Vehicle control method, electronic equipment and vehicle

Abstract

The invention provides a vehicle control method, electronic equipment and a vehicle, relates to the technical field of intelligent driving, and aims to improve the accuracy of takeover duration. The vehicle control method comprises the steps that when it is determined that a vehicle is in a non-automatic driving mode, a first driving behavior parameter of a driver and a first environment parameter of the vehicle are obtained; and calculating a first driver state score according to the first driving behavior parameter. And according to the first driver state score, the first environment parameter and the initial takeover duration, calculating a first takeover duration. When it is determined that the vehicle is in the automatic driving mode, a take-over prompt is sent out according to the first take-over duration. Since the first driver state score reflects the driver state and the first environmental parameter reflects the environmental complexity, the first takeover duration covers the influence of the driver state and the environmental complexity on the driver takeover capability, the driver takeover capability can be accurately reflected, the personalized takeover demand can be met, and the driver takeover efficiency is improved. And the safety and the use experience of the vehicle are improved.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology, and particularly relates to a vehicle control method, an electronic device, and a vehicle. Background Art

[0002] In the context of autonomous driving, when the autonomous driving system determines that the vehicle meets the takeover condition, the autonomous driving system issues a takeover prompt to prompt the driver to take over the control of the vehicle. When the autonomous driving system issues a takeover prompt, the autonomous driving system needs to continue to control the vehicle within a certain takeover duration to ensure a smooth transition of vehicle control from the autonomous driving system to the driver. The takeover duration is usually a fixed value. When the takeover duration is short, it is possible that the driver has not obtained vehicle control when the autonomous driving system relinquishes vehicle control, which poses a serious safety hazard. When the takeover duration is long, it is possible that the autonomous driving system and the driver both have vehicle control at the same time, which will reduce the user experience. Summary of the Invention

[0003] Embodiments of this application provide a vehicle control method, an electronic device, and a vehicle, aiming to improve the accuracy of the takeover duration.

[0004] A first aspect of an embodiment of this application provides a vehicle control method, which includes: when it is determined that the vehicle is in a non-autonomous driving mode, obtaining a first driving behavior parameter of the driver and a first environmental parameter of the vehicle. Calculating a first driver state score according to the first driving behavior parameter. Calculating a first takeover duration according to the first driver state score, the first environmental parameter, and an initial takeover duration. When it is determined that the vehicle is in an autonomous driving mode, issuing a takeover prompt according to the first takeover duration.

[0005] In this embodiment, since the first driver state score reflects the driver state in the non-autonomous driving scenario and the first environmental parameter reflects the environmental complexity in the non-autonomous driving scenario, the first takeover duration covers the influence of the driver state and environmental complexity in the non-autonomous driving scenario on the driver's takeover ability, can accurately reflect the driver's takeover ability, thus can meet the personalized takeover requirements, and further improve the safety and user experience of the vehicle.

[0006] In one embodiment, the method further includes: when it is determined that the vehicle is in an autonomous driving mode, updating the initial takeover duration.

[0007] In another embodiment, when it is determined that the vehicle is in the autonomous driving mode, updating the initial takeover duration includes: when it is determined that the vehicle is in the autonomous driving mode, obtaining the second driving behavior parameters of the driver and the second environmental parameters of the vehicle. Calculating a second driver status score according to the second driving behavior parameters. Calculating a second takeover duration according to the second driver status score, the second environmental parameters, and the first takeover duration. Updating the initial takeover duration with the second takeover duration.

[0008] In this embodiment, since the second takeover duration covers the influence of the driver's state and the environmental complexity in both autonomous driving scenarios and non-autonomous driving scenarios on the driver's takeover ability, when calculating the first takeover duration according to the first driver status score, the first environmental parameters, and the second takeover duration, the first takeover duration can also cover the influence of the driver's state and the environmental complexity in both autonomous driving scenarios and non-autonomous driving scenarios on the driver's takeover ability, and can more accurately reflect the driver's takeover ability, so as to meet the personalized takeover requirements, and further improve the safety and user experience of the vehicle.

[0009] In another embodiment, the method further includes: when it is determined that the vehicle is in the autonomous driving mode, obtaining the second driving behavior parameters of the driver and the second environmental parameters of the vehicle. Calculating a second driver status score according to the second driving behavior parameters. Calculating a minimum following distance according to the second driver status score, the second environmental parameters, and the following safety distance. Calculating a maximum lane departure value for lane keeping according to the second driver status score, the second environmental parameters, and the lane keeping safety value. Controlling the vehicle to travel according to the maximum lane departure value for lane keeping and the minimum following distance.

[0010] In this embodiment, since the second driver status score reflects the driver's state in the autonomous driving scenario and the second environmental parameters reflect the environmental complexity in the autonomous driving scenario, both the minimum following distance and the maximum lane departure value for lane keeping cover the influence of the driver's state and the environmental complexity in the autonomous driving scenario on driving safety, improving the safety of the vehicle.

[0011] In another embodiment, calculating the first driver status score according to the first driving behavior parameters includes: S1 = w 11 ·F1 + w 12 ·P1 + w 13 ·L Calculating the first takeover duration according to the first driver status score, the first environmental parameters, and the initial takeover duration includes: T1 = T0 – a1·S1 – b1·K1 Among them, the first environmental parameter includes the first traffic complexity coefficient. S1 is the first driver state score, F1 is the number of times the driver is distracted or fatigued when the vehicle is in the non-autonomous driving mode, P1 is the number of times the driver answers or makes a call when the vehicle is in the non-autonomous driving mode, L is the number of times the vehicle strays over the line, T0 is the initial takeover duration, T1 is the first takeover duration, K1 is the first traffic complexity coefficient, w 11 、w 12 、w 13 、a1, b1 are weight coefficients.

[0012] In another embodiment, calculating the second driver state score according to the second driving behavior parameter includes: S2 = w 21 ·F2 + w 22 ·P2 + w 23 ·R + w 24 ·C Calculating the second takeover duration according to the second driver state score, the second environmental parameter and the first takeover duration includes: T2 = T1 – a2·S2 – b2·K2 Among them, the second environmental parameter includes the second traffic complexity coefficient. S2 is the second driver state score, F2 is the number of times the driver is distracted or fatigued when the vehicle is in the autonomous driving mode, P2 is the number of times the driver answers or makes a call when the vehicle is in the autonomous driving mode, R is the driver's takeover response duration, C is the number of times of distraction prompt upgrade, T1 is the first takeover duration, T2 is the second takeover duration, K2 is the second traffic complexity coefficient, w 22 、w 23 、w 24 、a2, b2 are weight coefficients.

[0013] In another embodiment, calculating the minimum following distance according to the second driver state score, the second environmental parameter and the following safety distance includes: D1 = D0 + q1·S2 + r1·K2 Among them, the second environmental parameter includes the second traffic complexity coefficient. D1 is the minimum following distance, D0 is the following safety distance, S2 is the second driver state score, K2 is the second traffic complexity coefficient, q1, r1 are weight coefficients.

[0014] In another embodiment, calculating the maximum lane departure value according to the second driver state score, the second environmental parameter and the lane keeping safety value includes: M1 = M0 – q2·S2 – r2·K2 Among them, the second environmental parameter includes a second traffic complexity coefficient. M1 is the maximum lane-keeping deviation value, M0 is the lane-keeping safety value, S2 is the second driver status score, K2 is the second traffic complexity coefficient, and q2 and r2 are weight coefficients.

[0015] In a second aspect of the embodiments of the present application, an electronic device is provided, which includes a memory and a processor. When the processor executes computer instructions stored in the memory, the method provided in the first aspect is implemented.

[0016] In a third aspect of the embodiments of the present application, a vehicle is provided, which includes a processor configured to: when it is determined that the vehicle is in a non-autonomous driving mode, obtain the first driving behavior parameters of the driver and the first environmental parameters of the vehicle. Calculate the first driver status score according to the first driving behavior parameters. Calculate the first takeover duration according to the first driver status score, the first environmental parameters, and the initial takeover duration. When it is determined that the vehicle is in an autonomous driving mode, send a takeover prompt according to the first takeover duration.

[0017] In a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which computer instructions are stored. When the processor executes the computer instructions, the method provided in the first aspect is implemented.

[0018] In a fifth aspect of the embodiments of the present application, a computer program product is provided, which includes computer instructions. When the processor executes the computer instructions, the method provided in the first aspect is implemented.

[0019] It can be understood that the beneficial effects of the electronic device provided in the second aspect, the vehicle provided in the third aspect, the computer-readable storage medium provided in the fourth aspect, and the computer program product provided in the fifth aspect of the embodiments of the present application are substantially the same as the beneficial effects of the method provided in the first aspect, and will not be elaborated here. Description of the Drawings

[0020] Figure 1 is a flowchart of a vehicle control method provided by an embodiment.

[0021] Figure 2 is a schematic structural diagram of an electronic device provided by an example. Detailed Embodiments

[0022] It should be noted that "a plurality" in the embodiments of the present application means two or more than two. Terms such as "first", "second", "third", "fourth", etc. in the specification, claims, and drawings of the present application are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of the present application or shown in the flowcharts include one or more steps for implementing the method. Without departing from the scope of the claims, the execution orders of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0023] The autonomous driving systems involved in the embodiments of this application include Level 2 (L2) autonomous driving systems, Level 3 (L3) autonomous driving systems, and higher-level autonomous driving systems.

[0024] The L2 autonomous driving system can perform specific Dynamic Driving Tasks (DDTs) under certain conditions, but the driver needs to keep an eye on the surrounding environment and be ready to intervene at any time to ensure driving safety. The dynamic driving tasks in L2 autonomous driving scenarios include, but are not limited to, automatic emergency braking, adaptive cruise control, lane keeping, lane departure warning, automatic parking, and speed limit recognition.

[0025] The L3 autonomous driving system can independently complete all dynamic driving tasks under specific conditions and environments without the need for immediate driver intervention. However, when the L3 autonomous driving system encounters an unexpected situation, the L3 autonomous driving system will issue a takeover prompt to prompt the driver to take over the vehicle control.

[0026] In an autonomous driving scenario, when the autonomous driving system determines that the vehicle meets the takeover conditions, the autonomous driving system issues a takeover prompt to prompt the driver to take over and control the vehicle. The takeover conditions are related to the Operational Design Condition (ODC). The operational design condition is the operating condition specifically designed for dynamic driving tasks, that is, the operating condition of autonomous driving, and the takeover condition is the operating condition for exiting autonomous driving. The takeover conditions include, but are not limited to: (1) The speed of the vehicle exceeds the preset vehicle speed range; (2) The road condition is the preset road condition type, such as an obstacle appears in front of the road, a crowded area of people, etc.; (3) The light intensity exceeds the preset light intensity range; (4) The weather condition is the preset weather type, such as rainy, snowy, or foggy weather conditions, etc.; (5) The vehicle equipment is abnormal, such as a camera or lidar malfunction, etc.

[0027] In an autonomous driving scenario, in addition to the driver responding to the takeover prompt issued by the autonomous driving system to take over control, the driver can also take over the vehicle control actively at any time. For example, the driver actively takes over the vehicle control by triggering an instruction to exit the autonomous driving mode.

[0028] When the autonomous driving system issues a takeover prompt, the autonomous driving system needs to continue to control the vehicle within a certain takeover duration to ensure a smooth transition of vehicle control from the autonomous driving system to the driver. The takeover duration is usually a fixed value. When the takeover duration is short, it is possible that the driver has not obtained vehicle control when the autonomous driving system relinquishes vehicle control, which poses a relatively serious safety hazard. When the takeover duration is long, it is possible that both the autonomous driving system and the driver simultaneously have vehicle control, which will reduce the user experience.

[0029] To improve the accuracy of the takeover duration, an embodiment of the present application provides a vehicle control method. The execution subject of the vehicle control method can be an electronic device or a vehicle.

[0030] It can be understood that the electronic device includes, but is not limited to, in-vehicle devices, wireless terminals in self-driving, and wireless terminals in transportation safety.

[0031] The vehicle control method of the embodiment of the present application will be specifically described below with the electronic device as the execution subject.

[0032] Exemplarily, as Figure 1 shown, the vehicle control method includes the following steps: S101. Obtain the first driving behavior parameters of the driver and the first environmental parameters of the vehicle.

[0033] Among them, the first driving behavior parameters reflect the driving habits of the driver in non-autonomous driving scenarios, and the first environmental parameters reflect the environmental complexity around the vehicle in non-autonomous driving scenarios. The first driving behavior parameters include the number of times the driver is distracted or fatigued in non-autonomous driving scenarios, the number of times the driver answers or makes calls in non-autonomous driving scenarios, and the number of times the vehicle crosses the line.

[0034] In this embodiment, the electronic device can collect the first driving behavior parameters of the driver and the first environmental parameters of the vehicle through a sensor module. The sensor module includes, but is not limited to, cameras, lidar (LiDAR), millimeter-wave radars, ultrasonic sensors, and inertial measurement units (IMUs).

[0035] Exemplarily, the electronic device can collect images of the driver's driving behavior through an in-vehicle camera, extract driving behavior features from the driving behavior images, and compare the driving behavior features with various driving behavior features stored in the database to determine the category of the driving behavior features. When the electronic device determines that the category of the driving behavior features is driver distraction or fatigue, the electronic device records a driver distraction or fatigue event. When the electronic device determines that the category of the driving behavior features is the driver answering or making a call, the electronic device records a driver answering or making a call event. The electronic device counts the number of driver distraction or fatigue events and the number of driver answering or making a call events within a certain sliding time window. The number of driver distraction or fatigue events is the number of times of driver distraction or fatigue, and the number of driver answering or making a call events is the number of times of the driver answering or making a call. Among them, the sliding time window is a method for processing time series data. It analyzes data by applying a fixed time length as a window on the data set and gradually sliding this window forward or backward, so as to realize the dynamic analysis of the data stream.

[0036] The electronic device can collect vehicle driving images through an out-vehicle camera, identify the lane boundary based on the vehicle driving images, and calculate the minimum distance between the vehicle center and the lane boundary. When the electronic device determines that the minimum distance between the vehicle center and the lane boundary is less than the distance threshold, it means that the vehicle crosses the vehicle boundary, that is, the vehicle straddles the line, and the electronic device records a vehicle straddling the line event. The electronic device counts the number of vehicle straddling the line events within a certain sliding time window. The number of vehicle straddling the line events is the number of times the vehicle straddles the line.

[0037] In the embodiments of the present application, the environmental parameters include a traffic complexity coefficient, and the traffic complexity coefficient has a positive correlation with the number of vehicles around the vehicle. The electronic device can identify the number of vehicles around the vehicle through a lidar, a millimeter-wave radar or an ultrasonic sensor, and calculate the traffic complexity coefficient according to the number of vehicles around the vehicle. For example, the calculation of the traffic complexity coefficient is shown in formula (1).

[0038] K = c1·N + c2 (1) Where K is the traffic complexity coefficient, N is the number of vehicles around the vehicle, and c1 and c2 are constants.

[0039] In other embodiments, the environmental parameters further include, but are not limited to, road sign information, traffic signals, pedestrian information, and other vehicle information.

[0040] S102. Calculate a first driver state score according to the first driving behavior parameter.

[0041] In this embodiment, the first driver state score is negatively correlated with driving safety. In a non-autonomous driving scenario, the electronic device calculates the first driver state score based on the number of times the driver is distracted or fatigued, the number of times the driver answers or makes a call, and the number of times the vehicle crosses a line within a certain period of time.

[0042] Exemplarily, the calculation of the first driver state score is shown in formula (2).

[0043] S1 = w 11 ·F1 + w 12 ·P1 + w 13 ·L (2) Where S1 is the first driver state score, F1 is the number of times the driver is distracted or fatigued in a non-autonomous driving scenario, P1 is the number of times the driver answers or makes a call in a non-autonomous driving scenario, L is the number of times the vehicle crosses a line, and w 11 、w 12 、w 13 are weight coefficients.

[0044] It can be understood that the weight coefficients reflect the influence degree of driving behavior parameters on the driver state. The weight coefficients can be set as needed. The weight coefficients can also be optimized through experimental data, or the machine learning model can be trained based on historical driving data, and the weight coefficients can be dynamically adjusted based on the machine learning model. The machine learning model includes, but is not limited to, random forest, support vector machine (SVM), and deep Q-network (DQN).

[0045] S103. Calculate the first takeover duration according to the first driver state score, the first environmental parameter, and the initial takeover duration.

[0046] Where the initial takeover duration can be set as needed. For example, the initial takeover duration is 10 seconds. The initial takeover duration can also be selected by the user according to the preset gear. For example, the electronic device prompts the user to select the preset gear through voice. The preset gears include the first gear, the second gear, and the third gear. The first gear sets the initial takeover duration to 10 seconds, the second gear sets the initial takeover duration to 15 seconds, and the third gear sets the initial takeover duration to 20 seconds.

[0047] In this embodiment, the first takeover duration is negatively correlated with both the first driver state score and the first environmental parameter. The first environmental parameter includes the first traffic complexity coefficient. The electronic device calculates the first takeover duration according to the first driver state score, the first traffic complexity coefficient, and the initial takeover duration.

[0048] Exemplarily, the calculation of the first takeover duration is shown in formula (3).

[0049] T1 = T0 – a1·S1 – b1·K1 (3) Wherein, S1 is the first driver state score, T0 is the initial takeover duration, T1 is the first takeover duration, K1 is the first traffic complexity coefficient, and a1 and b1 are weight coefficients.

[0050] It can be understood that since the first driver state score reflects the driver state in the non-autonomous driving scenario and the first environmental parameter reflects the environmental complexity in the non-autonomous driving scenario, the first takeover duration covers the influence of the driver state and environmental complexity in the non-autonomous driving scenario on the driver's takeover ability, can accurately reflect the driver's takeover ability, thus can meet the personalized takeover requirements, and further improve the safety and user experience of the vehicle.

[0051] S104. Determine whether the vehicle is in the autonomous driving mode.

[0052] If yes, execute steps S105 - S112; if no, return to execute steps S101 - S104.

[0053] In this embodiment, the driving mode of the vehicle includes the autonomous driving mode and the non-autonomous driving mode. The electronic device may include a driving mode control. When the electronic device detects that the user triggers the driving mode control, the electronic device determines that the driving mode of the vehicle is the autonomous driving mode. When the driving mode of the vehicle is the autonomous driving mode, when the electronic device detects that the user triggers the driving mode control again, the electronic device determines that the driving mode of the vehicle switches to the non-autonomous driving mode. When the vehicle is in the autonomous driving mode, the autonomous driving system takes control of the vehicle. When the vehicle is in the non-autonomous driving mode, the driver takes over the control of the vehicle.

[0054] S105. Issue a takeover prompt according to the first takeover duration.

[0055] In this embodiment, the takeover prompt is used to prompt the driver to take over the control of the vehicle. The takeover prompt duration is the first takeover duration.

[0056] S106. Obtain the second driving behavior parameter of the driver and the second environmental parameter of the vehicle.

[0057] Wherein, the second driving behavior parameter reflects the driving habits of the driver in the autonomous driving scenario, and the second environmental parameter reflects the environmental complexity around the vehicle in the autonomous driving scenario. The second driving behavior parameter includes the number of times the driver is distracted or fatigued in the autonomous driving scenario, the number of times the driver answers or makes a call in the autonomous driving scenario, the number of times the distraction prompt is upgraded, and the driver's takeover response duration.

[0058] It can be understood that when the electronic device detects a driver distraction event, the electronic device issues a distraction prompt, and the distraction prompt is used to prompt the driver to concentrate. The distraction prompt may include a first-level distraction prompt and a second-level distraction prompt. When the electronic device detects a driver distraction event, the distraction prompt is a first-level distraction prompt. When the electronic device detects that the driver has not regained attention within a certain time period, the distraction prompt is upgraded from a first-level distraction prompt to a second-level distraction prompt.

[0059] The takeover response duration refers to the duration from when the electronic device issues a takeover prompt to when the driver completes the takeover. When the electronic device detects that the driver turns the steering wheel or detects that the driver steps on the brake, the electronic device determines that the driver has completed the takeover.

[0060] In this embodiment, the electronic device can collect the second driving behavior parameters of the driver through the sensor module.

[0061] Exemplarily, the electronic device counts the number of times the distraction prompt is upgraded from a first-level distraction prompt to a second-level distraction prompt within a certain sliding time window. The electronic device can count the takeover response duration of the driver through a timer.

[0062] S107. Calculate the second driver status score according to the second driving behavior parameters.

[0063] In this embodiment, the second driver status score is negatively correlated with driving safety. In the autonomous driving scenario, the electronic device calculates the second driver status score according to the number of times the driver is distracted or fatigued, the number of times the driver answers or makes a call, the number of times the distraction prompt is upgraded, and the takeover response duration of the driver.

[0064] Exemplarily, the calculation of the second driver status score is shown in formula (4).

[0065] S2 = w 21 ·F2 + w 22 ·P2 + w 23 ·R + w 24 ·C (4) Wherein, S2 is the second driver status score, F2 is the number of times the driver is distracted or fatigued in the autonomous driving scenario, P2 is the number of times the driver answers or makes a call in the autonomous driving scenario, R is the takeover response duration of the driver, C is the number of times the distraction prompt is upgraded, and w 22 、w 23 、w 24 are weight coefficients.

[0066] S108. Calculate the second takeover duration according to the second driver status score, the second environmental parameters, and the first takeover duration.

[0067] In this embodiment, the second takeover duration is negatively correlated with both the second driver status score and the second environmental parameter. The second environmental parameter includes the second traffic complexity coefficient. The electronic device calculates the second takeover duration based on the second driver status score, the second traffic complexity coefficient, and the first takeover duration.

[0068] Exemplarily, the calculation of the second takeover duration is shown in formula (5).

[0069] T2 = T1 – a2·S2 – b2·K2 (5) Where S2 is the second driver status score, T1 is the first takeover duration, T2 is the second takeover duration, K2 is the second traffic complexity coefficient, and a2, b2 are weight coefficients.

[0070] It can be understood that since the second driver status score reflects the driver status in the autonomous driving scenario, the second environmental parameter reflects the environmental complexity in the autonomous driving scenario, and the first takeover duration covers the influence of the driver status and environmental complexity in the non-autonomous driving scenario on the driver's takeover ability, the second takeover duration covers the influence of the driver status and environmental complexity in both the autonomous driving scenario and the non-autonomous driving scenario on the driver's takeover ability, can more accurately reflect the driver's takeover ability, thus can meet the personalized takeover requirements, and further improve the safety and user experience of the vehicle.

[0071] S109. Calculate the minimum following distance according to the second driver status score, the second environmental parameter, and the following safety distance.

[0072] In this embodiment, the minimum following distance refers to the minimum distance between the center of the vehicle and the vehicle in front in the same lane. The minimum following distance is positively correlated with both the second driver status score and the second environmental parameter. The second environmental parameter includes the second traffic complexity coefficient. The electronic device calculates the minimum following distance based on the second driver status score, the second traffic complexity coefficient, and the following safety distance. Among them, the following safety distance can be set as needed.

[0073] Exemplarily, the calculation of the minimum following distance is shown in formula (6).

[0074] D1 = D0 + q1·S2 + r1·K2 (6) Where D1 is the minimum following distance, D0 is the following safety distance, S2 is the second driver status score, K2 is the second traffic complexity coefficient, and q1, r1 are weight coefficients.

[0075] It can be understood that since the second driver status score reflects the driver status in the autonomous driving scenario and the second environmental parameter reflects the environmental complexity in the autonomous driving scenario, the minimum following distance covers the impacts of the driver status and environmental complexity in the autonomous driving scenario on driving safety, thereby enhancing the safety of the vehicle.

[0076] S110. Calculate the maximum lane-keeping deviation value according to the second driver status score, the second environmental parameter, and the lane-keeping safety value.

[0077] In this embodiment, the maximum lane-keeping deviation value refers to the maximum distance between the vehicle center and the lane center line. The maximum lane-keeping deviation value has a negative correlation with both the second driver status score and the second environmental parameter. The second environmental parameter includes the second traffic complexity coefficient. The electronic device calculates the maximum lane-keeping deviation value according to the second driver status score, the second traffic complexity coefficient, and the lane-keeping safety value. Among them, the lane-keeping safety value can be set as needed.

[0078] Exemplarily, the calculation of the maximum lane-keeping deviation value is shown in formula (7).

[0079] M1 = M0 – q2·S2 – r2·K2 (7) Wherein, M1 is the maximum lane-keeping deviation value, M0 is the lane-keeping safety value, S2 is the second driver status score, K2 is the second traffic complexity coefficient, and q2, r2 are weight coefficients.

[0080] It can be understood that since the second driver status score reflects the driver status in the autonomous driving scenario and the second environmental parameter reflects the environmental complexity in the autonomous driving scenario, the maximum lane-keeping deviation value covers the impacts of the driver status and environmental complexity in the autonomous driving scenario on driving safety, thereby enhancing the safety of the vehicle.

[0081] S111. Control the vehicle to travel according to the maximum lane-keeping deviation value and the minimum following distance.

[0082] In this embodiment, the electronic device controls the distance between the vehicle center and the lane center line to be less than or equal to the maximum lane-keeping deviation value, and controls the distance between the vehicle center and the vehicle in front in the same lane to be greater than or equal to the minimum following distance.

[0083] S112. Determine whether the vehicle is in the autonomous driving mode.

[0084] If so, return to execute steps S106 - S112; if not, execute step S113, and then return to execute steps S101 - S104.

[0085] In this embodiment, when the electronic device determines that the driver has completed takeover, the electronic device switches the driving mode of the vehicle from the autonomous driving mode to the non-autonomous driving mode.

[0086] S113. Update the initial takeover duration using the second takeover duration.

[0087] In this embodiment, when the driving mode of the vehicle is switched from the autonomous driving mode to the non-autonomous driving mode, the electronic device replaces the initial takeover duration with the second takeover duration.

[0088] It can be understood that since the second takeover duration covers the influence of the driver's state and the environmental complexity in both the autonomous driving scenario and the non-autonomous driving scenario on the driver's takeover ability, when calculating the first takeover duration based on the first driver state score, the first environmental parameter, and the second takeover duration, the first takeover duration can also cover the influence of the driver's state and the environmental complexity in both the autonomous driving scenario and the non-autonomous driving scenario on the driver's takeover ability, can more accurately reflect the driver's takeover ability, thus can meet the personalized takeover requirements, and further improve the safety and user experience of the vehicle.

[0089] The vehicle control method has been specifically described above with the electronic device as the execution subject. The following briefly describes the hardware structure of the electronic device.

[0090] As Figure 2 shown, the electronic device 100 includes a processor 110, an external memory interface 120, an internal memory 121, and a sensor module 130.

[0091] The processor 110 is used to execute each function or step executed by the electronic device in the above embodiments. The processor 110 includes, but is not limited to, a central processing unit (CPU), a neural-network processing unit (NPU), and an application processor (AP).

[0092] The external memory interface 120 is used to connect to an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device. The external memory card communicates with the processor 110 through the external memory interface 120 to implement the data storage function.

[0093] The internal memory 121 is used to store computer-executable program codes, and the executable program codes include instructions. The processor 110 executes each function or step performed by the electronic device in the above embodiments by running the instructions stored in the internal memory 121. The internal memory 121 includes a program storage area and a data storage area. The program storage area can store an operating system, applications (APPs) required for at least one function, etc. The data storage area can store data created during the use of the electronic device. The internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, and a Universal Flash Storage (UFS), etc.

[0094] The sensor module 130 includes, but is not limited to, a camera, a Light Detection and Ranging (LiDAR), a millimeter-wave radar, an ultrasonic sensor, an Inertial Measurement Unit (IMU), an acceleration sensor, a distance sensor, a proximity light sensor, and an ambient light sensor.

[0095] It can be understood that the structure illustrated in this embodiment does not constitute a specific limitation on the electronic device. In other embodiments, the electronic device may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or have different component arrangements.

[0096] Each function or step performed by the electronic device in the above embodiments can also be applied to a vehicle, a chip, a computer-readable storage medium, or a computer program product.

[0097] Exemplarily, a vehicle includes a processor, and when the processor executes computer instructions, each function or step performed by the electronic device in the above embodiments is implemented. For example, the processor is configured to: when it is determined that the vehicle is in a non-autonomous driving mode, obtain a first driving behavior parameter of the driver and a first environmental parameter of the vehicle. Calculate a first driver status score according to the first driving behavior parameter. Calculate a first takeover duration according to the first driver status score, the first environmental parameter, and an initial takeover duration. When it is determined that the vehicle is in an autonomous driving mode, send a takeover prompt according to the first takeover duration.

[0098] A chip includes a processor and an interface circuit, and the processor is electrically connected to the interface circuit. The interface circuit can read the computer instructions stored in the memory and send the computer instructions to the processor. When the processor executes the computer instructions, each function or step performed by the electronic device in the above embodiments is implemented.

[0099] A computer-readable storage medium stores computer instructions, and when the processor executes the computer instructions, each function or step performed by the electronic device in the above embodiments is implemented.

[0100] A computer-readable storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. The computer-readable storage medium includes, but is not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), flash memory or other memory, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cassette, tape, magnetic disk storage or other magnetic storage device, or any other medium that can be used to store the desired information and can be accessed by a computer.

[0101] A computer program product includes computer instructions that, when executed by a processor, implement each of the functions or steps performed by the electronic device in the above embodiments.

[0102] The embodiments of the present application have been described in detail above in conjunction with the accompanying drawings. However, the present application is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present application within the scope of knowledge possessed by those of ordinary skill in the art.

Claims

1. A vehicle control method, characterized in that, The method includes: When it is determined that the vehicle is in a non-autonomous driving mode, obtaining the first driving behavior parameters of the driver and the first environmental parameters of the vehicle; Calculating a first driver status score according to the first driving behavior parameters; Calculating a first takeover duration according to the first driver status score, the first environmental parameters, and the initial takeover duration; When it is determined that the vehicle is in an autonomous driving mode, sending a takeover prompt according to the first takeover duration.

2. The method according to claim 1, wherein The method further includes: When it is determined that the vehicle is in the autonomous driving mode, updating the initial takeover duration.

3. The method according to claim 2, wherein The step of when it is determined that the vehicle is in the autonomous driving mode and updating the initial takeover duration includes: When it is determined that the vehicle is in the autonomous driving mode, obtaining the second driving behavior parameters of the driver and the second environmental parameters of the vehicle; Calculating a second driver status score according to the second driving behavior parameters; Calculating a second takeover duration according to the second driver status score, the second environmental parameters, and the first takeover duration; Updating the initial takeover duration using the second takeover duration.

4. The method according to claim 1, characterized in that, The method further includes: When it is determined that the vehicle is in the autonomous driving mode, obtaining the second driving behavior parameters of the driver and the second environmental parameters of the vehicle; Calculating a second driver status score according to the second driving behavior parameters; Calculating a minimum following distance according to the second driver status score, the second environmental parameters, and the following safety distance; Calculating a maximum lane keeping deviation value according to the second driver status score, the second environmental parameters, and the lane keeping safety value; Controlling the vehicle to travel according to the maximum lane keeping deviation value and the minimum following distance.

5. The method according to any one of claims 1 to 4, characterized in that, The step of calculating a first driver status score according to the first driving behavior parameters includes: S1 = w 11 ·F1 + w 12 ·P1 + w 13 ·L The step of calculating a first takeover duration according to the first driver status score, the first environmental parameters, and the initial takeover duration includes: T1 = T0 – a1·S1 – b1·K1 Among them, the first environmental parameter includes a first traffic complexity coefficient; S1 is the first driver state score, F1 is the number of times the driver is distracted or fatigued when the vehicle is in the non-autonomous driving mode, P1 is the number of times the driver answers or makes a call when the vehicle is in the non-autonomous driving mode, L is the number of times the vehicle straddles the line, T0 is the initial takeover duration, T1 is the first takeover duration, K1 is the first traffic complexity coefficient, w 11 、w 12 、w 13 、a1, b1 are weight coefficients.

6. The method according to claim 3, characterized in that, The step of calculating a second driver status score according to the second driving behavior parameters includes: S2 = w 21 ·F2 + w 22 ·P2 + w 23 ·R + w 24 ·C The step of calculating a second takeover duration according to the second driver status score, the second environmental parameters, and the first takeover duration includes: T2 = T1 – a2·S2 – b2·K2 Among them, the second environmental parameter includes a second traffic complexity coefficient; S2 is the second driver state score, F2 is the number of times the driver is distracted or fatigued when the vehicle is in the autonomous driving mode, P2 is the number of times the driver answers or makes a call when the vehicle is in the autonomous driving mode, R is the driver's takeover response duration, C is the number of times of distraction prompt upgrade, T1 is the first takeover duration, T2 is the second takeover duration, K2 is the second traffic complexity coefficient, w 22 、w 23 、w 24 、a2, b2 are weight coefficients.

7. The method according to claim 4, wherein The step of calculating a minimum following distance according to the second driver status score, the second environmental parameters, and the following safety distance includes: D1 = D0 + q1·S2 + r1·K2 Wherein, the second environmental parameters include a second traffic complexity coefficient; D1 is the minimum following distance, D0 is the following safety distance, S2 is the second driver status score, K2 is the second traffic complexity coefficient, and q1, r1 are weight coefficients.

8. The method according to claim 4, wherein The step of calculating a maximum lane keeping deviation value according to the second driver status score, the second environmental parameters, and the lane keeping safety value includes: M1 = M0 – q2·S2 – r2·K2 Among them, the second environmental parameter includes a second traffic complexity coefficient; M1 is the maximum lane-keeping deviation value, M0 is the lane-keeping safety value, S2 is the second driver state score, K2 is the second traffic complexity coefficient, and q2 and r2 are weight coefficients.

9. An electronic device, characterized in that, It includes a memory and a processor, and when the processor executes the computer instructions stored in the memory, it implements the method according to any one of claims 1-8.

10. A vehicle, characterized in that, It includes a processor, and the processor is configured to: When it is determined that the vehicle is in a non-autonomous driving mode, obtain the first driving behavior parameters of the driver and the first environmental parameters of the vehicle; Calculate the first driver state score according to the first driving behavior parameters; Calculate the first takeover duration according to the first driver state score, the first environmental parameters and the initial takeover duration; When it is determined that the vehicle is in an autonomous driving mode, issue a takeover prompt according to the first takeover duration.

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