speed of the followable vehicle is lower than the economic cruising speed set by the autonomous driving vehicle, determine whether there is a lane change condition based on the obtained real-time status information of the autonomous driving vehicle and the surrounding environment information; if so, perform a lane change operation; if not, exit the cruise control and slow down to avoid collision; Step S44: If the distance between the autonomous driving vehicle and the following vehicle is lower than the set safety threshold, determine whether there is a lane change condition based on the real-time autonomous driving vehicle status information and surrounding environment information; if so, perform a lane change operation; if not, exit the cruise control and slow down to avoid collision; The cruise mode switching strategy proposed in step S4 is as follows: Step S411: Set the economic cruising speed to , the speed of the vehicle ahead is , in km / h. If there is no following vehicle in front, the vehicle will maintain the lane and cruise at a constant speed. The designed economic cruise speed here is 100 km / h. Step S412: If there is a vehicle ahead that can be followed, , then lane keeping and cruise control; Step S413: If there is a vehicle ahead that can be followed, , then determine whether the distance between the two vehicles exceeds the set safety threshold. If not, then keep the lane; , the distance between the two vehicles will continue to decrease. If the distance between the two vehicles exceeds the set safety threshold, the real-time autonomous driving vehicle information and road environment information obtained will be used to determine whether there is a lane change condition. If not, the relative distance between the two vehicles will be used as the control target, and the vehicle will cruise; Step S414: Based on step S413, if there are lane changing conditions, the automatic driving vehicle is controlled to perform lane changing operations.

[0033] The automatic driving vehicle motion control method proposed in step S5 comprises the following steps: Step S51: Based on the fact that the stability of lane changing behavior is crucial when the autonomous driving vehicle is cruising at high speed, a method based on inverse reinforcement learning is proposed to learn the steering behavior of experienced drivers to improve the feasibility of the vehicle's planned path and the stability of the vehicle.

[0034] Step S52: Based on the driving characteristics of the vehicle in the high-speed lane, a candidate path expressed in a polynomial is constructed as follows: (Formula 2) In the formula, is the vehicle lateral reference position, X is the longitudinal coordinate of the vehicle, , , , , and are the polynomial coefficients, The initial position of the vehicle is defined as , the lateral distance and longitudinal distance involved in the lane change process are respectively L and M Indicates that if =0, =0, then the terminal position The following relationship exists: (Formula 3) In the formula, L is the lane width, M is the longitudinal distance of lane change. During the lane change process, the longitudinal speed of the vehicle remains unchanged. The lane change time is , the longitudinal velocity of the vehicle is ,but: (Formula 4) Step S53: Use the reverse deep learning algorithm to evaluate the overall performance of the vehicle during the lane change process, including vehicle safety and driving comfort. Vehicle safety is quantified by vehicle stability and potential collision risk.

[0035] The vehicle stability state quantitative index is: (Formula 5) In the formula, is a stability indicator, is the vehicle lateral velocity, is the vehicle longitudinal velocity.

[0036] The quantitative index of the potential collision risk of the vehicle is: (Formula 6) In the formula, is the collision risk indicator, and is the weight factor, and are the lateral and longitudinal coordinates of the autonomous vehicle, and are the lateral and longitudinal coordinates of the collision risk vehicle.

[0037] The following vector is constructed to define the comprehensive performance index of vehicle control: (Formula 7) (Formula 8) In the formula, As a comprehensive performance indicator, is a stability indicator, is the collision risk indicator, is the total number of sample points collected for each candidate path, n is the total number of vehicles at risk of collision.

[0038] Formula 7 is normalized, and the normalized function is expressed as: (Formula 9) The reward function in the inverse depth algorithm is expressed as: (Formula 10) In the formula, is the reward function, is the weight coefficient, for The transpose of , set to a constant.

[0039] Step S54: Optimize the weight coefficient in step S53 based on the driving behavior of experienced drivers in lane change as expert experience and , and trained in the inverse deep learning algorithm, so that the driving vehicle can show lane-changing behavior similar to that of an experienced driver. The proposed framework for training lane-changing behavior of experienced drivers based on deep reinforcement learning is shown in the figure below. Figure 3 shown.

[0040] Figure 3 The following are three contents, including ① based on the constructed polynomial expression candidate path, applying deep reinforcement learning method to learn the path planning experience of skilled drivers and formulate the path planning method of autonomous driving vehicles; ② combining the vehicle dynamics model and the path planning scheme determined in ①, establishing the cruise control lane changing strategy, formulating the path planning and motion control reward function of autonomous driving vehicles; ③ according to the cruise and lane changing strategies determined in ②, and the determined reward function, the deep reinforcement learning architecture design and weight coefficient optimization of the cruise control of autonomous driving vehicles are carried out, and finally the high-speed cruise control capability of autonomous driving vehicles is formed. Among them, the lane changing strategy and path planning strategy are designed based on the deep reinforcement learning system framework, which improves the motion stability of autonomous driving vehicles and the potential collision avoidance risk probability of vehicles.

[0041] The present application also provides an electronic device for an autonomous vehicle, comprising a memory and a navigator. The memory stores computer programs, the processor is a central processing unit of the autonomous vehicle, and the software program in the memory can be run on the onboard central processor. The navigator is used to determine the current position, vehicle speed, and road speed limit of the vehicle.

[0042] It should be noted that the various module units proposed in the present invention may be integrated into one processing unit, or each unit may exist independently, or two or more may be integrated into one unit.

[0043] The advantages of this example are: In order to improve the safety and economy of cruising of autonomous vehicles, an overall framework for autonomous cruise control of autonomous vehicles on highways is proposed. The framework inherits the decision-making, path planning and motion control modules. The proposed framework can ensure the performance of high-speed cruising vehicles.

[0044] The present invention solves the problems of low control accuracy and poor stability in existing cruise control technologies, enhances the adaptability of the vehicle cruise control method to complex working conditions, and improves lane change safety and driving comfort. Simulation results confirm that the proposed method can effectively reduce the risk of vehicle collision while achieving stable control.

[0045] The above description is only a feasible implementation example of the present invention, and does not limit the scope of rights of the present invention. All equivalent structural changes made using the contents of the present invention and the drawings are included in the scope of rights of the present invention.

Claims

1. A high-speed cruise control method for an autonomous driving vehicle, characterized in that: The following steps are involved: Step S1: Acquire the real-time status information and road environment information of the autonomous driving vehicle, and accurately locate the autonomous driving vehicle in real time in combination with the positioning system; Step S2: uploading the real-time status information of the autonomous driving vehicle and the road environment information obtained in step S1 to the vehicle control unit; Step S3: Establish a vehicle dynamics model, which can characterize the longitudinal, lateral and yaw characteristics of the vehicle, as follows: The vehicle dynamics model is established as: (Formula 1) In the formula, the state vector , , , , , , represents the longitudinal velocity of the vehicle, the lateral velocity of the vehicle, the yaw angle of the vehicle, the yaw angular velocity of the vehicle, the ordinate of the vehicle geodetic coordinate system and the abscissa of the vehicle geodetic coordinate system respectively; the control vector , and They represent the vehicle longitudinal acceleration and the front wheel steering angle respectively; C is the state parameter matrix, D is the control parameter matrix; Step S4: establishing a cruise control decision module for the autonomous driving vehicle, formulating a hierarchical control strategy for lane keeping or lane changing of the autonomous driving vehicle by judging whether there is a followable vehicle in front of the autonomous driving vehicle, the speed of the followable vehicle, and the relative distance between the autonomous driving vehicle and the followable vehicle; Step S5: Establish an autonomous driving vehicle motion control method, formulate a vehicle motion control strategy for the vehicle cruise control decision module established in step S4, design a vehicle optimal path planning method, determine the optimal reference path, and perform vehicle motion control to achieve autonomous cruising of the vehicle.

2. The high-speed cruise control method for an autonomous driving vehicle according to claim 1, characterized in that: The vehicle real-time status information described in step S1 includes the speed, acceleration and position of the autonomous driving vehicle; the road environment information includes road width, road slope, road section speed limit, traffic environment information, and the position and speed information of surrounding vehicles.

3. The high-speed cruise control method for an autonomous driving vehicle according to claim 1, characterized in that: The road environment information uploaded to the vehicle-mounted control unit in step S2 is data within a 200-meter communication range around the driving vehicle.

4. The method for high-speed cruise control of an autonomous driving vehicle according to claim 1, characterized in that: The step S4 of establishing the autonomous driving vehicle cruise control decision module mainly includes the following steps: Step S41: based on the acquired real-time status information of the autonomous driving vehicle and the road environment information, detecting whether there is a following vehicle in front of the autonomous driving vehicle, and if there is no following vehicle in front, performing lane keeping and cruising at an economic speed designed for the autonomous driving vehicle; Step S42: Based on the acquired real-time status information and road environment information of the autonomous driving vehicle, detect whether there is a following vehicle in front of the autonomous driving vehicle. If there is a following vehicle in front, first perform lane keeping, cruise at the economic speed designed for the autonomous driving vehicle, and then determine whether a lane change is required by comparing the relative speed of the vehicle in front and the autonomous driving vehicle, and the relative distance between the vehicle in front and the autonomous driving vehicle. Step S43: Based on the acquired real-time status information of the autonomous driving vehicle and the road environment information, if the speed of the followable vehicle is lower than the economic cruising speed set by the autonomous driving vehicle, determine whether a lane change condition exists based on the acquired real-time status information of the autonomous driving vehicle and the surrounding environment information; If yes, then change lanes; if no, then exit cruise control and slow down to avoid collision; Step S44: if the distance between the autonomous driving vehicle and the following vehicle is lower than the set safety threshold, determine whether there is a lane change condition based on the autonomous driving vehicle state information and surrounding environment information obtained in real time; If yes, change lanes; if no, exit cruise control and slow down to avoid collision.

5. The high-speed cruise control method for an autonomous driving vehicle according to claim 4, characterized in that: The cruise mode switching strategy proposed in step S4 is as follows: Step S411: Set the economic cruising speed to , the speed of the vehicle being followed is , in km / h. If there is no vehicle ahead that can follow, the vehicle will maintain the lane and cruise at a constant speed; Step S412: If there is a vehicle ahead that can be followed, , then lane keeping and cruise control; Step S413: If there is a vehicle ahead that can be followed, , then determine whether the distance between the two vehicles exceeds the set safety threshold. If not, then keep the lane; , the distance between the two vehicles will continue to decrease. If the distance between the two vehicles exceeds the set safety threshold, the real-time autonomous driving vehicle and road environment information obtained will be used to determine whether there is a lane change condition. If not, the relative distance between the two vehicles will be used as the control target, and the vehicle will cruise; Step S414: Based on step S413, if there are lane changing conditions, the automatic driving vehicle is controlled to perform lane changing operations.

6. The method for high-speed cruise control of an autonomous driving vehicle according to claim 1, characterized in that: The automatic driving vehicle motion control method proposed in step S5 is characterized by comprising the following steps: Step S51: According to the characteristic that the stability of lane changing behavior is crucial when the autonomous driving vehicle is cruising at high speed, a method based on inverse reinforcement learning is proposed to learn the steering behavior of experienced drivers, thereby improving the feasibility of the vehicle's planned path and the stability of the vehicle; Step S52: Based on the driving characteristics of the vehicle in the high-speed lane, a candidate path expressed in a polynomial is constructed as follows: (Formula 2) In the formula, is the vehicle lateral reference position, X is the longitudinal coordinate of the vehicle, , , , , and are the polynomial coefficients, The initial position of the vehicle is defined as , the lateral distance and longitudinal distance involved in the lane change process are respectively L and M Indicates that if =0, =0, then the terminal position The following relationship exists: (Formula 3) In the formula, L is the lane width, M is the longitudinal distance of lane change. During the lane change process, the longitudinal speed of the vehicle remains unchanged. The lane change time is , the longitudinal velocity of the vehicle is ,but: (Formula 4) Step S53: Use the reverse deep learning algorithm to evaluate the overall performance of the vehicle during the lane change process, including vehicle safety and driving comfort. Vehicle safety is quantified by vehicle stability and potential collision risk.

7. The method for high-speed cruise control of an autonomous driving vehicle according to claim 6, characterized in that: In step S53 The vehicle stability state quantitative index is: (Formula 5) In the formula, is a stability indicator, is the vehicle lateral velocity, is the vehicle longitudinal velocity; The quantitative index of the potential collision risk of the vehicle is: (Formula 6) In the formula, is the collision risk indicator, and is the weight factor, and are the lateral and longitudinal coordinates of the autonomous vehicle, and are the lateral and longitudinal coordinates of the collision risk vehicle; The following vector is constructed to define the comprehensive performance index of vehicle control: (Formula 7) (Formula 8) In the formula, As a comprehensive performance indicator, is a stability indicator, is the collision risk indicator, is the total number of sample points collected for each candidate path, n is the total number of vehicles at risk of collision; Formula 7 is normalized, and the normalized function is expressed as: (Formula 9) The reward function in the inverse depth algorithm is expressed as: (Formula 10) In the formula, is the reward function, is the weight coefficient, for The transpose of , set to a constant.

8. The high-speed cruise control method for an autonomous driving vehicle according to claim 7, characterized in that: The step S54 is also included: using the driving behavior of experienced drivers in lane change as expert experience to optimize the weight coefficients in step S53 and , trained in an inverse deep learning algorithm so that the driving vehicle exhibits lane-changing behavior similar to that of an experienced driver.

9. An electronic device for an autonomous driving vehicle, used to implement the autonomous driving vehicle high-speed cruise control method according to claims 1-8, characterized in that: It includes a memory and a navigator. The memory stores computer programs. The software programs in the memory can be run on the central processing unit of the autonomous driving vehicle. The navigator is used to determine the current position, vehicle speed and road speed limit of the vehicle.

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