Method for allocating driving right proportion of intelligent automobile human-machine co-driving considering driver system adaptability

Abstract

This application relates to a method for allocating driving rights in human-machine co-driving of intelligent vehicles, considering driver system adaptability. The method includes: when the vehicle is in human-machine co-driving mode, processing the state data of objects ahead of the vehicle and the driving state data of the first vehicle based on a preset risk assessment strategy to obtain an environmental risk assessment index; processing the driving state data of the second vehicle, predicted path data, and driver steering operation data based on a preset adaptability assessment strategy to obtain a driver system adaptability assessment index; processing the environmental risk assessment index and the driver system adaptability assessment index at a target time to obtain a driving rights allocation coefficient; and performing human-machine co-driving steering control of the vehicle based on the driving rights allocation coefficient. This method enables the driver to quickly and effectively complete autonomous driving takeover tasks when situational awareness is lacking, thereby improving driving safety.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a method for allocating the proportion of driving rights in human-machine co-driving of intelligent vehicles, taking into account the driver's system adaptability. Background Technology

[0002] With the rapid development of autonomous driving technology, current autonomous driving products are at Level 2 Partial Driving Automation (L2) and Level 3 Conditional Driving Automation (L3). At these two levels, although the system can complete some driving tasks, a human driver still needs to constantly monitor the risks during the vehicle's operation or take over control of the vehicle in an emergency when the automated system cannot handle the current situation.

[0003] In related technologies, a fixed-value human-machine co-driving strategy is used to allocate driving rights. However, this static allocation method has limitations. When the autonomous driving system issues a takeover request, the human driver may be focused on non-driving tasks and find it difficult to quickly return to normal driving mode. This lack of situational awareness makes it difficult for the driver to take over in a timely and effective manner, resulting in poor driving safety. Summary of the Invention

[0004] Therefore, it is necessary to provide a method for allocating the driving rights ratio of intelligent vehicles in human-machine co-driving, which takes into account the driver's system adaptability, in order to address the above-mentioned technical problems. This method can enable the driver to quickly and effectively complete the autonomous driving takeover task when the driver lacks situational awareness, thereby improving the safety of the driving process.

[0005] Firstly, this application provides a method for allocating driving rights in human-machine co-driving of intelligent vehicles, taking into account the driver system's adaptability, including:

[0006] When the vehicle is in human-machine co-driving mode, the environmental risk assessment index of the vehicle is obtained by processing the state data of the object in front of the vehicle and the driving state data of the first vehicle based on the preset risk assessment strategy.

[0007] Based on a preset fitness evaluation strategy, the driving status data of the second vehicle, the predicted path data, and the driver's steering operation data are processed to obtain the driver system fitness evaluation index.

[0008] The environmental risk assessment index and the driver system adaptability assessment index at the target time are processed to obtain the driving rights allocation coefficient; and the vehicle is subjected to human-machine co-driving steering control based on the driving rights allocation coefficient.

[0009] In one embodiment, the process of processing the vehicle's forward object state data and first vehicle driving state data based on a preset risk assessment strategy to obtain the vehicle's environmental risk assessment index includes:

[0010] The difference between the first longitudinal position data and the second longitudinal position data is determined to be position difference data; and the difference between the second longitudinal velocity data and the first longitudinal velocity data is determined to be velocity difference data.

[0011] The ratio of the position difference data to the speed difference data is determined as the collision time of the vehicle.

[0012] The ratio of the position difference data to the second longitudinal velocity data is determined as the headway of the vehicle.

[0013] Based on the collision time, the headway, and the speed difference data, environmental risk assessment indicators for the vehicle under the target state are determined.

[0014] In one embodiment, the process of processing the second vehicle driving state data, predicted path data, and driver steering operation data based on a preset fitness evaluation strategy to obtain driver system fitness evaluation indicators includes:

[0015] Based on the second vehicle driving status data and the predicted path data, the tracking error of the vehicle driving path relative to the predicted path data is determined. The tracking error includes lateral displacement tracking error and yaw angle tracking error.

[0016] Based on the lateral displacement tracking error and yaw angle tracking error, the human-machine driving target correlation evaluation index is determined;

[0017] The driver's willingness to take over is determined based on the front wheel angle data and the front wheel angle change rate.

[0018] Based on the human-machine driving goal relevance evaluation index and the driver takeover willingness evaluation index, the driver system adaptability evaluation index is determined.

[0019] In one embodiment, determining the tracking error of the vehicle's driving path relative to the predicted path data based on the second vehicle driving state data and the predicted path data includes:

[0020] Based on the predicted path data and the vehicle's longitudinal position data, the predicted lateral position data and predicted yaw angle data of the vehicle are determined.

[0021] The difference between the vehicle's lateral position data and the predicted lateral position data is determined as the vehicle's lateral position tracking error, and the difference between the yaw angle data and the predicted yaw angle is determined as the vehicle's yaw angle tracking error.

[0022] In one embodiment, processing the environmental risk assessment index and the driver system fitness assessment index at the target time to obtain the driving rights allocation coefficient includes:

[0023] Determine the first product value of the first coefficient and the environmental risk assessment index, and determine the first difference between the preset value and the first product value; determine the second product value of the second coefficient and the environmental risk assessment index;

[0024] The process involves determining the third product value of the third coefficient and the driver system fitness evaluation index, determining the first sum of the target time and the third product value, determining the second difference between the first sum and the fourth coefficient, determining the fourth product value of the second difference and the driver system fitness evaluation index, processing the fourth product value based on a preset exponential decay strategy to obtain an exponential function value, and determining the second sum of the exponential function value and the preset value.

[0025] Determine the ratio of the first difference to the second sum; determine the sum of the ratio and the second product as the driving rights allocation coefficient.

[0026] In one embodiment, the human-machine co-driving steering control of the vehicle based on the driving rights allocation coefficient includes:

[0027] The driving rights allocation coefficient for the self-vehicle is determined based on the aforementioned driving rights allocation coefficient.

[0028] The product of the driving rights allocation coefficient and the first front wheel steering angle data applied by the driver of the vehicle is determined as the front wheel steering angle data controlled by the driver.

[0029] Based on the predicted path data, the second front wheel steering angle data of the vehicle is determined, and the product of the autonomous vehicle driving rights allocation coefficient and the second front wheel steering angle data is determined as the front wheel steering angle data for vehicle control.

[0030] The sum of the front wheel steering angle data controlled by the driver and the front wheel steering angle data controlled by the vehicle is determined as the target front wheel steering angle data, and the vehicle is subjected to human-machine co-driving steering control based on the target front wheel steering angle data.

[0031] Secondly, this application also provides a driver-machine co-driving ratio allocation device for intelligent vehicles that considers the driver system's adaptability, comprising:

[0032] The first processing module is used to process the state data of the object in front of the vehicle and the driving state data of the first vehicle based on a preset risk assessment strategy when the vehicle is in human-machine co-driving mode, so as to obtain the environmental risk assessment index of the vehicle.

[0033] The second processing module is used to process the second vehicle driving status data, predicted path data and driver steering operation data based on a preset fitness evaluation strategy to obtain the driver system fitness evaluation index.

[0034] The third processing module is used to process the environmental risk assessment index and the driver system adaptability assessment index at the target time to obtain the driving rights allocation coefficient; and to perform human-machine co-driving steering control on the vehicle based on the driving rights allocation coefficient.

[0035] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0036] When the vehicle is in human-machine co-driving mode, the environmental risk assessment index of the vehicle is obtained by processing the state data of the object in front of the vehicle and the driving state data of the first vehicle based on the preset risk assessment strategy.

[0037] Based on a preset fitness evaluation strategy, the driving status data of the second vehicle, the predicted path data, and the driver's steering operation data are processed to obtain the driver system fitness evaluation index.

[0038] The environmental risk assessment index and the driver system adaptability assessment index at the target time are processed to obtain the driving rights allocation coefficient; and the vehicle is subjected to human-machine co-driving steering control based on the driving rights allocation coefficient.

[0039] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0040] When the vehicle is in human-machine co-driving mode, the environmental risk assessment index of the vehicle is obtained by processing the state data of the object in front of the vehicle and the driving state data of the first vehicle based on the preset risk assessment strategy.

[0041] Based on a preset fitness evaluation strategy, the driving status data of the second vehicle, the predicted path data, and the driver's steering operation data are processed to obtain the driver system fitness evaluation index.

[0042] The environmental risk assessment index and the driver system adaptability assessment index at the target time are processed to obtain the driving rights allocation coefficient; and the vehicle is subjected to human-machine co-driving steering control based on the driving rights allocation coefficient.

[0043] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0044] When the vehicle is in human-machine co-driving mode, the environmental risk assessment index of the vehicle is obtained by processing the state data of the object in front of the vehicle and the driving state data of the first vehicle based on the preset risk assessment strategy.

[0045] Based on a preset fitness evaluation strategy, the driving status data of the second vehicle, the predicted path data, and the driver's steering operation data are processed to obtain the driver system fitness evaluation index.

[0046] The environmental risk assessment index and the driver system adaptability assessment index at the target time are processed to obtain the driving rights allocation coefficient; and the vehicle is subjected to human-machine co-driving steering control based on the driving rights allocation coefficient.

[0047] The aforementioned method for allocating driving rights in human-machine co-driving intelligent vehicles, considering driver system adaptability, assesses the current environmental risks of the vehicle while it is in human-machine co-driving mode. It also determines driver system adaptability assessment indicators to evaluate the adaptability of both the driver and the system. At a target time, a driving rights allocation coefficient is determined based on a combination of these indicators, enabling adaptive adjustment of the coefficient. By incorporating driver system adaptability into the driving rights allocation process, the method maximizes the fulfillment of the driver's intentions while reducing the likelihood of human-machine conflict. Furthermore, the method uses the driving rights allocation coefficient at the target time for human-machine co-driving steering control, ensuring a smooth and timely transfer of driving rights to the human driver when necessary. This avoids safety issues caused by the driver losing awareness of the vehicle's status and surrounding environment while performing non-driving tasks, thus improving overall vehicle safety. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart illustrating a method for allocating driving rights in a human-machine co-driving system for intelligent vehicles, taking into account the driver system's adaptability, in one embodiment.

[0050] Figure 2 This is a schematic diagram illustrating a scenario in which the autonomous driving system of an intelligent vehicle fails and is taken over by a human-machine co-driving system in one embodiment;

[0051] Figure 3 This is a flowchart illustrating a method for allocating driving rights in a human-machine co-driving system for intelligent vehicles, taking into account the driver system's adaptability, in one embodiment.

[0052] Figure 4 This is a structural block diagram of a smart car human-machine co-driving driving rights allocation device that considers the driver system adaptability in one embodiment.

[0053] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0055] In one exemplary embodiment, such as Figure 1 As shown, a method for allocating driving rights in human-machine co-driving in intelligent vehicles, considering the driver system's adaptability, is provided. This embodiment illustrates the method's application to a terminal. It is understood that this method can also be applied to a server, or to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0056] Step 101: When the vehicle is in human-machine co-driving mode, process the state data of the object in front of the vehicle and the driving state data of the first vehicle based on the preset risk assessment strategy to obtain the environmental risk assessment index of the vehicle.

[0057] Among them, the human-machine co-driving mode refers to a driving mode in which the vehicle's intelligent system and the human driver cooperate to jointly control the vehicle. The preset risk assessment strategy can be an algorithm that assesses the risk indicators of the vehicle's environment. The "self-vehicle" refers to the vehicle as the primary viewpoint in the current scenario. The object in front of the self-vehicle can be an obstacle or another vehicle in front of it; the state data of the object in front of the self-vehicle can be the position data of the obstacle or the longitudinal position and speed data of the vehicle in front of it. The first self-vehicle driving state data can be the longitudinal position and speed data of the self-vehicle in its current state. Environmental risk assessment indicators can characterize the risk situation of the environment in which the vehicle is currently located.

[0058] Specifically, the terminal can acquire real-time data on the state of objects in front of the vehicle, the state of obstacles around the vehicle, and the vehicle's driving status from sensors in various parts of the vehicle during driving. The data on the state of objects in front of the vehicle can also include lateral position data, lateral velocity data, first longitudinal position data, and first longitudinal velocity data of the objects in front. The vehicle's driving status data can also include lateral position data, longitudinal position data, second lateral velocity data, second longitudinal position data, yaw angle data, driver steering operation data, and predicted path data from the vehicle system. While the vehicle is in motion, the terminal can determine the vehicle's driving mode based on the above data. For example, the driving mode can include autonomous driving mode, human-machine co-driving mode, and human driving mode, etc. Additionally, the terminal can also switch driving modes based on received driving mode switching signals.

[0059] The vehicle can acquire real-time data on the front of the vehicle in human-machine co-driving mode. Based on a preset risk assessment strategy, it can calculate the first longitudinal position and first longitudinal speed data of the object in front of the vehicle, as well as the second longitudinal position and second longitudinal speed data of the vehicle, to obtain the vehicle's environmental risk assessment index. Alternatively, when switching from autonomous driving mode to human-machine co-driving mode, the vehicle can calculate the first longitudinal position and first longitudinal speed data of the object in front of the vehicle, as well as the second longitudinal position and second longitudinal speed data of the vehicle, based on a preset risk assessment strategy to obtain the vehicle's environmental risk assessment index.

[0060] Furthermore, the switch from autonomous driving to human-machine co-driving mode can be triggered by the terminal detecting that the vehicle is in an emergency, or by the terminal detecting a switch-triggered signal from a human driver. The terminal can determine whether the vehicle's environment and its own status information meet preset emergency conditions by using the state data of objects in front of the vehicle, the state data of surrounding obstacles, and the vehicle's driving status data. If the emergency conditions are met, the vehicle is determined to be in an emergency. An emergency can be a long-tail scenario, which refers to extreme or complex driving scenarios that have a very low probability of occurring during vehicle operation but are diverse and difficult to predict.

[0061] For example, by default, the vehicle enters human-machine co-driving mode when it detects a long-tail scenario requiring intervention. Figure 2 , Figure 2 This diagram illustrates a scenario where the autonomous driving system of a smart car fails and a human-machine co-driving system takes over. Vehicle A in the diagram is the autonomous vehicle. When vehicle A encounters an unidentified obstacle directly in front, the autonomous driving system fails, requiring a human driver to intervene. At this point, the vehicle enters human-machine co-driving mode. The lines with arrows in the diagram represent possible driving routes in human-machine co-driving mode.

[0062] Optionally, the first longitudinal position data can be the position data of the obstacle or vehicle in front of the vehicle in the direction of travel, and the first longitudinal speed data can be the speed data of the obstacle or vehicle in front of the vehicle in the direction of travel. The second longitudinal position data can be the position data of the vehicle in the direction of travel, and the second longitudinal speed data can be the speed data of the vehicle in the direction of travel.

[0063] Step 102: Based on the preset fitness evaluation strategy, process the second vehicle driving status data, predicted path data and driver steering operation data to obtain the driver system fitness evaluation index.

[0064] The preset fitness assessment strategy can be an evaluation algorithm that assesses the fitness index between the driver and the vehicle system. The second vehicle driving state data can be the vehicle's lateral position data and yaw angle data. The predicted path data can be the driving data of the vehicle system's predicted path and the corresponding vehicle predicted operation data. The driver steering operation data can be the driver's steering operation data, such as the steering wheel angle and the rate of change of steering wheel angle. The driver system fitness evaluation index can be the fitness between the driver and the vehicle system.

[0065] Specifically, the terminal can calculate the human-machine driving goal relevance evaluation index based on the vehicle's lateral position data, yaw angle data, and predicted path data using a preset fitness assessment strategy; and calculate the driver's willingness to take over evaluation index based on the steering wheel angle and steering wheel angle change rate operated by the driver using the preset fitness assessment strategy. The terminal can then determine the driver system fitness evaluation index based on the human-machine driving goal relevance evaluation index and the driver's willingness to take over evaluation index.

[0066] Step 103: Process the environmental risk assessment index and driver system adaptability assessment index at the target time to obtain the driving rights allocation coefficient; and perform human-machine co-driving steering control on the vehicle based on the driving rights allocation coefficient.

[0067] The target time can be the equivalent of the starting time, and the starting time can be the time when switching to the human-machine co-driving mode. The driving rights allocation coefficient can be the driving rights coefficient assigned to the human driver in the human-machine co-driving mode.

[0068] Specifically, at the target time, the terminal processes environmental risk assessment indicators and driver system adaptability assessment indicators to obtain the human driver's driving rights allocation coefficient. The terminal can then determine the vehicle's driving rights allocation coefficient based on the human driver's vehicle driving rights allocation system. Based on both the human driver's and the vehicle's driving rights allocation coefficients, the terminal performs human-machine co-driving control on the vehicle's driving parameters, obtaining the controlled driving parameters. The vehicle can then perform steering control based on these controlled driving parameters.

[0069] The aforementioned method for allocating driving rights in human-machine co-driving intelligent vehicles, considering driver system adaptability, assesses the current environmental risks of the vehicle while it is in human-machine co-driving mode. It also determines driver system adaptability assessment indicators to evaluate the adaptability of both the driver and the system. At a target time, a driving rights allocation coefficient is determined based on a combination of these indicators, enabling adaptive adjustment of the coefficient. By incorporating driver system adaptability into the driving rights allocation process, the method maximizes the fulfillment of the driver's intentions while reducing the likelihood of human-machine conflict. Furthermore, the method uses the driving rights allocation coefficient at the target time for human-machine co-driving steering control, ensuring a smooth and timely transfer of driving rights to the human driver when necessary. This avoids safety issues caused by the driver losing awareness of the vehicle's status and surrounding environment while performing non-driving tasks, thus improving overall vehicle safety.

[0070] In an exemplary embodiment, the specific implementation process of step 101, "processing the state data of the object in front of the vehicle and the first driving state data of the vehicle based on a preset risk assessment strategy to obtain the environmental risk assessment index of the vehicle," may include:

[0071] The difference between the first longitudinal position data and the second longitudinal position data is determined as position difference data; the difference between the second longitudinal speed data and the first longitudinal speed data is determined as speed difference data; the ratio of the position difference data and the speed difference data is determined as the collision time of the vehicle; the ratio of the position difference data and the second longitudinal speed data is determined as the headway of the vehicle; based on the collision time, headway, and speed difference data, the environmental risk assessment index of the vehicle under the target state is determined.

[0072] The data includes the following: First longitudinal position data and first longitudinal velocity data of the object in front of the vehicle. Second longitudinal position data and second longitudinal velocity data of the vehicle. The object in front of the vehicle can be an obstacle or another vehicle in front of the vehicle. Position difference data represents the distance difference between the vehicle and the object in the direction of travel. Velocity difference data represents the speed difference between the vehicle and the object in the direction of travel. Collision time represents the time at which a collision may occur between the vehicle and the object in front. Headway time difference represents the time difference between the arrival of the vehicle at a certain point and the arrival of the front vehicle's headway at the same point. Target state represents the current state of the vehicle.

[0073] Specifically, the terminal can determine that the difference between the first longitudinal position data and the second longitudinal position data is the position difference data; and determine that the difference between the second longitudinal speed data and the first longitudinal speed data is the speed difference data; the ratio of the position difference data and the speed difference data is determined as the vehicle's time-to-collision (TTC), and the specific formula for calculating the collision time can be:

[0074]

[0075] Where TTC is the collision time, x p x is the first longitudinal position data of the object in front of the vehicle. e This refers to the vehicle's second longitudinal position data. This is the vehicle's second longitudinal velocity data. This is the first longitudinal velocity data for the object in front of the vehicle.

[0076] The terminal can determine the vehicle's time headway (TH) by the ratio of the position difference data and the second longitudinal velocity data. The specific formula for calculating the time headway is as follows:

[0077]

[0078] Where TH represents the headway.

[0079] The terminal can determine a first threshold and a first ratio to the headway, a second threshold, the product of gravitational acceleration and collision time, and a second ratio to the speed difference data and this product. The sum of the first and second ratios is then used as the environmental risk assessment index for the vehicle under the target condition. The first threshold can be determined based on the driver's reaction time; for example, the first threshold can be 1. The second threshold can be determined based on the vehicle's maximum deceleration; for example, the second threshold can be 1.5. The specific calculation formula for the environmental risk assessment index can be:

[0080]

[0081] Where EH is the environmental risk assessment index, and g is the gravitational acceleration at the current location.

[0082] In this embodiment, environmental risk assessment indicators are determined by using the driving status data of the vehicle itself and the driving status data of the vehicle in front, so as to assess the risk of the current environment in which the vehicle is located.

[0083] In an exemplary embodiment, the specific implementation process of step 102, "processing the second vehicle driving state data, predicted path data, and driver steering operation data based on a preset fitness evaluation strategy to obtain the driver system fitness evaluation index," may include:

[0084] Based on the second vehicle driving status data and predicted path data, the tracking error of the vehicle driving path relative to the predicted path data is determined; based on the lateral displacement tracking error and yaw angle tracking error, the human-machine driving target correlation evaluation index is determined; based on the front wheel steering angle data and the front wheel steering angle change rate, the driver takeover willingness evaluation index is determined; based on the human-machine driving target correlation evaluation index and the driver takeover willingness evaluation index, the driver system adaptability evaluation index is determined.

[0085] The second type of vehicle driving status data includes the vehicle's lateral position data and yaw angle data. Tracking errors include lateral displacement tracking error and yaw angle tracking error. Driver steering operation data includes the front wheel steering angle data applied by the driver and the rate of change of the front wheel steering angle. Predicted path data can be the path planned by the vehicle system based on the current environment. Tracking errors reflect the lateral displacement tracking error and yaw angle tracking error of both the human driver's operation of the vehicle and the system's operation of the vehicle. Yaw angle refers to the angle of rotation of the vehicle around a vertical axis (Z-axis) perpendicular to the ground, used to describe the vehicle's heading change. The vehicle's lateral position data is the position data in the direction perpendicular to the vehicle's direction of travel.

[0086] Specifically, the terminal can calculate the tracking error of the vehicle's lateral position data, yaw angle data, and predicted path data based on a preset fitness evaluation strategy.

[0087] The terminal can calculate the first squared value of the product of the first constant and the lateral displacement tracking error, calculate the second squared value of the product of the second constant and the yaw angle tracking error, calculate the sum of the first squared value and the second squared value, and determine the reciprocal of this sum as the target correlation (TC) index for human-machine driving. The specific calculation formula for the target correlation index for human-machine driving can be:

[0088]

[0089] TC is the human-machine driving target correlation evaluation index, α1 is the first constant, and α2 is the second constant. The first and second constants are used to adjust the relative weights of the vehicle's lateral position tracking error and yaw angle tracking error. For example, α1=1 and α2=0.2. It should be understood that the first and second constants are only used as examples and do not constitute specific limitations. Specific data can be determined according to specific application scenarios.

[0090] The terminal can determine the sum of the squares of the front wheel steering angle data within a preset time period up to the target time, and the sum of the squares of the rate of change of the front wheel steering angle within the preset time period up to the target time. It then determines the power of the third constant of the sum of these two sums, calculates the product of this power and the fourth constant, and uses the sum of this product and the fifth constant as the driver takeover intention (TA) evaluation index. The specific formula for calculating the driver takeover intention evaluation index can be:

[0091]

[0092] Where TA is the evaluation index of human driver's willingness to take over, and t0 is the current moment. The front wheel steering angle data at time t, Let β be the rate of change of the front wheel steering angle at time t, β1 be the fourth constant, β2 be the third constant, and β3 be the fifth constant. β1, β2, and β3 are used to adjust the magnitude of the human driver's willingness to take over evaluation index. β1, β2, and β3 are obtained through fitting from driver bench experiments and real-world perception assessments. For example, β1 = 0.9, β2 = 0.14, and β3 = 0.57. It should be understood that the examples here are for illustrative purposes only and do not constitute specific limitations.

[0093] The terminal can calculate the seventh power of the product of the sixth constant and the human-machine driving target correlation evaluation index, the ninth power of the product of the eighth constant and the driver takeover willingness evaluation index, and the product of the seventh and ninth power values. It can also calculate the negative exponential function value of this product with the natural constant e as its base. The difference between 1 and this negative exponential function value is determined as the driver system adaptation evaluation index (Driver Adaptation, DA). The specific calculation formula for the driver system adaptation evaluation index can be:

[0094]

[0095] Wherein, DA is the driver system fitness evaluation index, γ1 is the sixth constant, γ2 is the seventh constant, γ3 is the eighth constant, and γ4 is the ninth constant, which are obtained through parameter adjustment. γ1, γ2, γ3, and γ4 are used to adjust the influence of human-machine driving goal correlation and human driver takeover willingness on driver system fitness, and can be determined through driver bench tests. For example, γ1=2, γ2=1, γ3=3, γ4=3. It should be understood that the examples here are for illustrative purposes only and do not constitute specific limitations.

[0096] Optionally, the front wheel steering angle data can be determined based on the steering wheel angle data controlled by the driver. Specifically, it can be determined through steering ratio, nonlinear calibration, chassis testing, etc., without specific limitations here.

[0097] In this embodiment, by incorporating the driver system adaptability into the considerations for driving rights allocation, the realization of the driver's driving intentions can be guaranteed to the greatest extent possible, while reducing the probability of human-machine conflict.

[0098] In an exemplary embodiment, the specific implementation process of the step "determining the tracking error of the vehicle's driving path relative to the predicted path data based on the second vehicle driving state data and the predicted path data" may include:

[0099] Based on the predicted path data and the vehicle's longitudinal position data, the predicted lateral position data and predicted yaw angle data of the vehicle are determined; the difference between the vehicle's lateral position data and the predicted lateral position data is determined as the vehicle's lateral position tracking error, and the difference between the yaw angle data and the predicted yaw angle is determined as the vehicle's yaw angle tracking error.

[0100] The second vehicle driving status data includes the vehicle's lateral position data and yaw angle data.

[0101] Specifically, the vehicle controller can generate predicted path data based on the current environment. The terminal can determine the vehicle's predicted lateral position data based on the vehicle's current longitudinal position data and the predicted path data. The terminal can take the derivative of the predicted path data and input the longitudinal position data into this derivative to determine the vehicle's predicted yaw angle data. The terminal can determine the difference between the vehicle's lateral position data and the predicted lateral position data as the vehicle's lateral position tracking error, and determine the difference between the yaw angle data and the predicted yaw angle data as the vehicle's yaw angle tracking error. The formulas for calculating the lateral position tracking error and the yaw angle tracking error are as follows:

[0102]

[0103]

[0104] Among them, e y This refers to the lateral displacement tracking error of the vehicle. This represents the vehicle's yaw rate tracking error. e Here, f(x) represents the vertical location data, and f(x) represents the predicted path data. e (This refers to data for predicting lateral position.) This is the lateral position data of the vehicle. To predict the derivative of the path data, To predict yaw angle data.

[0105] In an exemplary embodiment, the specific implementation process of the step "processing the environmental risk assessment index and the driver system fitness assessment index at the target time to obtain the driving right allocation coefficient" may include:

[0106] The process involves determining the first product of the first coefficient and the environmental risk assessment indicator, and the first difference between the preset value and the first product value; determining the second product of the second coefficient and the environmental risk assessment indicator; determining the third product of the third coefficient and the driver system adaptability assessment indicator; determining the first sum of the target time and the third product value; determining the second difference between the first sum and the fourth coefficient; determining the fourth product of the second difference and the driver system adaptability assessment indicator; processing the fourth product value based on a preset exponential decay strategy to obtain an exponential function value; determining the second sum of the exponential function value and the preset value; determining the ratio of the first difference and the second sum; and determining the ratio and the sum of the second product value as the driving rights allocation coefficient.

[0107] Wherein, the target time is the time relative to the start time, the start time is the time when the voluntary vehicle sends the takeover request, and the preset exponential decay strategy can be a negative exponential function.

[0108] Specifically, the terminal can dynamically calculate the driving rights allocation coefficient of the human-machine co-driving system based on environmental risk assessment indicators and driver system adaptability assessment indicators. The specific calculation method is as follows: The terminal can determine the first product value of the first coefficient and the environmental risk assessment indicator, and determine the first difference between the preset value and the first product value; determine the second product value of the second coefficient and the environmental risk assessment indicator; determine the third product value of the third coefficient and the driver system adaptability assessment indicator; determine the first sum value of the target time and the third product value; determine the second difference between the first sum value and the fourth coefficient; determine the fourth product value of the second difference value and the driver system adaptability assessment indicator; process the fourth product value based on a preset exponential decay strategy to obtain an exponential function value; and determine the second sum value of the exponential function value and the preset value; determine the ratio of the first difference value and the second sum value; and determine the ratio and the sum of the second product value as the driving rights allocation coefficient.

[0109] Taking the moment when the vehicle system detects an obstacle and sends a takeover request to the human driver as the starting point, the changing pattern of the driving authority allocation coefficient for a human-machine co-driving system can be expressed as follows:

[0110]

[0111] in, In a human-machine co-driving system, λ1 represents the driver's driving rights allocation coefficient, λ2 represents the time relative to the start time, λ3 represents the third coefficient, and λ4 represents the fourth coefficient. These coefficients are obtained through parameter adjustment. λ1, λ2, λ3, and λ4 are used to adjust the relationship between the driver's driving rights allocation coefficient and the system's environmental risk and driver's system adaptability. They can be obtained through fitting using driver bench experiments and real-world experience assessments. For example, λ1=0.75, λ2=0.75, λ3=10, and λ4=13. It should be understood that this example is for illustrative purposes only and does not constitute a specific limitation.

[0112] EH determines the initial driving rights of the human-machine co-driving driving rights allocation coefficient, and DA determines the transfer rate of the human-machine co-driving driving rights allocation coefficient.

[0113] In this embodiment, the adjustment based on the human-machine co-driving driving right allocation method ensures dynamic allocation of driving rights while considering the environmental risk of the vehicle and the driver's adaptability to the human-machine co-driving system. When the environmental risk is high, the initial driving right allocated to the driver is higher, ensuring the driver can quickly exert control of the vehicle according to their intentions; conversely, the initial driving right is lower. When the driver's system adaptability is high, it indicates that the driver's goal is aligned with the system's, and the driver's willingness to control the vehicle is also high; in this case, the rate of increase in the driver's driving right is higher, and vice versa. The method considers the environmental hazards of the vehicle, ensuring the vehicle's adaptability to different dangerous scenarios. The driving right allocation ratio of the human-machine co-driving system is dynamically adjusted according to changes in scenario hazards. The method also considers the human driver's adaptability to the human-machine co-driving system, dynamically and adaptively adjusting the driver's driving right based on the level of human driver system adaptability. This ensures that the driver's state is taken into account in the driving right allocation method.

[0114] In an exemplary embodiment, the specific implementation process of the step "human-machine co-driving steering control of the vehicle based on the driving right ratio allocation coefficient" may include:

[0115] The driving rights allocation coefficient of the self-vehicle is determined based on the driving rights allocation coefficient; the product of the driving rights allocation coefficient and the first front wheel steering angle data applied by the self-vehicle driver is determined as the front wheel steering angle data controlled by the driver; the second front wheel steering angle data of the vehicle is determined based on the predicted path data, and the product of the self-vehicle driving rights allocation coefficient and the second front wheel steering angle data is determined as the front wheel steering angle data controlled by the vehicle; the sum of the front wheel steering angle data controlled by the driver and the front wheel steering angle data controlled by the vehicle is determined as the target front wheel steering angle data, and the vehicle is subjected to human-machine co-driving steering control based on the target front wheel steering angle data.

[0116] Specifically, the terminal can calculate the first front wheel steering angle data applied by the driver based on the driver's steering operation data. The terminal can determine the difference between 1 and the driving rights allocation coefficient as the driving rights allocation coefficient, and the product of the driving rights allocation coefficient and the first front wheel steering angle data applied by the driver as the driver-controlled front wheel steering angle data. Based on the predicted path data, the terminal determines the second front wheel steering angle data, and the product of the driving rights allocation coefficient and the second front wheel steering angle data is determined as the vehicle-controlled front wheel steering angle data. The sum of the driver-controlled front wheel steering angle data and the vehicle-controlled front wheel steering angle data is determined as the target front wheel steering angle data. The specific calculation formula for the target front wheel steering angle data can be:

[0117]

[0118] in, This refers to the steering angle data of the vehicle's second front wheel. The target is the front wheel steering angle data. The first front wheel steering angle data applied to the driver of the vehicle.

[0119] In this embodiment, the first front wheel steering angle data and the second front wheel steering angle data are dynamically allocated through the driving rights allocation coefficient, and the target front wheel steering angle data of the vehicle at different times are output to realize the human-machine co-driving steering control of the vehicle. Based on the driving rights allocation coefficient at the target time, the human-machine co-driving steering control of the vehicle ensures that when the autonomous driving system encounters a situation that requires human driver intervention, the driving rights can be smoothly transferred to the human driver in a timely manner. This avoids the safety problems caused by the driver losing situational awareness of the vehicle status and the surrounding environment due to engaging in non-driving tasks, and improves the safety of the vehicle during driving.

[0120] In one embodiment, such as Figure 3 As shown, Figure 3 This is a flowchart illustrating a method for allocating driving rights in human-machine co-driving of an intelligent vehicle, taking into account the adaptability of the driver system, in one embodiment. In an emergency scenario where a human driver needs to take over vehicle control in an intelligent connected vehicle with human-machine driving capabilities, the vehicle enters human-machine co-driving mode upon detecting the long-tail scenario requiring intervention. While in human-machine co-driving mode, the method dynamically controls the vehicle's steering based on a driving rights allocation coefficient. Specifically, this method may include the following steps:

[0121] Step 301: When an abnormal signal is detected in the vehicle's autonomous driving system, output a takeover request signal and enter the human-machine co-driving mode.

[0122] Step 302: Obtain the status data of the object in front of the vehicle, the status data of the surrounding obstacles, and the driving status data of the vehicle.

[0123] Step 303: Calculate the collision time and headway of the vehicle based on the state data of the object in front of the vehicle, the state data of the surrounding obstacles, and the driving state data of the vehicle.

[0124] Step 304: Calculate the environmental risk assessment index of the vehicle's environment based on the collision time and the distance to the front of the vehicle.

[0125] Step 305: Obtain the vehicle's lateral position data, lateral velocity data, yaw angle information, predicted path data, and driver steering operation data.

[0126] Step 306: Based on the second vehicle driving status data and the predicted path data, determine the tracking error of the vehicle driving path relative to the predicted path data.

[0127] Step 307: Determine the human-machine driving target correlation evaluation index based on tracking error.

[0128] Step 308: Determine the driver's willingness to take over based on the front wheel angle data and the front wheel angle change rate.

[0129] Step 309: Determine the driver system adaptability evaluation index based on the human-machine driving goal correlation evaluation index and the driver takeover willingness evaluation index.

[0130] Step 310: Process the environmental risk assessment index and driver system fitness assessment index at the target time to obtain the driving rights allocation coefficient.

[0131] Step 311: Perform human-machine co-driving steering control on the vehicle based on the driving rights allocation coefficient, and return to step 301.

[0132] In this implementation, the goal is to maximize the realization of the driver's driving intentions while minimizing the likelihood of human-machine conflict. Furthermore, by using a driving rights allocation coefficient based on the target time point for human-machine co-driving steering control, the system ensures that when the autonomous driving system requires human driver intervention, it can smoothly and promptly transfer driving rights to the human driver. This avoids safety issues caused by the driver losing awareness of the vehicle's status and surrounding environment due to non-driving tasks, thus improving vehicle safety during driving.

[0133] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0134] Based on the same inventive concept, this application also provides a device for allocating the proportion of driving rights in human-machine co-driving of an intelligent vehicle, considering driver system adaptability, to implement the aforementioned method for allocating the proportion of driving rights in human-machine co-driving of an intelligent vehicle. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the device for allocating the proportion of driving rights in human-machine co-driving of an intelligent vehicle, considering driver system adaptability, provided below can be found in the limitations of the method for allocating the proportion of driving rights in human-machine co-driving of an intelligent vehicle, considering driver system adaptability, as described above, and will not be repeated here.

[0135] In one exemplary embodiment, such as Figure 4 As shown, a driver-machine co-driving ratio allocation device 40 for intelligent vehicles considering driver system adaptability is provided, comprising: a first processing module 41, a second processing module 42, and a third processing module 43, wherein:

[0136] The first processing module 41 is used to process the state data of the object in front of the vehicle and the driving state data of the first vehicle based on a preset risk assessment strategy when the vehicle is in the human-machine co-driving mode, so as to obtain the environmental risk assessment index of the vehicle.

[0137] The second processing module 42 is used to process the second vehicle driving status data, predicted path data and driver steering operation data based on a preset fitness evaluation strategy to obtain the driver system fitness evaluation index.

[0138] The third processing module 43 is used to process the environmental risk assessment index and the driver system adaptability assessment index at the target time to obtain the driving right allocation coefficient; and to perform human-machine co-driving steering control on the vehicle based on the driving right allocation coefficient.

[0139] In one embodiment, the first processing module 41 is configured to determine that the difference between the first longitudinal position data and the second longitudinal position data is position difference data; and to determine that the difference between the second longitudinal velocity data and the first longitudinal velocity data is velocity difference data.

[0140] The ratio of position difference data to speed difference data is determined as the collision time of the vehicle;

[0141] The ratio of the position difference data and the second longitudinal velocity data is determined as the headway of the vehicle.

[0142] Based on collision time, headway, and speed difference data, environmental risk assessment indicators for vehicles under target conditions are determined.

[0143] In one embodiment, the second processing module 42 is used to determine the tracking error of the vehicle's driving path relative to the predicted path data based on the second vehicle driving state data and the predicted path data. The tracking error includes lateral displacement tracking error and yaw angle tracking error.

[0144] Based on the lateral displacement tracking error and the yaw angle tracking error, the human-machine driving target correlation evaluation index is determined;

[0145] The driver's willingness to take over was determined based on the front wheel angle data and the rate of change of the front wheel angle.

[0146] Based on the evaluation index of human-machine driving goal relevance and the evaluation index of driver takeover willingness, the evaluation index of driver system adaptability is determined.

[0147] In one embodiment, the second processing module 42 is used to determine the predicted lateral position data and predicted yaw angle data of the vehicle based on the predicted path data and the longitudinal position data of the vehicle.

[0148] The difference between the determined lateral position data and the predicted lateral position data is the vehicle's lateral position tracking error, and the difference between the determined yaw angle data and the predicted yaw angle is the vehicle's yaw angle tracking error.

[0149] In one embodiment, the third processing module 43 is used to determine the first product value of the first coefficient and the environmental risk assessment index, and to determine the first difference between the preset value and the first product value; and to determine the second product value of the second coefficient and the environmental risk assessment index.

[0150] The third product value of the third coefficient and the driver system fitness evaluation index is determined; the first sum of the target time and the third product value is determined; the second difference between the first sum and the fourth coefficient is determined; the fourth product value of the second difference and the driver system fitness evaluation index is determined; the fourth product value is processed based on the preset exponential decay strategy to obtain the exponential function value; and the second sum of the exponential function value and the preset value is determined.

[0151] Determine the ratio of the first difference to the second sum; determine the sum of the ratio and the second product as the driving rights allocation coefficient.

[0152] In one embodiment, the third processing module 43 is used to determine the driving right allocation coefficient of the vehicle based on the driving right allocation coefficient.

[0153] The product of the driving rights allocation coefficient and the first front wheel steering angle data applied by the driver of the vehicle is determined as the front wheel steering angle data controlled by the driver.

[0154] Based on the predicted path data, the second front wheel steering angle data of the vehicle is determined, and the product of the self-driving right allocation coefficient and the second front wheel steering angle data is determined as the front wheel steering angle data for vehicle control.

[0155] The sum of the front wheel steering angle data controlled by the driver and the front wheel steering angle data controlled by the vehicle is determined as the target front wheel steering angle data, and the vehicle is subjected to human-machine co-driving steering control based on the target front wheel steering angle data.

[0156] The various modules in the aforementioned intelligent vehicle human-machine co-driving driving rights allocation device, which considers driver system adaptability, can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0157] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for allocating driving rights in a human-machine co-driving system in an intelligent vehicle, taking into account the driver system's adaptability. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0158] Those skilled in the art will understand that Figure 5The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0159] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0160] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0161] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0162] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0163] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0164] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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, they should be considered to be within the scope of this application.

[0165] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for allocating driving rights in human-machine co-driving of intelligent vehicles, considering the driver system's adaptability, characterized in that... The method includes: When the vehicle is in human-machine co-driving mode, the environmental risk assessment index of the vehicle is obtained by processing the state data of the object in front of the vehicle and the driving state data of the first vehicle based on the preset risk assessment strategy. Based on a preset fitness evaluation strategy, the driving status data of the second vehicle, the predicted path data, and the driver's steering operation data are processed to obtain the driver system fitness evaluation index. The environmental risk assessment index and the driver system adaptability assessment index at the target time are processed to obtain the driving rights allocation coefficient; and the vehicle is subjected to human-machine co-driving steering control based on the driving rights allocation coefficient. The vehicle's forward object status data includes first longitudinal position data and first longitudinal velocity data of the object in front of the vehicle. The first vehicle driving status data includes second longitudinal position data and second longitudinal velocity data of the vehicle. The process of processing the forward object status data and the first vehicle driving status data based on a preset risk assessment strategy to obtain the vehicle's environmental risk assessment index includes: The difference between the first longitudinal position data and the second longitudinal position data is determined to be position difference data; and the difference between the second longitudinal velocity data and the first longitudinal velocity data is determined to be velocity difference data. The ratio of the position difference data to the speed difference data is determined as the collision time of the vehicle. The ratio of the position difference data to the second longitudinal velocity data is determined as the headway of the vehicle. Based on the collision time, the time distance to the front of the vehicle, and the speed difference data, the environmental risk assessment index of the vehicle under the target state is determined. The driver steering operation data includes the front wheel angle data and the rate of change of the front wheel angle applied by the driver. The second vehicle driving state data, predicted path data, and driver steering operation data are processed based on a preset fitness evaluation strategy to obtain driver system fitness evaluation indicators, including: Based on the second vehicle driving status data and the predicted path data, the tracking error of the vehicle driving path relative to the predicted path data is determined. The tracking error includes lateral displacement tracking error and yaw angle tracking error. Based on the lateral displacement tracking error and yaw angle tracking error, the human-machine driving target correlation evaluation index is determined; The driver's willingness to take over is determined based on the front wheel angle data and the front wheel angle change rate. Based on the human-machine driving goal relevance evaluation index and the driver takeover willingness evaluation index, the driver system adaptability evaluation index is determined; The target time is a time relative to the start time, where the start time is the moment the vehicle sends a takeover request. The environmental risk assessment index and the driver system adaptability assessment index at the target time are processed to obtain the driving rights allocation coefficient, including: Determine the first product value of the first coefficient and the environmental risk assessment index, and determine the first difference between the preset value and the first product value; determine the second product value of the second coefficient and the environmental risk assessment index; The process involves determining the third product value of the third coefficient and the driver system fitness evaluation index, determining the first sum of the target time and the third product value, determining the second difference between the first sum and the fourth coefficient, determining the fourth product value of the second difference and the driver system fitness evaluation index, processing the fourth product value based on a preset exponential decay strategy to obtain an exponential function value, and determining the second sum of the exponential function value and the preset value. Determine the ratio of the first difference to the second sum; determine the sum of the ratio and the second product as the driving rights allocation coefficient.

2. The method according to claim 1, characterized in that, The second vehicle driving status data includes the vehicle's lateral position data and yaw angle data. The step of determining the tracking error of the vehicle's driving path relative to the predicted path data based on the second vehicle driving status data and the predicted path data includes: Based on the predicted path data and the vehicle's longitudinal position data, the predicted lateral position data and predicted yaw angle data of the vehicle are determined. The difference between the vehicle's lateral position data and the predicted lateral position data is determined as the vehicle's lateral position tracking error, and the difference between the yaw angle data and the predicted yaw angle is determined as the vehicle's yaw angle tracking error.

3. The method according to claim 1, characterized in that, The method of performing human-machine co-driving steering control on the vehicle based on the driving rights allocation coefficient includes: The driving rights allocation coefficient for the self-vehicle is determined based on the aforementioned driving rights allocation coefficient. The product of the driving rights allocation coefficient and the first front wheel steering angle data applied by the driver of the vehicle is determined as the front wheel steering angle data controlled by the driver. Based on the predicted path data, the second front wheel steering angle data of the vehicle is determined, and the product of the autonomous vehicle driving rights allocation coefficient and the second front wheel steering angle data is determined as the front wheel steering angle data for vehicle control. The sum of the front wheel steering angle data controlled by the driver and the front wheel steering angle data controlled by the vehicle is determined as the target front wheel steering angle data, and the vehicle is subjected to human-machine co-driving steering control based on the target front wheel steering angle data.

4. The method according to claim 1, characterized in that, The first longitudinal position data is the position data of the obstacle or vehicle in front of the vehicle in the direction of travel; the first longitudinal speed data is the speed data of the obstacle or vehicle in front of the vehicle in the direction of travel. The second longitudinal position data is the position data of the vehicle in the direction of travel; the second longitudinal speed data is the speed data of the vehicle in the direction of travel.

5. A driver-machine co-driving ratio allocation device for intelligent vehicles that considers driver system adaptability, characterized in that, The device includes: The first processing module is used to process the state data of the object in front of the vehicle and the driving state data of the first vehicle based on a preset risk assessment strategy when the vehicle is in human-machine co-driving mode, so as to obtain the environmental risk assessment index of the vehicle. The second processing module is used to process the second vehicle driving status data, predicted path data and driver steering operation data based on a preset fitness evaluation strategy to obtain the driver system fitness evaluation index. The third processing module is used to process the environmental risk assessment index and the driver system adaptability assessment index at the target time to obtain the driving right allocation coefficient; and to perform human-machine co-driving steering control on the vehicle based on the driving right allocation coefficient. The vehicle's forward object status data includes first longitudinal position data and first longitudinal speed data of the object in front of the vehicle. The first vehicle driving status data includes second longitudinal position data and second longitudinal speed data of the vehicle. The first processing module is used to determine that the difference between the first longitudinal position data and the second longitudinal position data is position difference data; and to determine that the difference between the second longitudinal speed data and the first longitudinal speed data is speed difference data. The ratio of the position difference data to the speed difference data is determined as the collision time of the vehicle. The ratio of the position difference data to the second longitudinal velocity data is determined as the headway of the vehicle. Based on the collision time, the time distance to the front of the vehicle, and the speed difference data, the environmental risk assessment index of the vehicle under the target state is determined. The driver steering operation data includes the front wheel angle data and the front wheel angle change rate applied by the driver of the vehicle. The second processing module is used to determine the tracking error of the vehicle's driving path relative to the predicted path data based on the second vehicle driving state data and the predicted path data. The tracking error includes lateral displacement tracking error and yaw angle tracking error. Based on the lateral displacement tracking error and yaw angle tracking error, the human-machine driving target correlation evaluation index is determined; The driver's willingness to take over is determined based on the front wheel angle data and the front wheel angle change rate. Based on the human-machine driving goal relevance evaluation index and the driver takeover willingness evaluation index, the driver system adaptability evaluation index is determined; The target time is a time relative to the start time, where the start time is the time when the vehicle sends the takeover request. The third processing module is used to determine the first product value of the first coefficient and the environmental risk assessment index, and to determine the first difference between the preset value and the first product value; and to determine the second product value of the second coefficient and the environmental risk assessment index. The process involves determining the third product value of the third coefficient and the driver system fitness evaluation index, determining the first sum of the target time and the third product value, determining the second difference between the first sum and the fourth coefficient, determining the fourth product value of the second difference and the driver system fitness evaluation index, processing the fourth product value based on a preset exponential decay strategy to obtain an exponential function value, and determining the second sum of the exponential function value and the preset value. Determine the ratio of the first difference to the second sum; determine the sum of the ratio and the second product as the driving rights allocation coefficient.

6. The apparatus according to claim 5, characterized in that, The second vehicle driving status data includes the vehicle's lateral position data and yaw angle data. The first processing module is used to determine the vehicle's predicted lateral position data and predicted yaw angle data based on the predicted path data and the vehicle's longitudinal position data. The difference between the vehicle's lateral position data and the predicted lateral position data is determined as the vehicle's lateral position tracking error, and the difference between the yaw angle data and the predicted yaw angle is determined as the vehicle's yaw angle tracking error.

7. The apparatus according to claim 5, characterized in that, The third processing module is used to determine the self-vehicle driving right allocation coefficient based on the driving right allocation coefficient. The product of the driving rights allocation coefficient and the first front wheel steering angle data applied by the driver of the vehicle is determined as the front wheel steering angle data controlled by the driver. Based on the predicted path data, the second front wheel steering angle data of the vehicle is determined, and the product of the autonomous vehicle driving rights allocation coefficient and the second front wheel steering angle data is determined as the front wheel steering angle data for vehicle control. The sum of the front wheel steering angle data controlled by the driver and the front wheel steering angle data controlled by the vehicle is determined as the target front wheel steering angle data, and the vehicle is subjected to human-machine co-driving steering control based on the target front wheel steering angle data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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