Vehicle cooperative control method and system based on risk field and adaptive security domain

Abstract

The application discloses a vehicle cooperative control method and system based on a risk field and an adaptive safety domain, and relates to the technical field of intelligent driving cooperative control; data of an automatic driving vehicle at a road intersection without signals and vehicles is collected, a control area and a vehicle state are updated, and vehicles for cooperative decision-making in the current period are screened; short-time trajectories are predicted in combination with a vehicle kinematic model, conflict vehicles are screened according to trajectory space intersection; a speed risk field is constructed to solve a vehicle expected speed, and adaptive elliptical safety domains are generated for the conflict vehicles; the adaptive elliptical safety domains are used as vehicle collision avoidance constraints, the expected speed vector is used as a vehicle speed guiding target, a rolling optimization model is constructed, optimal control sequences of the vehicles are solved, and optimal acceleration and steering angular velocity in the current control period are output; the vehicle cooperative control method and system based on the risk field and the adaptive safety domain are used, so that the passing safety, efficiency and stability of the automatic driving vehicle in a complex intersection scene are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving cooperative control technology, and in particular to a vehicle cooperative control method and system based on risk field and adaptive safety domain. Background Technology

[0002] With the development of connected autonomous driving technology, autonomous vehicles can acquire information about the surrounding traffic environment through onboard sensors, vehicle-to-everything (V2X) communication, and vehicle-to-infrastructure (V2I) perception, and perform motion control and path planning based on vehicle status, road environment, and driving intentions. Intersections, as key areas where multi-directional traffic flows converge, diverge, and weave, involve complex traffic conflicts between vehicles, making them a critical scenario that needs to be addressed in the practical application of cooperative control technology for autonomous vehicles.

[0003] Traditional intersections typically rely on traffic lights, lane markings, and fixed traffic rules to organize vehicle movement. While this approach provides clear guidance for manually driven vehicles, in scenarios involving connected and autonomous vehicles operating in collaboration with each other, fixed signal phases and lane constraints are insufficient to fully adapt to the flexible, precise, and collaborative control characteristics of autonomous vehicles. This limits the vehicle's ability to dynamically adjust speed, lateral position, and travel path, thereby impacting the utilization rate of intersection road space and traffic efficiency.

[0004] To improve the efficiency of autonomous vehicles at intersections, existing vehicle cooperative control schemes for unsignalized intersections rely on roadside equipment to collect vehicle operation information, combine kinematic models to predict states, identify conflicts, and formulate traffic control strategies, effectively shortening vehicle waiting times. However, this scheme has significant drawbacks when adapted to unsignalized, laneless intersections: without lane constraints, vehicle interaction becomes more complex. Existing methods are either limited by preset trajectories and lane-based judgments, failing to fully utilize road space; or speed planning does not comprehensively assess surrounding traffic conditions and traffic risks, easily leading to delayed avoidance and uncoordinated driving; various fixed-form safety domains also struggle to balance safety and road utilization; furthermore, existing technologies lack unified area modeling and cooperative detour strategies for faulty or stopped vehicles, easily inducing congestion, sudden deceleration, and secondary conflicts. Summary of the Invention

[0005] The purpose of this invention is to provide a vehicle cooperative control method and system based on risk field and adaptive safety domain, which can comprehensively consider vehicle speed risk, adaptive safety domain, abnormal vehicle impact and multi-vehicle cooperative decision-making, thereby improving the traffic safety, efficiency and stability of autonomous vehicles in complex intersection scenarios.

[0006] To achieve the above objectives, this invention provides a vehicle cooperative control method based on a risk field and an adaptive safety domain, comprising the following steps: S1. Obtain information on autonomous vehicles and road environment entering the control area of ​​a no-signal, no-lane intersection, determine the vehicle kinematics model and kinematic constraints, update the control area information and vehicle state, and determine the set of vehicle information participating in cooperative control within the current control cycle. S2. Based on the set of vehicle information participating in the cooperative control within the current control cycle determined by S1, the vehicle kinematic model of S1 is used to predict the running trajectory of each vehicle in the preset prediction time domain, and the set of potential conflicting vehicle information is determined according to the spatial overlap relationship between the predicted vehicle trajectories. S3. Construct the vehicle speed risk field based on the vehicle information set in S1 to obtain the vehicle's expected speed vector within the current control cycle. S4. Based on the vehicle information in S1, the set of potential conflict vehicle information in S2, and the predicted vehicle trajectory, calculate the relative arrival time of the vehicle to the common conflict area, and construct the vehicle's adaptive elliptical safety domain based on the relative arrival time. S5. Based on the road environment information of S1, the vehicle kinematic model and kinematic constraints, the expected velocity vector of S3, and the vehicle's adaptive elliptical safety domain in S4, a rolling optimization model for vehicle cooperative control is constructed to solve the optimal control sequence of the vehicle in the prediction time domain. S6. Execute the first control quantity of the optimal control sequence in S5 within the current control cycle. After entering the next control cycle, reacquire the vehicle status and road environment information, and repeat S1-S5. Update the set of vehicles participating in cooperative control, the set of vehicles with potential conflicts, the speed risk field, the adaptive elliptical safety domain, and the optimal control sequence. Then execute the first control quantity in the updated optimal control sequence until the vehicle leaves the control area of ​​the unsignalized, laneless intersection.

[0007] Preferably, the autonomous vehicle information in S1 includes vehicle number, vehicle position, speed, acceleration, heading angle, steering angular velocity, vehicle length, vehicle width, vehicle wheelbase, entering direction, and target exit direction; Road environment information includes road boundaries, passable areas, restricted areas, and speed limits at intersections without traffic signals or lanes. The vehicle kinematics model is used to recursively predict the vehicle's operating state at the next moment based on the vehicle's current state and control variables; vehicle kinematic constraints are used to limit the vehicle's speed, acceleration, steering angle, and steering angular velocity to not exceeding the vehicle's permissible physical range and road traffic requirements; The update of control area information is to determine whether there are abnormal vehicles in the control area. If there are no abnormal vehicles, only the vehicle status is updated. If there are abnormal vehicles, the corresponding vehicles are marked as abnormal vehicles, and an abnormal area is generated based on the information of the abnormal vehicles with a preset safety margin. The control area information is then updated again.

[0008] Preferably, the specific process of S2 is as follows: S21. Generate the predicted trajectory for each vehicle in the prediction time domain, and expand the trajectory along the predicted trajectory to form the trajectory occupancy area according to the vehicle length, vehicle width and preset safety margin. S22, if the vehicle If the trajectory area occupied by a vehicle overlaps with the trajectory area occupied by another vehicle, or if the two have a common conflicting region in the prediction time domain, then the vehicle is identified as a vehicle. Potential conflict vehicles ; S23. For abnormal vehicles, normal motion prediction trajectories will no longer be generated. The area occupied by the vehicle in the prediction time domain will be regarded as an abnormal area, and the abnormal area will be used in the judgment of potential conflict vehicles.

[0009] Preferably, the specific process of S3 is as follows: S31, During the control cycle Inside, according to the vehicle The speed risk field is constructed, which includes the vehicle's intention potential field and the influence potential field of neighboring vehicles; S32, According to the vehicle The vehicle's intended travel and current location are used to determine its travel purpose. The desired velocity direction is used to obtain the potential field vector of the vehicle's intended velocity. S33. Based on the position, speed, heading angle, relative distance, relative azimuth angle and traffic density of surrounding vehicles, the local speed distribution of vehicles is corrected to obtain the influence potential field vector of neighboring vehicles. S34. Superimpose the potential field vectors of the vehicle's intention from S32 and S33 with the potential field vectors of the neighboring vehicles to obtain a composite potential field vector. Then, calculate the vehicle's potential field vector based on the composite potential field vector. The desired velocity vector.

[0010] Preferably, the specific content of S32 is as follows: S321, Regarding vehicles Based on its current location , direction of entry Drive out of the direction of the target Determine the direction of the vehicle's intended potential field. The specific expression is as follows: ; in, The intention direction function is used to determine the vehicle's desired direction of movement based on the vehicle's inbound direction and the target outbound direction; the intention direction function selects the corresponding direction sub-functions for going straight, turning left, or turning right based on the vehicle's intention. S322, Vehicles The potential field vector of the vehicle's intention for: ; in, Speed ​​limits are imposed on roads.

[0011] Preferably, the specific process of S33 is as follows: For the current vehicle Potential conflict vehicles During the control cycle Inside, the potential impact vector of vehicles involved in a conflict The specific formula is as follows: ; in, For potential conflict vehicles For vehicles The influence of potential field strength For potential conflict vehicles For vehicles The direction of influence.

[0012] Preferably, the specific process of S34 is as follows: S341, Let For vehicles During the control cycle The set of neighboring vehicles within the vehicle's range, the vehicle speed risk field is obtained by superimposing the potential field vector of the vehicle's own intention and the potential field vector of the neighboring vehicles to obtain the composite potential field vector. : ; S342, Synthesis Direction of Vehicle Speed ​​Risk Field as follows: ; in, and These are the composite potential vectors. exist direction and Components in direction; S343. Determine the vehicle based on the magnitude of the composite potential field vector and the road speed limit. Expected speed The specific constraints are as follows: ; S344, Vehicles The desired velocity vector As shown below: .

[0013] Preferably, the specific process of S4 is as follows: S41, Regarding the control cycle Vehicles inside and its potential conflict vehicles According to the vehicle and potential conflict vehicles Within the prediction time domain, the predicted trajectories are used to determine the common conflict area between the two. ; S42. In identifying areas of common conflict Then, calculate the vehicles separately. and potential conflict vehicles Trajectory distance from the current location to the common conflict zone along the predicted trajectory and ; S43. Calculate the vehicle distance based on the trajectory distance in S42. and potential conflict vehicles relative arrival time The specific calculation formula is as follows: ; in, and vehicles and potential conflict vehicles During the control cycle speed within, The preset minimum speed threshold; S44, Vehicles Targeting vehicles in potential conflict Calculate the semi-major axis of the adaptive elliptic safety region The specific calculation formula is as follows: ; in, To cover vehicles in potential conflict The basic semi-major axis required for the geometric outline, For the safety domain adjustment coefficient, For regularization parameters; S45. After determining the semi-long shaft in S44, according to the vehicle... The width, vehicle outline dimensions, and preset lateral safety margin determine the semi-short shaft. The semi-short wheelbase can cover the vehicle The minimum value of the geometric outline.

[0014] Preferably, the specific process of S5 is as follows: S51, the vehicle The acceleration and steering angular velocity in the prediction time domain constitute the vehicle control sequence; S52. Based on the vehicle control sequence in the prediction time domain in S51, construct a vehicle cost function that includes driving stability cost, acceleration comfort cost, steering comfort cost, and speed guidance cost. S53. Based on vehicle kinematic constraints, vehicle state and control quantity constraints, road boundary constraints, abnormal region constraints, and adaptive elliptical safety region constraints, determine the set of vehicle control sequences for the rolling optimization model. S54. In each control cycle, with the objective of minimizing the weighted sum of the vehicle cost functions in S52, the optimal control sequence is solved from the set of vehicle control sequences.

[0015] To achieve the above objectives, the present invention also provides a vehicle cooperative control system based on a risk field and an adaptive safety domain, including an intersection perception and vehicle state update module, a vehicle trajectory prediction and conflict identification module, a speed risk field and desired speed solution module, an adaptive elliptical safety domain construction module, and a periodic iterative scheduling module. The intersection perception and vehicle status update module collects vehicle and road environment information, establishes vehicle kinematics models, updates the control area and vehicle status, and selects vehicles for current collaborative control. The vehicle trajectory prediction and conflict identification module predicts short-term vehicle trajectories and identifies potential conflicting vehicles at intersections by analyzing the spatial overlap of trajectories. The speed risk field and expected speed solution module constructs the vehicle speed risk field, quantifies traffic risks, and outputs the expected speed for safe and efficient vehicle operation. The adaptive elliptical safety region construction module calculates the relative arrival time of conflicting vehicles at the intersection, dynamically adjusts the size of the elliptical safety region, and forms real-time safety constraints. The rolling time-domain collaborative optimization solution module integrates motion constraints, risk speed, and safety domain boundary to construct an optimization model and solve for the optimal control sequence of the vehicle. The periodic iterative scheduling module executes the optimal control quantity step by step and iteratively updates the strategy until the vehicle leaves the intersection.

[0016] Therefore, the vehicle cooperative control method and system based on risk field and adaptive safety domain described above, as presented in this invention, has the following advantages compared to the prior art: 1. This application uses a speed risk field to uniformly characterize the vehicle's travel intention, the influence of surrounding vehicle movement, and traffic density changes, enabling the vehicle to form a reasonable expected speed in a laneless continuous space; it describes the vehicle's dynamic collision avoidance constraints by using an adaptive elliptical safety domain based on relative arrival time, so that the safety domain can expand or shrink with the change of conflict risk between vehicles, reducing the lateral redundancy caused by the fixed circular safety domain, and avoiding insufficient safety margin of the fixed-size safety domain in high-risk scenarios. 2. This application models the static occupancy area or accident impact area of ​​the disabled vehicle or the vehicle stopped due to a collision, and incorporates its static occupancy impact into the potential conflict judgment and rolling optimization constraints, so that normal vehicles can slow down, detour or redistribute the passage space in advance, thereby reducing the risk of local congestion and secondary conflict. 3. This application improves intersection traffic efficiency, vehicle smoothness, and traffic flow stability by using rolling time-domain collaborative optimization to enable vehicles to dynamically update vehicle control quantities under the conditions of satisfying vehicle kinematic constraints, road boundary constraints, accident impact area constraints, and adaptive elliptical safety domain constraints.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating an application scenario of the vehicle cooperative control method based on risk field and adaptive safety domain of the present invention; Figure 2 This is a schematic diagram of the potential field direction of the vehicle's intention under different travel intentions in the vehicle cooperative control method based on risk field and adaptive safety domain of the present invention; Figure 3 This is a schematic diagram of the speed risk field of neighboring vehicles under different neighboring vehicle speeds in the vehicle cooperative control method based on risk field and adaptive safety domain of the present invention; Figure 4 This is a schematic diagram of the adaptive elliptical safety domain construction process of the vehicle cooperative control method based on risk field and adaptive safety domain of the present invention; Figure 5 This is a schematic diagram of the adaptive elliptical safety domain parameters based on relative arrival time for the vehicle cooperative control method based on risk field and adaptive safety domain of the present invention. Figure 6 This is a schematic diagram of the speed risk field construction process of the vehicle cooperative control method based on risk field and adaptive safety domain of the present invention; Figure 7 This is a schematic diagram of the speed risk field superposition and desired speed extraction of the vehicle cooperative control method based on the risk field and adaptive safety domain of the present invention; Figure 8 This is a schematic diagram of the multi-vehicle cooperative control process based on rolling time-domain updates for the vehicle cooperative control method based on risk field and adaptive safety domain of the present invention; Figure 9 This is a flowchart of the vehicle cooperative control system based on risk field and adaptive safety domain of the present invention. Detailed Implementation

[0019] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of this invention is usually placed when in use. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0020] Example like Figures 1-8 As shown, the vehicle cooperative control method based on risk field and adaptive safety domain of the present invention includes the following steps: S1. Obtain information on autonomous vehicles and road environment entering the control area of ​​a no-signal, no-lane intersection, determine the vehicle kinematics model and kinematic constraints, update the control area information and vehicle state, and determine the set of vehicle information participating in cooperative control within the current control cycle. The information for autonomous vehicles in S1 includes vehicle number, vehicle position, speed, acceleration, heading angle, steering angular velocity, vehicle length, vehicle width, vehicle wheelbase, direction of entry, and direction of exit. Road environment information includes road boundaries, passable areas, restricted areas, and speed limits at intersections without traffic signals or lanes. The vehicle kinematics model is used to recursively predict the vehicle's operating state at the next moment based on the vehicle's current state and control variables; vehicle kinematic constraints are used to limit the vehicle's speed, acceleration, steering angle, and steering angular velocity to not exceeding the vehicle's permissible physical range and road traffic requirements; A discrete vehicle kinematics model is used to describe the vehicle's state changes within the control area. The vehicle's position, velocity, heading angle, and steering angle are updated according to the vehicle's current state, acceleration, and steering angular velocity using the following formula: ; in, and For the vehicle's position coordinates, For vehicle speed, For vehicle steering angle, For the vehicle's heading angle, To accelerate the vehicle, The vehicle's steering angular velocity, This refers to the vehicle's wheelbase. The discrete time step; The specific vehicle kinematic constraints are: ; ; ; ; in, and These are the minimum and maximum permissible accelerations for the vehicle, respectively. Speed ​​limits for roads, The maximum permissible steering angular velocity of the vehicle. This is the maximum permissible steering angle for the vehicle. The control area information is updated to determine whether there are any abnormal vehicles (faulty vehicles or vehicles stopped due to collisions) within the control area. If no abnormal vehicles are found, only the vehicle status is updated. If abnormal vehicles are found, the corresponding vehicles are marked as abnormal vehicles, and an abnormal area (static occupancy area or accident-affected area) is generated based on the information of the abnormal vehicles with a preset safety margin. The control area information is then updated again. S2. Based on the set of vehicle information participating in cooperative control within the current control cycle determined by S1, the vehicle kinematic model of S1 is used to predict the running trajectory of each vehicle in the preset prediction time domain, and the set of potential conflicting vehicle information is determined according to the spatial overlap relationship between the predicted vehicle trajectories. ; The specific process of S2 is as follows: S21. Generate the predicted trajectory for each vehicle in the prediction time domain, and expand the trajectory along the predicted trajectory to form the trajectory occupancy area according to the vehicle length, vehicle width and preset safety margin. For a normal vehicle, its future trajectory within a preset prediction time domain is predicted based on the vehicle's current control cycle state and motion model. Within the preset prediction time domain, a short-term prediction of the vehicle's future position is made based on its current speed and direction of motion. Let the length of the prediction time domain be... ,vehicle During the control cycle The predicted trajectory within can be denoted as: Based on the vehicle length, vehicle width, and preset safety margin, the vehicle trajectory is extended to both sides by a preset width to form the area occupied by the vehicle trajectory. If the vehicle The area occupied by the trajectory of a certain vehicle overlaps with that of another vehicle, that is: ; Then the vehicle is identified as a vehicle. Potential conflict vehicles For abnormal vehicles (faulty vehicles or vehicles that have collided and stopped), the abnormal area caused by them is used as the trajectory occupancy area in the judgment of the vehicle that fell into the conflict. S22, if the vehicle If the trajectory area occupied by a vehicle overlaps with the trajectory area occupied by another vehicle, or if the two have a common conflicting region in the prediction time domain, then the vehicle is identified as a vehicle. Potential conflict vehicles ; S23. For abnormal vehicles, normal motion prediction trajectories will no longer be generated. The area occupied by the vehicle in the prediction time domain will be regarded as an abnormal area and the abnormal area will be used in the judgment of potential conflict vehicles. S3. Construct the vehicle speed risk field based on the vehicle information set in S1 to obtain the vehicle's expected speed vector within the current control cycle. The specific process of S3 is as follows: S31, During the control cycle Inside, according to the vehicle The speed risk field is constructed, which includes the vehicle's intention potential field and the influence potential field of neighboring vehicles; S32, According to the vehicle The vehicle's intended travel and current location are used to determine its travel purpose. The desired velocity direction is used to obtain the potential field vector of the vehicle's intended velocity. The specific details of S32 are as follows: S321, Regarding vehicles Based on its current location , direction of entry Drive out of the direction of the target Determine the direction of the vehicle's intended potential field. The specific expression is as follows: ; in, This is the traffic intention direction function, used to determine the vehicle's desired direction of movement based on the vehicle's entry direction and target exit direction. The traffic intention direction function selects the corresponding direction sub-function for going straight, turning left, or turning right based on the vehicle's traffic intention. For vehicles going straight, the traffic intention direction function directs the vehicle to travel along the target exit direction; for vehicles turning left or right, the traffic intention direction function gradually transitions the vehicle from the entry direction to the target exit direction. Vehicles with different entry directions can invoke the corresponding direction function through coordinate rotation, translation, or mirror transformation. S322, Vehicles The potential field vector of the vehicle's intention for: ; in, Speed ​​limits on roads; S33. Based on the position, speed, heading angle, relative distance, relative azimuth angle and traffic density of surrounding vehicles, the local speed distribution of vehicles is corrected to obtain the influence potential field vector of neighboring vehicles. The specific process of S33 is as follows: For the current vehicle Potential conflict vehicles During the control cycle Inside, the influence potential field vector of the adjacent vehicle The specific formula is as follows: ; in, For potential conflict vehicles For vehicles The influence of potential field strength For potential conflict vehicles For vehicles The direction of influence; the strength of the influence potential field is related to the speed of adjacent vehicles, relative distance, relative azimuth angle and traffic density, and the greater the speed of adjacent vehicles and the smaller the relative distance, the greater the influence strength. When constructing the influence potential field vector of neighboring vehicles, For potential conflict vehicles To the vehicle distance, For potential conflict vehicles Pointing to the vehicle The azimuth angle, and as a potential conflict vehicle For vehicles The direction of the potential field, i.e. , For potential conflict vehicles The heading angle, For potential conflict vehicles speed, For vehicle density, The potential field's effective range coefficient; potential conflict vehicles For vehicles Influence potential field strength It can be represented as: ; This formula is used to characterize vehicles in potential conflict. The speed, relative distance, relative azimuth angle, and traffic density affect the current vehicle's speed; the higher the speed of adjacent vehicles, the larger the influence range; the closer the distance between vehicles, the more obvious the influence intensity; the higher the traffic density, the smaller the range of the potential field of a single vehicle, in order to avoid excessive superposition of potential fields under high-density traffic conditions. The above angle quantities can be expressed in degrees or radians, but the units should be consistent in the same embodiment; when using radians, 90° corresponds to _____. ; S34. Superimpose the potential field vectors of the vehicle's intention from S32 and S33 with the potential field vectors of the neighboring vehicles to obtain a composite potential field vector. Then, calculate the vehicle's potential field vector based on the composite potential field vector. The desired velocity vector; The specific process of S34 is as follows: S341, Let For vehicles During the control cycle The set of potential conflicting vehicles within the system, the vehicle speed risk field is obtained by superimposing the potential field vector of the vehicle's own intention and the potential field vector of the neighboring vehicles to obtain the composite potential field vector. : ; S342, Synthesis Direction of Vehicle Speed ​​Risk Field as follows: ; in, and These are the composite potential vectors. exist direction and Components in direction; S343. Determine the vehicle based on the magnitude of the composite potential field vector and the road speed limit. Expected speed The specific constraints are as follows: ; S344, Vehicles The desired velocity vector As shown below: ; in, For the direction of the vehicle's desired speed, The desired speed of the vehicle; S4. Based on the vehicle information in S1, the set of potential conflict vehicle information in S2, and the predicted vehicle trajectory, calculate the relative arrival time of the vehicle to the common conflict area, and construct the vehicle's adaptive elliptical safety domain based on the relative arrival time. The specific process of S4 is as follows: S41, Regarding the control cycle Vehicles inside and its potential conflict vehicles According to the vehicle and vehicles Within the prediction time domain, the predicted trajectories are used to determine the common conflict area between the two. ; S42. In identifying areas of common conflict Then, calculate the vehicles separately. and vehicles Trajectory distance from the current location to the common conflict zone along the predicted trajectory and ; S43. Calculate the vehicle distance based on the trajectory distance in S42. and vehicles relative arrival time The specific calculation formula is as follows: ; in, and vehicles and potential conflict vehicles During the control cycle speed within, A preset minimum speed threshold is used to prevent the denominator from being zero when the vehicle is at low speed or stationary. S44. The adaptive elliptical safety region is centered on the geometric center of the corresponding vehicle, with its major axis aligned with the vehicle's direction of travel or front wheel steering, and its minor axis perpendicular to the major axis. Targeting vehicles in potential conflict Calculate the semi-major axis of the adaptive elliptic safety region The specific calculation formula is as follows: ; in, To cover vehicles in potential conflict The basic semi-major axis required for the geometric outline, For the safety domain adjustment coefficient, For regularization parameters; S45. After determining the semi-long wheelbase, according to the vehicle... i The width, vehicle outline dimensions, and preset lateral safety margin determine the semi-short shaft. The semi-short wheelbase can cover the vehicle The minimum value of the geometric outline.

[0021] S5. Based on the road environment information vehicle kinematic model and kinematic constraints in S1, the expected velocity vector in S3, and the adaptive elliptical safety domain of the vehicle in S4, a rolling optimization model for vehicle cooperative control is constructed to solve the optimal vehicle control sequence in the prediction time domain. Among them, the expected velocity vector is used to form the vehicle speed guidance target, the adaptive elliptical safety domain is used to form the collision avoidance constraints between vehicles, and the rolling optimization model is used to solve the vehicle control sequence in the prediction time domain. The specific process of S5 is as follows: S51, Use the preset prediction time domain length in S2. , will the vehicle The acceleration and steering angular velocity in the prediction time domain constitute the vehicle control sequence; vehicle During the control cycle The vehicle control sequence within is represented as follows: ; ; in, For vehicles During the control cycle The predictive control sequence within, For the first Vehicle control quantity at each predicted time. and These are the vehicle's acceleration and steering angular velocity, respectively. According to the vehicle during the control cycle The vehicle's position, speed, steering angle, and heading angle in the prediction time domain are obtained recursively from the current state, vehicle kinematics model, and control sequence. S52. Based on the vehicle control sequence in the prediction time domain from S51, construct a vehicle cost function including driving stability cost, acceleration ride comfort cost, steering ride comfort cost, and speed guidance cost; During the control cycle The cost function within is expressed as: ; in, , , and These are the weighting coefficients for driving stability cost, acceleration ride comfort cost, steering ride comfort cost, and speed steering cost, respectively, and they satisfy the following: ; The driving stability cost is determined based on the vehicle's predicted speed and predicted steering angle: ; in, The maximum allowable steering angle for the vehicle is used to limit the vehicle from making large turns at higher speeds; The acceleration smoothness cost is determined based on the acceleration change between adjacent prediction times: ; Among them, when hour, Pick up the vehicle The acceleration actually executed in the previous control cycle; The trade-off for steering smoothness is determined by the vehicle's steering angular velocity: ; in, This represents the maximum permissible steering angular velocity of the vehicle; the steering smoothness trade-off is used to suppress excessively rapid steering.

[0022] Within the current prediction time domain, the expected velocity obtained from S3 will be... and the direction of the desired velocity As a reference for vehicle speed guidance, the cost of speed guidance is expressed as: ; The first term is used to characterize the deviation between the predicted speed and the expected speed of the vehicle, and the second term is used to characterize the deviation between the predicted heading angle and the expected speed direction of the vehicle. S53. Based on vehicle kinematic constraints, vehicle state and control quantity constraints, road boundary constraints, abnormal region constraints, and adaptive elliptical safety region constraints, determine the set of vehicle control sequences for the rolling optimization model. Road boundary constraints are used to limit the predicted position and outline of vehicles in the prediction time domain, so that vehicles are always located in the passable area of ​​unsignalized, laneless intersections and do not overlap with road boundaries, no-entry areas or other impassable areas. Anomaly region constraints are used to limit the predicted trajectory and vehicle outline of normal vehicles in the prediction time domain so that they do not overlap with the anomaly regions formed by faulty vehicles or vehicles that have collided and stopped. Adaptive elliptical safety domain constraints are used to limit the relative position between a vehicle and its potential conflicting vehicles, so that the adaptive elliptical safety domains corresponding to the two do not overlap in the prediction time domain. The vehicle control sequence that satisfies vehicle kinematic constraints, vehicle state and control quantity constraints, road boundary constraints, abnormal region constraints, and adaptive elliptical safety region constraints constitutes the set of vehicle control sequences for the rolling optimization model in the current control cycle. S54, Let Given a set of vehicle control sequences, within each control cycle, the optimal control sequence is derived from this set, with the objective of minimizing the weighted sum of the vehicle cost functions of S52. Specifically, this is expressed as: ; in, To control the cycle A collection of vehicles participating in collaborative decision-making. For the joint control strategy corresponding to the vehicle set, For vehicles The cost function in the prediction time domain Control cycle The optimal control sequence obtained internally; S6. Execute the first control quantity of the optimal control sequence in S5 within the current control cycle. After entering the next control cycle, reacquire the vehicle status and road environment information, and repeat S1-S5. Update the set of vehicles participating in cooperative control, the set of vehicles with potential conflicts, the speed risk field, the adaptive elliptical safety domain, and the optimal control sequence. Then execute the first control quantity in the updated optimal control sequence until the vehicle leaves the control area of ​​the unsignalized, laneless intersection. The first control variable in the optimal control sequence is selected as the vehicle's control output in the current control cycle; the vehicle During the control cycle The optimal control quantity within the range is expressed as: ; in, For vehicles The optimal acceleration within the current control cycle For vehicles The optimal steering angular velocity within the current control cycle; After applying the optimal control quantity to the corresponding vehicle, the next control cycle begins. The vehicle status and road environment information are reacquired, and the abnormal vehicle set, neighbor vehicle set, potential conflict vehicle set, speed risk field, and adaptive elliptical safety domain are updated. The rolling optimization model is then reconstructed and solved until the vehicle leaves the control area of ​​the unsignalized, laneless intersection.

[0023] Figure 2 The three parts a, b, and c represent the expected speed and direction changes under the intention to go straight, turn right, and turn left, respectively. Figure 3 a, b, c, d, and e represent the changes in the range and intensity of the speed risk field as the speed of the adjacent vehicle gradually increases.

[0024] like Figure 9 As shown, the vehicle cooperative control system based on risk field and adaptive safety domain includes an intersection perception and vehicle state update module, a vehicle trajectory prediction and conflict identification module, a speed risk field and desired speed solution module, an adaptive elliptical safety domain construction module, and a periodic iterative scheduling module. The intersection perception and vehicle status update module collects vehicle and road environment information, establishes vehicle kinematics models, updates the control area and vehicle status, and selects vehicles for current collaborative control. The vehicle trajectory prediction and conflict identification module predicts short-term vehicle trajectories and identifies potential conflicting vehicles at intersections by analyzing the spatial overlap of trajectories. The speed risk field and expected speed solution module constructs the vehicle speed risk field, quantifies traffic risks, and outputs the expected speed for safe and efficient vehicle operation. The adaptive elliptical safety region construction module calculates the relative arrival time of conflicting vehicles at the intersection, dynamically adjusts the size of the elliptical safety region, and forms real-time safety constraints. The rolling time-domain collaborative optimization solution module integrates motion constraints, risk speed, and safety domain boundary to construct an optimization model and solve for the optimal control sequence of the vehicle. The periodic iterative scheduling module executes the optimal control quantity step by step and iteratively updates the strategy until the vehicle leaves the intersection.

[0025] In specific implementation, for the cooperative passage scenario of multiple autonomous vehicles in a signalless and laneless intersection, a signalless and laneless intersection refers to an intersection where no traffic lights are set up for phase control, and vehicles are not strictly constrained by fixed lane lines within the intersection control area. They can adjust their lateral position, driving trajectory and passage order within the continuous passable space defined by the road boundary. However, vehicles are still restricted by road boundaries, no-entry areas, central dividers or other preset road geometric constraints and cannot drive out of the passable area.

[0026] In one implementation, the traffic intention direction function can be constructed by partitioning the vehicle's position within the intersection control area. The partitions include at least an entry direction holding zone, a turning transition zone, and a target direction holding zone. Vehicles going straight maintain their target traffic direction within the entry direction holding zone and the target direction holding zone. Vehicles turning left and right gradually transition from their entry direction to their target exit direction within the turning transition zone. Based on the vehicle's entry direction and the target's exit direction obtained above, the vehicle can be identified. entry direction angle and the target exit angle ; where the entry direction angle Used to characterize the initial direction of motion of a vehicle when entering the controlled area of ​​an unsignalized, laneless intersection; target exit direction angle. Used to characterize the target's direction of motion when a vehicle leaves the controlled area; for vehicles traveling straight, the entry direction angle is the same as the exit direction angle. During the control cycle The intentional direction angle can be expressed as: ; For vehicles turning left and vehicles turning right, the vehicle The intended direction angle can be continuously transitioned from the entry direction angle to the target exit direction angle, and its expression is: ; in, For the entry direction angle Pointing at the target and driving out of the direction angle The directional angle difference is used to characterize the amount of directional change required for a vehicle to transition from the approach direction to the target exit direction; to avoid angles crossing 0°, 360° or , A sudden change in direction occurs at the boundary, resulting in a directional angular difference. The equivalent angle difference can be taken within a preset range according to the angle representation method. For example, in the degree system, it can be taken as [-180°, 180°], and in the radian system, it can be taken as [-180°, 180°]. ; For vehicles During the control cycle The steering process parameters are used to characterize the degree to which the vehicle moves from the approach direction to the target exit direction; the steering process parameters can be determined based on the longitudinal process of the vehicle in the local travel coordinate system: ; in, For vehicles The longitudinal progress in the local travel coordinate system corresponding to its travel direction. and These are the starting and ending positions of the steering transition zone, respectively.

[0027] Based on relative arrival time, an adaptive elliptical safety region is constructed for the vehicle. For vehicles The semi-major axis of the constructed elliptical safety region It can be determined according to the following formula: ; in, Let be the minimum semi-major axis of the elliptical model. The semi-major axis required to cover the vehicle's outline can be determined based on the vehicle's length, width, steering angle, and preset safety margin. As shown in the above formula, when the relative arrival time between vehicles decreases, the elliptical safety region expands along the vehicle's travel direction; when the relative arrival time increases, the elliptical safety region contracts.

[0028] Determining the semi-major axis Then, the semi-short wheelbase is determined based on the vehicle width, vehicle overall dimensions, and lateral safety margin. This ensures that the elliptical safety region can cover the corresponding vehicle outline; semi-short axis The minimum value that satisfies the condition that all corner points of the vehicle's outer contour are located within the elliptical safety domain can be selected. For disabled vehicles or vehicles stopped due to collisions, their safety domain is no longer dynamically updated based on the relative arrival time, but rather a static safety domain or accident impact area is generated based on their current position, attitude, vehicle size, and preset accident safety margin. The set of feasible joint control strategies for the multi-vehicle cooperative control rolling optimization model is jointly determined by vehicle kinematic constraints, road boundary constraints, static occupancy area or accident impact area constraints, and adaptive elliptical safety domain constraints. In the set of feasible joint control strategies, the cooperative control strategy within the current control cycle is solved with the goal of minimizing the vehicle cost function, so that the vehicle tracks the expected speed given by the speed risk field under the condition of satisfying safety constraints, and reduces rapid acceleration, rapid deceleration and frequent steering. In other implementations, the rolling optimization model can also be solved by centralized optimization, distributed optimization or game optimization.

[0029] When there are multiple feasible control strategies that meet the constraints, the final output strategy can be determined according to preset rules. The preset rules include at least one of the following: minimum overall cost, minimum maximum single vehicle cost, priority vehicle passage rule, or vehicle comfort priority rule. After obtaining the final control strategy, the vehicle control quantity corresponding to the current control cycle is used as the output.

[0030] The rolling time domain approach is adopted. In each control cycle, the vehicle state is reacquired, the abnormal vehicle set, the potential conflict vehicle set, the speed risk field and the adaptive elliptical safety domain are updated, and a new cooperative control strategy is solved. After entering the next control cycle, the above process is repeated until the vehicle leaves the control area of ​​the unsignalized laneless intersection.

[0031] In one optional implementation, the aforementioned cooperative control strategy is solved using a Pareto optimal multi-vehicle cooperative game method. The vehicles participating in cooperative decision-making within the current control cycle are considered as game participants, and the control sequences of each vehicle in the prediction time domain are considered as game strategies. The feasible joint control strategy set is jointly determined by vehicle kinematic constraints, road boundary constraints, abnormal region constraints, and adaptive elliptical safety region constraints. Joint control strategy The cost is denoted as Let the Pareto optimal joint control strategy be denoted as .

[0032] If no other feasible joint control strategy exists If the cost of all vehicles does not increase and the cost of at least one vehicle strictly decreases, then... The Pareto optimal joint control strategy was determined, namely: ; When multiple Pareto optimal joint control strategies exist, the final control strategy is determined based on at least one of the following: minimum overall cost, minimum maximum single-vehicle cost, priority vehicle passage, or priority of vehicle comfort. After obtaining the final control strategy, the first control variable in each vehicle control sequence is executed, and the game is re-solved in the next control cycle based on the updated vehicle state.

[0033] Therefore, the vehicle cooperative control method and system based on risk field and adaptive safety domain of the present invention can realize multi-vehicle cooperative passage in unsignalized and laneless intersections, and can be applied to complex traffic scenarios with abnormal vehicle occupancy. The speed risk field provides vehicles with the desired speed direction and desired speed magnitude in continuous space, the adaptive elliptical safety domain adjusts the safety constraints according to the relative arrival time between vehicles, and the multi-vehicle cooperative control optimization further makes a comprehensive trade-off between safety, efficiency and smoothness, thereby realizing the cooperative passage of autonomous vehicles in complex intersection scenarios.

[0034] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A vehicle cooperative control method based on risk field and adaptive safety domain, characterized in that, Includes the following steps: S1. Obtain information on autonomous vehicles and road environment entering the control area of ​​a no-signal, no-lane intersection, determine the vehicle kinematics model and kinematic constraints, update the control area information and vehicle state, and determine the set of vehicle information participating in cooperative control within the current control cycle. S2. Based on the set of vehicle information participating in the cooperative control within the current control cycle determined by S1, the vehicle kinematic model of S1 is used to predict the running trajectory of each vehicle in the preset prediction time domain, and the set of potential conflicting vehicle information is determined according to the spatial overlap relationship between the predicted vehicle trajectories. S3. Construct the vehicle speed risk field based on the vehicle information set in S1 to obtain the vehicle's expected speed vector within the current control cycle. S4. Based on the vehicle information in S1, the set of potential conflict vehicle information in S2, and the predicted vehicle trajectory, calculate the relative arrival time of the vehicle to the common conflict area, and construct the vehicle's adaptive elliptical safety domain based on the relative arrival time. S5. Based on the road environment information of S1, the vehicle kinematic model and kinematic constraints, the expected velocity vector of S3, and the vehicle's adaptive elliptical safety domain in S4, a rolling optimization model for vehicle cooperative control is constructed to solve the optimal control sequence of the vehicle in the prediction time domain. S6. Execute the first control quantity of the optimal control sequence in S5 within the current control cycle. After entering the next control cycle, reacquire the vehicle status and road environment information, and repeat S1-S5. Update the set of vehicles participating in cooperative control, the set of vehicles with potential conflicts, the speed risk field, the adaptive elliptical safety domain, and the optimal control sequence. Then execute the first control quantity in the updated optimal control sequence until the vehicle leaves the control area of ​​the unsignalized, laneless intersection.

2. The vehicle cooperative control method based on risk field and adaptive safety domain according to claim 1, characterized in that: The information for autonomous vehicles in S1 includes vehicle number, vehicle position, speed, acceleration, heading angle, steering angular velocity, vehicle length, vehicle width, vehicle wheelbase, direction of entry, and direction of exit. Road environment information includes road boundaries, passable areas, restricted areas, and speed limits at intersections without traffic signals or lanes. The vehicle kinematics model is used to recursively predict the vehicle's operating state at the next moment based on the vehicle's current state and control variables; vehicle kinematic constraints are used to limit the vehicle's speed, acceleration, steering angle, and steering angular velocity to not exceeding the vehicle's permissible physical range and road traffic requirements; The update of control area information is to determine whether there are abnormal vehicles in the control area. If there are no abnormal vehicles, only the vehicle status is updated. If there are abnormal vehicles, the corresponding vehicles are marked as abnormal vehicles, and an abnormal area is generated based on the information of the abnormal vehicles with a preset safety margin. The control area information is then updated again.

3. The vehicle cooperative control method based on risk field and adaptive safety domain according to claim 2, characterized in that: The specific process of S2 is as follows: S21. Generate the predicted trajectory for each vehicle in the prediction time domain, and expand the trajectory along the predicted trajectory to form the trajectory occupancy area according to the vehicle length, vehicle width and preset safety margin. S22, if the vehicle If the trajectory area occupied by a vehicle overlaps with the trajectory area occupied by another vehicle, or if the two have a common conflicting region in the prediction time domain, then the vehicle is identified as a vehicle. Potential conflict vehicles ; S23. For abnormal vehicles, normal motion prediction trajectories will no longer be generated. The area occupied by the vehicle in the prediction time domain will be regarded as an abnormal area, and the abnormal area will be used in the judgment of potential conflict vehicles.

4. The vehicle cooperative control method based on risk field and adaptive safety domain according to claim 3, characterized in that: The specific process of S3 is as follows: S31, During the control cycle Inside, according to the vehicle The speed risk field is constructed, which includes the vehicle's intention potential field and the influence potential field of neighboring vehicles; S32, According to the vehicle The vehicle's intended travel and current location are used to determine its travel purpose. The desired velocity direction is used to obtain the potential field vector of the vehicle's intended velocity. S33. Based on the position, speed, heading angle, relative distance, relative azimuth angle and traffic density of surrounding vehicles, the local speed distribution of vehicles is corrected to obtain the influence potential field vector of neighboring vehicles. S34. Superimpose the potential field vectors of the vehicle's intention from S32 and S33 with the potential field vectors of the neighboring vehicles to obtain a composite potential field vector. Then, calculate the vehicle's potential field vector based on the composite potential field vector. The desired velocity vector.

5. The vehicle cooperative control method based on risk field and adaptive safety domain according to claim 4, characterized in that: The specific details of S32 are as follows: S321, Regarding vehicles Based on its current location , direction of entry Drive out of the direction of the target Determine the direction of the vehicle's intended potential field. The specific expression is as follows: ； in, The intention direction function is used to determine the vehicle's desired direction of movement based on the vehicle's inbound direction and the target outbound direction; the intention direction function selects the corresponding direction sub-functions for going straight, turning left, or turning right based on the vehicle's intention. S322, Vehicles The potential field vector of the vehicle's intention for: ； in, Speed ​​limits are set for roads.

6. The vehicle cooperative control method based on risk field and adaptive safety domain according to claim 5, characterized in that: The specific process of S33 is as follows: For the current vehicle Potential conflict vehicles During the control cycle Inside, the potential impact vector of vehicles involved in a conflict The specific formula is as follows: ； in, For potential conflict vehicles For vehicles The influence of potential field strength For potential conflict vehicles For vehicles The direction of influence.

7. The vehicle cooperative control method based on risk field and adaptive safety domain according to claim 6, characterized in that: The specific process of S34 is as follows: S341, Let For vehicles During the control cycle The set of neighboring vehicles within the vehicle's range, the vehicle speed risk field is obtained by superimposing the potential field vector of the vehicle's own intention and the potential field vector of the neighboring vehicles to obtain the composite potential field vector. : ； S342, Synthesis Direction of Vehicle Speed ​​Risk Field as follows: ； in, and These are the composite potential vectors. exist direction and Components in direction; S343. Determine the vehicle based on the magnitude of the composite potential field vector and the road speed limit. Expected speed The specific constraints are as follows: ； S344, Vehicles The desired velocity vector As shown below: 。 8. The vehicle cooperative control method based on risk field and adaptive safety domain according to claim 7, characterized in that: The specific process of S4 is as follows: S41, Regarding the control cycle Vehicles inside and its potential conflict vehicles According to the vehicle and potential conflict vehicles Within the prediction time domain, the predicted trajectories are used to determine the common conflict area between the two. ; S42. In identifying areas of common conflict Then, calculate the vehicles separately. and potential conflict vehicles Trajectory distance from the current location to the common conflict zone along the predicted trajectory and ; S43. Calculate the vehicle distance based on the trajectory distance in S42. and potential conflict vehicles relative arrival time The specific calculation formula is as follows: ； in, and vehicles and potential conflict vehicles During the control cycle speed within, The preset minimum speed threshold; S44, Vehicles Targeting potential conflict vehicles Calculate the semi-major axis of the adaptive elliptic safety region The specific calculation formula is as follows: ； in, To cover vehicles in potential conflict The basic semi-major axis required for the geometric outline, For the safety domain adjustment coefficient, For regularization parameters; S45. After determining the semi-long shaft in S44, according to the vehicle... The width, vehicle outline dimensions, and preset lateral safety margin determine the semi-short shaft. The semi-short wheelbase can cover the vehicle The minimum value of the geometric outline.

9. The vehicle cooperative control method based on risk field and adaptive safety domain according to claim 8, characterized in that: The specific process of S5 is as follows: S51, Vehicle The acceleration and steering angular velocity in the prediction time domain constitute the vehicle control sequence; S52. Based on the vehicle control sequence in the prediction time domain in S51, construct a vehicle cost function that includes driving stability cost, acceleration comfort cost, steering comfort cost, and speed guidance cost. S53. Based on vehicle kinematic constraints, vehicle state and control quantity constraints, road boundary constraints, abnormal region constraints, and adaptive elliptical safety region constraints, determine the set of vehicle control sequences for the rolling optimization model. S54. In each control cycle, with the objective of minimizing the weighted sum of the vehicle cost functions in S52, the optimal control sequence is solved from the set of vehicle control sequences.

10. A vehicle cooperative control system based on a risk field and an adaptive safety domain, characterized in that: Applying any one of the vehicle cooperative control methods based on risk field and adaptive safety domain as described in claims 1-9, the system includes an intersection perception and vehicle state update module, a vehicle trajectory prediction and conflict identification module, a speed risk field and desired speed solution module, an adaptive elliptical safety domain construction module, and a periodic iterative scheduling module. The intersection perception and vehicle status update module collects vehicle and road environment information, establishes vehicle kinematics models, updates the control area and vehicle status, and selects vehicles for current collaborative control. The vehicle trajectory prediction and conflict identification module predicts short-term vehicle trajectories and identifies potential conflicting vehicles at intersections by analyzing the spatial overlap of trajectories. The speed risk field and expected speed solution module constructs the vehicle speed risk field, quantifies traffic risks, and outputs the expected speed for safe and efficient vehicle operation. The adaptive elliptical safety region construction module calculates the relative arrival time of conflicting vehicles at the intersection, dynamically adjusts the size of the elliptical safety region, and forms real-time safety constraints. The rolling time-domain collaborative optimization solution module integrates motion constraints, risk speed, and safety domain boundary to construct an optimization model and solve for the optimal control sequence of the vehicle. The periodic iterative scheduling module executes the optimal control quantity step by step and iteratively updates the strategy until the vehicle leaves the intersection.

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