Adaptive scene acceleration and deceleration control and switching method

Abstract

The invention is applicable to the technical field of automobiles, and provides a scene-adaptive acceleration and deceleration control and switching method, which comprises the following steps of: acquiring vehicle driving environment information and vehicle state information; establishing an offline scene recognition model, recognizing the current driving scene of the vehicle, and outputting a driving mode; determining a scene recognition time window, collecting driving scene data, and recognizing a driving scene; and establishing a driver intention recognition module. According to the method, vehicle driving environment information and vehicle state information are obtained through an intelligent sensing module, and then the sensed vehicle and environment information serves as input feature vectors of scene recognition based on a support vector machine, so that precise recognition of the current driving scene of the vehicle is achieved. Meanwhile, an accurate and conventional acceleration and deceleration intention recognition module is designed according to scene requirements, and for the accurate acceleration and deceleration intention recognition module, the driver can'finely adjust 'the longitudinal acceleration and deceleration of the vehicle to meet the accurate control requirements of the driver in some scenes.

Description

Technical Field

[0001] The present invention belongs to the technical field of automobiles, and particularly relates to a method for accelerating, decelerating, controlling, and switching in an adaptive scenario. Background Art

[0002] The automotive manufacturing industry is accelerating its development towards intelligence. The automotive industry is undergoing a huge transformation. This industrial transformation triggered by innovative technologies is reshaping the value system of the century-old automotive industry. With the continuous evolution of automotive chassis technology towards intelligence, higher-level functional requirements are imposed on intelligent chassis systems. Specifically, the core point is that intelligent chassis must possess intelligent perception capabilities, that is, it can accurately identify and distinguish diverse driving scenarios, just as a human driver judges road conditions, traffic environment, etc. based on their own experience and perception. At the same time, users' demands for driving experience are also gradually increasing. Drivers are no longer satisfied with a single and unchanging driving mode, but expect the vehicle to adaptively adjust its driving mode according to the changes in its scenario, so that drivers can more comfortably operate the vehicle in different driving scenarios.

[0003] Currently, there are still limitations in the adaptive switching of acceleration and deceleration modes based on different driving scenarios: current adaptive chassis mainly focuses on the design of corresponding adaptive mode switching in the suspension system, and there is less research on the adaptive design and switching method of acceleration and deceleration driving modes. Currently, the acceleration and deceleration driving modes of vehicles cannot be adjusted and optimized according to specific driving requirements to assist drivers in achieving more precise and efficient driving operations in some driving scenarios that require precise operation (such as parking scenarios, urban congestion scenarios, low-speed meeting scenarios, low-curve radius scenarios, bad weather, etc.), and improving the overall driving experience and vehicle performance. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for accelerating, decelerating, controlling, and switching in an adaptive scenario, aiming to solve the technical problems existing in the prior art determined in the background art.

[0005] The present invention is implemented as follows. A method for accelerating, decelerating, controlling, and switching in an adaptive scenario, the method comprising: S1. Obtain vehicle driving environment information and vehicle own state information through on-vehicle sensors; S2. Use the data set collected based on a driving simulator to establish an offline scenario recognition model through a support vector machine algorithm. This model can identify the current driving scenario of the vehicle and output a matching driving mode according to the driving scenario; S3. Determine the scenario recognition time window of the offline scenario recognition model , and collect the driving scenario data in S1 within the time window for driving scenario recognition; S4. Establish a driver intention recognition module according to the recognized acceleration and deceleration patterns, and perform driving mode switching through the Sigmoid function.

[0006] As a further solution of the present invention, the S2 specifically includes: Step 1: Driving scenario annotation. Classify and annotate regular driving scenarios and precise driving scenarios based on the data set collected by the driving simulator. The precise driving scenarios include parking scenarios, urban congestion scenarios, low-speed oncoming vehicle scenarios, low curve radius scenarios, and bad weather scenarios. Further, other scenarios in the data set are annotated as regular driving scenarios.

[0007] Step 2: Driving scenario data preprocessing. Use the data in the precise driving scenarios and regular driving scenarios in Step 1 as input features, and then normalize these features. Use min-max normalization to unify the dimension of each feature to the interval [0, 1].

[0008] ; Wherein, represents the maximum value in a single feature, represents the minimum value in a single feature, represents the current feature value, represents the standardized feature value after normalizing the current feature value.

[0009] Step 3: Establish a driving scenario recognition model based on support vector machine using the driving scenario data obtained in Step 1 and Step 2. Since the RBF kernel performs optimally in non-linearly separable scenarios, the RBF kernel is used as the kernel function of this support vector machine model. At the same time, 70% of the data in the data set is used as the training set, and the remaining 30% of the data is used as the test set to verify the driving scenario recognition effect. The driving scenario recognition model is trained through the training set, and the recognition accuracy is verified through the test set.

[0010] As a further solution of the present invention, the S3 specifically includes: Set a scenario recognition time window based on the recognition requirements of the offline scenario recognition model ; Within the recognized time window , continuously collect vehicle driving environment data and vehicle own state data through in-vehicle sensors; The vehicle driving environment data and vehicle own state data include: the vehicle speed of surrounding vehicles, the distance from the vehicle itself, the distance between the vehicle and obstacles, lane line information, weather conditions, road surface conditions, vehicle load, vehicle speed, acceleration and deceleration data; Perform real-time analysis on the collected vehicle driving environment data and vehicle own state data, and use the driving scenario recognition model based on support vector machine obtained through S2 to perform driving scenario recognition processing on the vehicle driving environment information and vehicle own state information obtained in the recognition time window and output the driving mode corresponding to the current driving environment according to the recognition result, including the normal driving mode and the precise driving mode; if it is recognized that the current driving scenario matches the driving conditions of the precise driving mode, the recognition result will be fed back and the driving mode will be switched to the precise driving mode.

[0011] As a further solution of the present invention, the S4 specifically includes: Combined with the driving mode corresponding to the current environment, a precise acceleration / deceleration intention recognition module and a normal acceleration / deceleration intention recognition module are respectively established, and the precise acceleration / deceleration intention recognition module and the normal acceleration / deceleration intention recognition module respectively match different driver intentions; Both the normal acceleration / deceleration intention recognition module and the precise acceleration / deceleration intention recognition module are the mapping relationships between the accelerator / brake pedal opening and the desired motion intensity of the vehicle.

[0012] As a further solution of the present invention, the specific expression of the normal acceleration / deceleration intention recognition module is: ; where is the opening of the normal acceleration / brake pedal, is the acceleration output by the normal acceleration / deceleration intention recognition, is the acceleration gain coefficient; The specific expression of the precise acceleration / deceleration intention recognition module is: ; where is the opening of the precise acceleration / brake pedal, is the acceleration output by the normal acceleration / deceleration intention recognition, and under the same pedal opening change, the acceleration / deceleration change amount output by the precise acceleration / deceleration intention recognition module is less than the acceleration / deceleration change amount output by the normal acceleration / deceleration intention recognition module, is the acceleration gain coefficient, G is the driving mode fineness coefficient, which represents the scaling ratio with the normal mode.

[0013] As a further solution of the present invention, controlling the mixing ratio between the normal driving mode and the precise driving mode generates a driving mode switching weight coefficient through the Sigmoid function to achieve, and , controlling the mixing ratio between the normal driving mode and the precise driving mode: ; Among them, is the weight coefficient for driving mode switching, is the transition center point, is the transition duration, is the slope parameter, which is used to control the transition speed between the normal driving mode and the precise driving mode; During the process of mode transition, the output of the hybrid motion intensity is: ; Among them, is the hybrid motion intensity, is the current accelerator / brake pedal opening, and , is the driving mode transition timestamp.

[0014] As a further solution of the present invention, in the normal driving mode, the driver's operation amount is obtained as the input of the normal acceleration / deceleration intention recognition module, and the output is the expected longitudinal acceleration / deceleration in the normal driving mode; In the precise driving mode, the driver's operation amount is obtained as the input of the precise acceleration / deceleration intention recognition module, and the output is the expected longitudinal acceleration / deceleration in the precise driving mode.

[0015] During the process of longitudinal acceleration / deceleration mode switching, the driver's operation amount is obtained as the input of the precise acceleration / deceleration intention recognition module, and the output is the hybrid expected longitudinal acceleration / deceleration during the driving mode switching process.

[0016] Through the above process, the mapping relationship between the driver's operation amount and the expected longitudinal acceleration / deceleration in the two driving modes and during the driving mode switching process can be obtained. The overall expression is as follows: ; Among them, is the expected longitudinal acceleration / deceleration, is the accelerator / brake pedal opening.

[0017] The beneficial effects of the present invention are: The method establishes an offline scene recognition model based on the support vector machine algorithm using driving simulator data. This model can identify the current driving scene of the vehicle. Further, the vehicle driving environment information and vehicle state information are obtained through the intelligent perception module, and then the perceived vehicle and environment information is used as the input feature vector of the offline scene recognition model based on the support vector machine to achieve accurate recognition of the current driving scene of the vehicle. At the same time, a precise and conventional acceleration / deceleration intention recognition module is designed according to the scene requirements. For the precise acceleration / deceleration intention recognition module, the driver can "fine-tune" the longitudinal acceleration and deceleration of the vehicle to meet the precise control requirements of the driver in some scenarios. Further, when the driving mode switch is recognized, the method controls the weight mixing through the Sigmoid function to achieve a smooth and seamless driving mode switch. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a functional architecture diagram of a method for designing and switching acceleration / deceleration modes for an adaptive scene provided by an embodiment of the present invention; Figure 2 is a flowchart of a method for designing and switching acceleration / deceleration modes for an adaptive scene provided by an embodiment of the present invention; Figure 3 is a mapping relationship between the accelerator / decelerator pedal opening and the desired vehicle motion intensity in a method for designing and switching acceleration / deceleration modes for an adaptive scene provided by an embodiment of the present invention; Figure 4 The Sigmoid function is used as a transition function for switching between the precise driving mode and the standard driving mode. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] As Figure 1 and Figure 2 shown, a method for adaptive scene acceleration / deceleration control and switching is provided. The method includes: S1. Comprehensively obtain vehicle driving environment information and vehicle own state information through on-vehicle sensors. Among them, the driving environment information covers multi-dimensional parameters such as the driving speed of surrounding vehicles, the dynamic distance parameters between surrounding vehicles and the vehicle itself, the distance information between the vehicle and various obstacles, as well as road markings, weather conditions, and road surface conditions; the vehicle state information includes parameters such as the dynamic load information carried by the vehicle, the real-time driving speed of the vehicle, and the acceleration / deceleration selection mode information.

[0021] ; S2. Use the dataset collected based on a driving simulator to establish an offline scenario recognition model through a support vector machine algorithm. This model can identify the current driving scenario of the vehicle and output a matching driving mode according to the driving scenario. Specifically, the vehicle driving environment information in the driving simulator dataset (such as parameters like the vehicle speeds of surrounding vehicles, the distance from the vehicle itself, the distance between the vehicle and obstacles, lane line information, weather conditions, and road surface conditions) and the vehicle's own state information (such as parameters like the vehicle load, vehicle speed, acceleration and deceleration modes) are used as the input feature vectors of the support vector machine (SVM) algorithm. After the classification and recognition processing of the SVM algorithm, the corresponding driving scenario is output, and the corresponding driving mode is also output, including the normal driving mode and the precise driving mode.

[0022] In the example of the present invention, training and test datasets are extracted from the prior driving simulator driving dataset. The input feature vectors are the vehicle speeds of surrounding vehicles, the distance from the vehicle itself, the distance between the vehicle and obstacles, lane line information, weather conditions, road surface conditions, vehicle load, vehicle speed, acceleration and deceleration. The driving scenario recognition model based on SVM can be realized through the following steps: Step 1, driving scenario annotation. Classify and annotate the normal driving scenario and the precise driving scenario based on the dataset collected by the driving simulator. The precise driving scenarios include the parking scenario, the urban congestion scenario, the low-speed vehicle meeting scenario, the low curve radius scenario, and the bad weather scenario. Further, other scenarios in the dataset are annotated as normal driving scenarios.

[0023] Step 2, driving scenario data preprocessing. Use the data in the precise driving scenario and the normal driving scenario in Step 1 as input features, and then normalize these features. The min-max normalization is used to unify the dimension of each feature to the interval [0, 1].

[0024] ; Among them, represents the maximum value in a single feature, represents the minimum value in a single feature, represents the current feature value, represents the standardized feature value after normalizing the current feature value.

[0025] Step 3: Establish a driving scenario recognition model based on a support vector machine using the driving scenario data obtained in Steps 1 and 2. Since the RBF kernel performs optimally in non-linearly separable scenarios, the RBF kernel is used as the kernel function of this support vector machine model. At the same time, 70% of the data in the dataset is used as the training set, and the remaining 30% of the data is used as the test set to verify the driving scenario recognition effect. The driving scenario recognition model is trained using the training set, and the recognition accuracy is verified using the test set.

[0026] Through the above machine learning training, an offline SVM driving scenario recognition model, i.e., an offline scenario recognition model, can be obtained. At the same time, it is defined that an output of -1 from the offline scenario recognition model represents the conventional acceleration and deceleration mode, and an output of +1 represents the precise acceleration and deceleration mode.

[0027] Among them, the conventional driving mode corresponds to the conventional acceleration and deceleration mode, and the precise driving mode corresponds to the precise acceleration and deceleration mode.

[0028] S3. Determine the scenario recognition time window of the offline scenario recognition model and collect driving scenario data within the time window for driving scenario recognition; Based on the recognition requirements of the offline scenario recognition model, set the scenario recognition time window ; Within a time window, collect the vehicle speed of surrounding vehicles, the distance from the vehicle, the distance between the vehicle and obstacles, lane line information, weather conditions, road surface conditions, the vehicle load, vehicle speed, acceleration and deceleration data through in-vehicle sensors, and use the collected data as the data input for the current driving scenario recognition.

[0029] Since scenario recognition requires real-time performance, the scenario recognition time window should not be too large. After recognizing that the driving scenario meets the conditions of the precise driving mode, it should be immediately fed back to the driver and the driving mode should be switched to the precise driving mode. As a preferred embodiment of the present invention, the scenario recognition time window is initially set to 10 s.

[0030] Perform real-time analysis on the collected vehicle driving environment data and vehicle own state data, and use the driving scenario recognition model based on the support vector machine obtained in S2 to perform driving scenario recognition processing on the vehicle driving environment information and vehicle own state information obtained in the recognition time window and output the driving mode corresponding to the current driving environment according to the recognition result, including the conventional driving mode and the precise driving mode; if it is recognized that the current driving scenario matches the driving conditions of the precise driving mode, the recognition result is fed back and the driving mode is switched to the precise driving mode.

[0031] S4. Based on the recognized acceleration and deceleration patterns, establish a driver intention recognition module and switch the driving mode through the Sigmoid function.

[0032] Combined with the driving mode corresponding to the current environment, establish a precise acceleration and deceleration intention recognition module and a conventional acceleration and deceleration intention recognition module respectively. The precise acceleration and deceleration intention recognition module and the conventional acceleration and deceleration intention recognition module match different driver intentions respectively. Based on the acceleration and deceleration patterns recognized in S2, match different driver intention recognition modules. As Figure 3 shown, both the conventional acceleration and deceleration intention recognition module and the precise acceleration and deceleration intention recognition module are the mapping relationships between the opening of the acceleration / deceleration pedal and the expected motion intensity of the vehicle.

[0033] The specific expression of the conventional acceleration and deceleration intention recognition module is: ; Among them, is the opening of the conventional acceleration / braking pedal, is the acceleration output by the conventional acceleration and deceleration intention recognition, is the acceleration gain coefficient; The precise acceleration and deceleration intention recognition module is also the mapping relationship between the opening of the acceleration / deceleration pedal and the expected motion intensity of the vehicle. The precise acceleration and deceleration intention recognition module requires that the mapping relationship between the opening of the acceleration / deceleration pedal and the expected motion intensity of the vehicle needs to be more refined to meet the needs of the driver to precisely control the vehicle. That is, when the driver changes the same opening of the acceleration / deceleration pedal, the acceleration and deceleration change amount output by the precise acceleration and deceleration intention recognition module is smaller than that output by the conventional acceleration and deceleration intention recognition module. Therefore, when the driver makes a large operation amount, the acceleration and deceleration change amount of the vehicle is smaller. That is, in the precise acceleration and deceleration mode, the driver can "fine-tune" the longitudinal acceleration and deceleration of the vehicle. Its specific expression is as follows: ; Among them, is the opening of the precise acceleration / braking pedal, is the acceleration output by the conventional acceleration and deceleration intention recognition, and when the pedal opening changes by the same amount, the acceleration and deceleration change amount output by the precise acceleration and deceleration intention recognition module is smaller than that output by the conventional acceleration and deceleration intention recognition module, is the acceleration gain coefficient, and G is the driving mode refinement coefficient, which represents the scaling ratio with the conventional mode.

[0034] The Sigmoid function is used as the transition function for switching between the precise driving mode and the standard driving mode. After the recognition output based on S2 indicates the need to switch the acceleration and deceleration mode, directly switching the driving mode will cause a sudden change in the longitudinal acceleration output by the driver intention recognition module, affecting the driving experience of the driver. Therefore, the Sigmoid function is used to switch the acceleration and deceleration mode, so that the longitudinal acceleration output by the driver intention recognition module changes continuously. By controlling the weight mixing through the Sigmoid function, a smooth and seamless driving mode switch can be achieved, while retaining the flexible control of the transition speed and emergency interruption. The driving mode switch weight coefficient is generated through the Sigmoid function , and , to control the mixing ratio between the conventional driving mode and the precise driving mode: ; Among them, is the driving mode switch weight coefficient, is the transition center point, is the transition duration, is the slope parameter, which is used to control the transition speed between the conventional driving mode and the precise driving mode and can be optimized according to the driver's needs or on-vehicle tests; As a preferred embodiment of the present invention, the transition center point is set to 1 s, the transition duration is set to 2 s, and the slope parameter is initially set to 5, ; The image of using the Sigmoid function as the transition function for switching between the precise driving mode and the standard driving mode is as shown in Figure 4 .

[0035] During the mode transition process, the mixed expected longitudinal acceleration and deceleration output is: ; Among them, is the mixed expected longitudinal acceleration and deceleration, is the current acceleration / brake pedal opening, and , is the driving mode transition timestamp.

[0036] In the conventional driving mode, the driver's operation amount is obtained as the input of the conventional acceleration and deceleration intention recognition module, and the output is the expected longitudinal acceleration and deceleration in the conventional driving mode; In the precise driving mode, the driver's operation amount is obtained as the input of the precise acceleration and deceleration intention recognition module, and the output is the expected longitudinal acceleration and deceleration in the precise driving mode.

[0037] During the longitudinal acceleration and deceleration mode switching process, the longitudinal acceleration and deceleration are input by the driver's operation amount, which serves as both the input for conventional acceleration and deceleration intention recognition and precise acceleration and deceleration intention recognition. The output is the mixed expected longitudinal acceleration and deceleration based on the Sigmoid as the transition function.

[0038] Through the above process, the mapping relationships between the driver's operation amount and the expected longitudinal acceleration and deceleration in the two driving modes and during the driving mode switching process can be obtained. The overall expression is as follows: ; Among them, is the expected longitudinal acceleration and deceleration, is the opening of the accelerator / brake pedal.

[0039] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0040] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0041] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention shall be subject to the appended claims.

[0042] The above is only the preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A scene-adaptive acceleration and deceleration control and switching method, characterized in that: The method comprises: S1, obtaining vehicle driving environment information and vehicle status information through vehicle-mounted sensors; S2. Using the data set collected based on the driving simulator, an offline scene recognition model is established through a support vector machine algorithm, wherein the offline scene recognition model is used to identify the current driving scene of the vehicle and output a matching driving mode according to the driving scene; S3. Determine the scene recognition time window of the offline scene recognition model , and collect the driving scene data described in S1 within the time window to perform driving scene recognition; S4. According to the identified acceleration and deceleration mode, a driver intention recognition module is established, and the driving mode is switched through the Sigmoid function.

2. The method according to claim 1, characterized in that: The S2 specifically includes: Step 1: Driving scene annotation: classify and annotate conventional driving scenes and precise driving scenes based on the data set collected by the driving simulator. Precise driving scenes include parking scenes, urban congestion scenes, low-speed meeting scenes, low curve radius scenes, and severe weather scenes. Other scenes in the data set are annotated as conventional driving scenes. Step 2: Preprocessing of driving scene data: taking the data of the precise driving scene and the conventional driving scene in step 1 as input features, and normalizing them, using min-max standardization to unify the dimensions of each feature to the interval [0,1]; ； in, Represents the maximum value of a single feature. Represents the minimum value of a single feature. represents the current eigenvalue, Represents the standard eigenvalue after normalization of the current eigenvalue; Step three, establish a driving scene recognition model based on support vector machine through the driving scene data obtained in steps one and two. At the same time, use 70% of the data in the data set as a training set and the remaining 30% of the data as a test set. Use the training set to train the driving scene recognition model and use the test set to verify the recognition accuracy.

3. The method according to claim 2, characterized in that The S3 specifically includes: Set the scene recognition time window based on the recognition requirements of the offline scene recognition model ; In the identified time window The vehicle's driving environment data and the vehicle's own status data are continuously collected through on-board sensors; The vehicle driving environment data and the vehicle's own status data include: the speed of surrounding vehicles, the distance from the vehicle, the distance between the vehicle and obstacles, lane line information, weather conditions and road conditions, vehicle load, speed, acceleration and deceleration data; The collected vehicle driving environment data and vehicle status data are analyzed in real time, and the driving scene recognition model based on support vector machine obtained by S2 is used to identify the time window. The system uses the vehicle driving environment information and the vehicle's own status information obtained from the vehicle to perform driving scene recognition processing, and outputs the driving mode corresponding to the current driving environment based on the recognition result, including the conventional driving mode and the precise driving mode; if it is recognized that the current driving scene matches the driving conditions of the precise driving mode, the recognition result will be fed back and the driving mode will be switched to the precise driving mode.

4. The method according to claim 3, characterized in that The S4 specifically includes: In combination with the driving mode corresponding to the current environment, a precise acceleration / deceleration intention recognition module and a conventional acceleration / deceleration intention recognition module are respectively established, wherein the precise acceleration / deceleration intention recognition module and the conventional acceleration / deceleration intention recognition module respectively match different driver intentions; The conventional acceleration / deceleration intention recognition module and the precise acceleration / deceleration intention recognition module are both mapping relationships between the acceleration / deceleration pedal opening and the vehicle's expected motion intensity.

5. The method according to claim 4, characterized in that The specific expression of the conventional acceleration / deceleration intention recognition module is: ； in, is the opening of the normal accelerator / brake pedal, The acceleration output for conventional acceleration / deceleration intention recognition, is the acceleration gain coefficient; The specific expression of the precise acceleration and deceleration intention recognition module is: ； in, To accurately adjust the opening of the accelerator / brake pedal, is the acceleration output by the conventional acceleration / deceleration intention recognition module, and under the same pedal opening change, the acceleration / deceleration change output by the precise acceleration / deceleration intention recognition module is smaller than the acceleration / deceleration change output by the conventional acceleration / deceleration intention recognition module. is the acceleration gain coefficient, G is the driving mode refinement coefficient, which represents the scaling ratio with the normal mode.

6. The method according to claim 5, characterized in that Control the mixing ratio between the conventional driving mode and the precise driving mode to generate a driving mode switching weight coefficient through a Sigmoid function to achieve, and , controls the mix between normal and precision driving modes: ； in, Switch weight coefficients for driving modes, is the transition center point, is the transition time, is the slope parameter, which is used to control the transition speed between the normal driving mode and the precision driving mode; During the mode transition, the mixed expected longitudinal acceleration and deceleration output is: ； in, is the mixed desired longitudinal acceleration and deceleration, is the current accelerator / brake pedal opening, and , Timestamp for driving mode transition.

7. The method according to claim 4, characterized in that In the normal driving mode, the driver's operation amount is obtained as the input of the normal acceleration and deceleration intention recognition module, and the output is the expected longitudinal acceleration and deceleration in the normal driving mode; In the precision driving mode, the driver's operation amount is obtained as the input of the precision acceleration and deceleration intention recognition module, and the output is the expected longitudinal acceleration and deceleration in the precision driving mode; During the longitudinal acceleration and deceleration mode switching process, the driver's operation amount is obtained as the input of the precise acceleration and deceleration intention recognition module, and the output is the mixed expected longitudinal acceleration and deceleration during the driving mode switching process.

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