Main vehicle operation control method based on road environment traffic risk field identification

Abstract

The invention provides a main vehicle operation control method based on road environment traffic risk field identification. Firstly, a vehicle-mounted platform of a main vehicle is used for collecting motion tracks and motion data of the main vehicle and vehicles with abnormal driving behaviors around the main vehicle in real time, and an abnormal driving behavior feature set is formed; secondly, the driving risk field is used for representing the driving risk of the vehicle with the abnormal driving behavior, and the real-time risk type is judged; thirdly, the risk type and the risk level of the abnormal driving behavior are divided through the interaction risk field potential. And finally, according to the identified abnormal driving behavior type and the risk type and the risk level thereof, the vehicle control system enters different working modes to control longitudinal and transverse movement of the main vehicle, so that the driving risk is avoided, and the driving safety is guaranteed. Potential risks caused by abnormal driving behaviors can be avoided in the driving process of the vehicle, the probability of occurrence of traffic accidents is reduced, and the driving safety of the vehicle is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of road traffic safety, and in particular to a main vehicle operation control method based on road environment traffic risk field identification. Background Art

[0002] With the rapid development of the automobile industry, research related to automobile operation control has received more and more attention. Automobile operation control has a vital impact on the safety and stability of the driving process, which is also a key research direction. During the driving process, vehicles often encounter vehicles with abnormal driving behaviors such as lane changing, overtaking, and deceleration. These vehicles bring great road traffic risks and seriously interfere with the driving safety of the main vehicle. Therefore, it is necessary to carry out vehicle operation control research based on the road environment traffic risk field to identify driving risks for these abnormal driving behavior vehicles.

[0003] Most of the existing vehicle operation control methods control the vehicle to accelerate, decelerate, turn and other operations according to a certain strategy based on the vehicle's position, speed and other driving status information, so that the vehicle can travel at the desired trajectory and speed to achieve the purpose of safe driving. However, most of the existing related vehicle control research is based on one-way lateral control of the vehicle or longitudinal control of the vehicle. These studies often only consider the operating status of the main vehicle and ignore the risks brought by vehicles with abnormal driving behaviors in the road environment. In addition, there are few strategies in the prior art that use driving risk fields and risk level tables to perform lateral and longitudinal coordinated control of vehicles. There are problems such as inaccurate identification of abnormal driving behavior types, unclear identification of driving risk levels, and poor control effects. Therefore, the present invention proposes a main vehicle operation control method based on the identification of road environment traffic risk fields. On the basis of considering the road environment traffic risk field, the main vehicle is controlled laterally and longitudinally to avoid risks, which is of great significance to the development of vehicle control technology. Summary of the invention

[0004] The present invention discloses a main vehicle operation control method based on road environment traffic risk field identification, aiming to solve the problems in the prior art of few strategies for lateral and longitudinal coordinated control of vehicles using driving risk fields and risk level tables, inaccurate identification of abnormal driving behavior types, unclear identification of driving risk levels, and poor control effects.

[0005] To achieve the above object, the present invention discloses a main vehicle operation control method based on road environment traffic risk field identification, which specifically includes the following steps:

[0006] S1. Identify the motion trajectory of the vehicle with abnormal driving behavior through the vehicle-mounted platform of the host vehicle and form a set of abnormal driving behavior features;

[0007] The abnormal driving behaviors include: lane changing (CL), overtaking (OT), and deceleration (SC);

[0008] The abnormal driving behavior feature set includes: abnormal driving behavior details (ADB), abnormal driving behavior location details (L-ADB); wherein ADB includes: abnormal driving behavior type (ADB-T), start time (ADB-t), end time (ADB-s); L-ADB includes: longitude (L-ADB-l), latitude (L-ADB-la), speed (L-ADB-v), time (L-ADB-t), vehicle body spacing size (L-ADB-sb), field potential of interactive risk field (L-ADB-P f );

[0009] Furthermore, when the vehicle is in the process of changing lanes (CL), ADB-T=CL. In L-ADB, except for the time period of ADB-t<L-ADB-t<ADB-s, the L-ADB-l and L-ADB-la of the leading vehicle are located in different lanes from the main vehicle, and the L-ADB-v and L-ADB-sb of the two vehicles remain unchanged; within the time period of ADB-t<L-ADB-t<ADB-s, the L-ADB-l and L-ADB-la of the leading vehicle are offset and L-ADB-v gradually increases or decreases, and the L-ADB-sb of the main vehicle and the leading vehicle continue to increase or decrease until the leading vehicle and the main vehicle are located in the same lane;

[0010] That is, when the main vehicle and the front vehicle are in different lanes, the front vehicle deviates laterally and accelerates or decelerates, and then the distance between the two vehicles increases or decreases and they are in the same lane, it is identified as abnormal lane change driving behavior;

[0011] Furthermore, when the vehicle is in overtaking (OT), ADB-T=OT, and in L-ADB, except for the time period of ADB-t<L-ADB-t<ADB-s, the L-ADB-l and L-ADB-la of the rear vehicle are located in different lanes from the main vehicle, and the L-ADB-v and L-ADB-sb of the two vehicles remain unchanged; in the time period of ADB-t<L-ADB-t<ADB-s, the L-ADB-l and L-ADB-la of the rear vehicle are offset and L-ADB-v gradually increases, and thereafter the L-ADB-sb of the two vehicles continuously decreases and when the L-ADB-la of the main vehicle and the rear vehicle are the same, the L-ADB-sb of the two vehicles reaches the minimum, and thereafter the L-ADB-sb of the two vehicles continuously increases, at which time the rear vehicle surpasses the main vehicle and the L-ADB-l and L-ADB-la of the rear vehicle are offset and L-ADB-v gradually increases until the two vehicles are located in the same lane;

[0012] That is, when the main vehicle and the rear vehicle are in different lanes, the rear vehicle's trajectory shifts laterally and accelerates. The distance between the two vehicles reaches the minimum, and then the distance between the two vehicles increases and the rear vehicle's trajectory shifts laterally and accelerates. At this time, the rear vehicle and the main vehicle are in the same lane, which is identified as abnormal overtaking driving behavior;

[0013] Furthermore, when the vehicle is in deceleration (SC), ADB-T = SC. In L-ADB, within the time period of ADB-t < L-ADB-t < ADB-s, the L-ADB-l and L-ADB-la of the main vehicle and the front vehicle remain unchanged and the L-ADB-l of the two vehicles are the same. At this time, the L-ADB-sb of the two vehicles continuously decreases and the L-ADB-l remains unchanged.

[0014] That is, when the main vehicle and the front vehicle are in the same lane, the distance between the two vehicles decreases sharply, and the movement trajectory of the front vehicle does not deviate, it is identified as abnormal deceleration driving behavior;

[0015] S2. Using the vehicle's driving risk field to identify the risk of vehicles with abnormal driving behaviors around the main vehicle;

[0016] The driving risk field refers to the kinetic energy field of the degree of influence of moving objects on the road on driving risk, wherein the driving risk field of a single vehicle is defined as a single-vehicle risk field, and the driving risk field between the main vehicle and the vehicles around the main vehicle is defined as an interactive risk field;

[0017] The specific expression of the bicycle risk field is as follows:

[0018]

[0019]

[0020]

[0021] Where: x i ,y i is the horizontal and vertical coordinates of a single vehicle i, x k ,y k are the horizontal and vertical coordinates of point k; the x-axis is along the direction of vehicle travel, and the y-axis is along the direction perpendicular to the direction of vehicle travel; P f is the potential danger level of a single vehicle i to the surrounding environment when it moves. The greater the field strength, the greater the potential danger. The direction of the field strength is related to r ik Same; r ik is the pseudo distance between a single vehicle i and point k; P i is the road condition influencing factor, M i is the equivalent mass of a single vehicle i; m i is the real mass of a single vehicle i; V i is the speed of a single vehicle i; θ i is the movement direction of a single vehicle i and r ik The angle of iis the acceleration of a single vehicle i; λ1 is 0.056; λ2 is -0.169; λ3 is 2; α is 0.029; τ is 2.717;

[0022] The specific expression of the interactive risk field is as follows:

[0023]

[0024]

[0025] Q j =M j exp(-λ4V j cosφ j )

[0026]

[0027] Where: x i ,y i is the horizontal and vertical coordinates of vehicle i around the main vehicle, x j ,y j is the horizontal and vertical coordinates of the main vehicle j; P f-ji F is the risk potential energy of the interaction risk field between the main vehicle j and the vehicles i around the main vehicle. The greater the risk potential energy, the greater the potential danger between the main vehicle and the surrounding vehicles. ji is the field force on the main vehicle; Q j is the characteristic coefficient of vehicle j; P i is (x i ,y i ) is the road condition influencing factor, M i M is the equivalent mass of vehicle i around the main vehicle; j is the equivalent mass of vehicle j; V i V is the movement speed of vehicle i around the main vehicle; j is the moving speed of vehicle j; r ji is the pseudo distance between the main vehicle j and the surrounding vehicles i; θ i The movement direction of vehicle i around the main vehicle and r ji The angle of j The moving direction of vehicle j and r ji The angle of λ1 is 0.056; λ2 is -0.169; λ3 is 2; λ4 is -0.029; α is 0.029; τ is 2.717;

[0028] Predict the potential risk between the host vehicle and surrounding vehicles through an interactive risk field;

[0029] S3. Identify the risk type of abnormal driving behavior based on the risk type information and establish a risk type table;

[0030] The risk type information (ADB-RTI) includes: risk type (ADB-RTI-rt), interactive risk field potential threshold (ADB-RTI-Pi (i=1, 2, 3));

[0031] The risk type results are divided into three types: mild (SL), average (OR), and severe (SE);

[0032] In the risk type information (ADB-RTI), ADB-RTI-P1=0.0003 is the field potential threshold for classifying ADB-RTI-rt=SL; ADB-RTI-P2=0.003 is the field potential threshold for classifying ADB-RTI-rt=OR; ADB-RTI-P3=0.03 is the field potential threshold for classifying ADB-RTI-rt=SE;

[0033] The risk types are shown in Table 1:

[0034] Table 1 Risk type table

[0035]

[0036] S4. Prepare a risk level table based on the risk type table, and inform the main vehicle of the real-time risk according to the risk level table;

[0037] In the risk level information (ADB-RLI) in the risk level table, the risk level (ADB-RLI-rl) includes: low level (LL), intermediate level (IL), and high level (HL);

[0038] The risk level table includes: risk level matrix table, risk level acceptance table;

[0039] The risk level standards are specified according to the risk level matrix table, and the risk level matrix table is shown in Table 2 below;

[0040] Table 2 Risk level matrix

[0041]

[0042] Explain the current risk level and corresponding acceptance status according to the risk level acceptance criteria table. The risk level acceptance criteria table is as follows Table 3;

[0043] Table 3 Risk level acceptance criteria

[0044] Risk Level Fields Acceptance level Countermeasures Low Level ADB-RLI-rl=LL Acceptable Be prepared to adjust response measures at any time intermediate ADB-RLI-rl=IL Conditional acceptance Certain measures need to be taken advanced ADB-RLI-rl=HL Unacceptable Immediate response measures are needed

[0045] According to the risk level table, the risk type and risk level of the abnormal driving behavior feature set are identified. The risk level table is shown in Table 4 below;

[0046] Table 4 Risk level table

[0047]

[0048] S5. Use vehicle control systems to avoid driving risks;

[0049] The vehicle control system includes: a lateral controller and a longitudinal controller; wherein the lateral controller is a steering wheel steering controller, which is controlled by the steering angle and speed of the steering wheel; the longitudinal controller is an accelerator pedal controller and a brake pedal controller, which are controlled by the degree of opening and closing of the pedals;

[0050] The vehicle control system uses different working modes to control the controller, and the working modes are divided into A gear, B gear, and C gear;

[0051] Specifically, when the vehicle control system enters the A gear working mode, the longitudinal controller actively brakes and controls, and the corresponding risk level is low; when the vehicle control system enters the B gear working mode, the lateral controller actively steers and controls, and the corresponding risk level is medium; when the vehicle control system enters the C gear working mode, the corresponding risk level is high, and the vehicle enters the pre-collision active braking state, decelerating and stopping with maximum braking intensity.

[0052] The working mode response is shown in Table 5 below;

[0053] Table 5 Working mode response table

[0054]

[0055] That is, when ADB-T = CL, OT, SC, in L-ADB, take the time period of ADB-t<L-ADB-t<ADB-s, if L-ADB-P f =ADB-RTI-P1, the vehicle control system is in a critical state of entering the working mode, triggering an audible and visual alarm to remind the driver to drive carefully;

[0056] If ADB-RTI-P1<L-ADB-P f <ADB-RTI-P2, then ADB-RTI-rt=SL, ADB-RLI-rl=LL, indicating that L-ADB-sb of the two vehicles is greater than the latest braking point (L-ADB-LPTB). If the driver has no effective input, the vehicle control system enters the A gear working mode, that is, the longitudinal controller actively brakes;

[0057] If ADB-RTI-P2≤L-ADB-P f<ADB-RTI-P3, then ADB-RTI-rt=OR, ADB-RLI-rl=IL, indicating that the L-ADB-sb of the two vehicles is less than the latest braking point (L-ADB-LPTB), greater than the latest turning point (L-ADB-LPTS), and there is enough space in the target lane. If the driver has no effective input, the vehicle control system enters the B gear working mode, and the lateral controller actively controls the steering;

[0058] If ADB-RTI-P3≤L-ADB-P f , then ADB-RTI-rt=SE, ADB-RLI-rl=HL, indicating that the L-ADB-sb of the two vehicles is less than the latest turning point L-ADB-LPTS and there is not enough space in the target lane. The vehicle control system enters the C gear working mode, and the vehicle enters the pre-collision active braking state, decelerating and stopping with the maximum braking intensity;

[0059] The control principle is to give priority to braking with the longitudinal controller. When the longitudinal active braking alone cannot avoid danger, the lateral controller is used for active steering. At the same time, the driver's control right has a higher priority. That is, during active braking or active steering, if the driver accelerates, brakes or steers, etc., which are inconsistent with the system's working logic, the system will immediately quit working and return the control right to the driver.

[0060] Preferably, the longitudinal controller in the vehicle control system performs active braking control on the vehicle, and the longitudinal controller adopts a hierarchical control design, including: an upper controller based on the LQR algorithm, solving the desired acceleration for the lower control input; a lower controller based on the PID control method of acceleration feedback; wherein the upper controller includes two steps of establishing a state space equation and calculating the desired acceleration;

[0061] The step of establishing the state space equation is to use the current speed v of the vehicle x and the expected speed v des As input, derive the current acceleration a of the vehicle x and the expected acceleration a des The state space equations of

[0062] The step of calculating the expected acceleration is based on the established state space equation to obtain the expected acceleration a of the vehicle. des ;

[0063] The current speed v of the vehicle x and the expected speed v des The speed error ε can be obtained v for:

[0064] ε v =v des -v x

[0065] During the longitudinal control process, the current acceleration and the expected acceleration of the vehicle satisfy the first-order inertia relationship, and the transfer function expression is:

[0066]

[0067] Where: K l is the control system gain; τ del The system delay time.

[0068] State quantity X = [ε v a x ] T , control quantity u=[a des ], the state space equation is:

[0069]

[0070] Where:

[0071] The quadratic performance function expression is:

[0072]

[0073] Where: R=[r]

[0074] Combining the LQR theoretical knowledge, the optimal feedback of the control system can be obtained as:

[0075] U=-R -1 B T P

[0076] The P matrix can be obtained by solving the Riccati equation PA+A T P-PBR -1 B T P+Q=0 is obtained.

[0077] Feedback gain matrix K = [k1 k2], expressed as:

[0078] K=lqr(A,B,Q,R)

[0079] Where:

[0080] Therefore, combining the feedback gain matrix K = [k1 k2] and the system state quantity, the expected acceleration a of the vehicle can be obtained: des for a des =-(k1ε v +k2a x ), where k1 and k2 are the first and second values ​​of the feedback gain matrix K respectively; ε v is the speed error; ax is the current acceleration.

[0081] Furthermore, by adopting a lower-layer controller of a PID control method based on acceleration feedback, control information of an accelerator pedal controller and a brake pedal controller is output, and driving at a desired speed is achieved through an accelerator-brake calibration table;

[0082] The lower controller of the acceleration feedback-based PID control method is used to convert the desired acceleration output by the upper controller into the desired throttle opening and brake pressure.

[0083] First, the desired acceleration output by the upper controller is controlled by the "feedforward + feedback" method, and the PID control algorithm is introduced for adjustment; secondly, the acceleration is converted into the desired throttle opening and brake pressure by means of a longitudinal calibration table. Among them, the longitudinal calibration table characterizes the vehicle's response characteristics to the throttle and brake at different speeds. Its input is the speed and the desired acceleration, and the output is the throttle opening or brake pressure value. After the data is interpolated, a calibration table containing all throttles and brakes is generated, so that at any given speed and acceleration, there is a unique throttle and brake value corresponding to it.

[0084] Further, the engine, transmission, and brake system of the host vehicle respond based on the output information of the longitudinal controller and the throttle-brake calibration table;

[0085] The main vehicle engine, transmission, and brake system respond based on the information output by the longitudinal controller and the throttle-brake calibration table. In the specific implementation method, the throttle and pedal controller information output by the longitudinal controller and the throttle-brake calibration table is output to the engine, and the engine is connected to the transmission; the output brake pedal controller information is output to the brake device.

[0086] Preferably, the lateral controller in the vehicle control system performs active steering control on the vehicle, and the lateral controller is a feedforward LQR control;

[0087] Furthermore, the vehicle error parameter e is calculated based on the expected trajectory and vehicle state parameters through the trajectory tracking error model. rr and trajectory curvature k r ;

[0088] When tracking the desired path, there is a lateral error e d and heading error Affects tracking performance. Lateral error e d is the distance from the center of mass of the vehicle to the center of the desired path trajectory, and the heading error is the heading angle θ of the vehicle body and the heading angle θ of the tangent line at the center point of the desired trajectory r The difference is The vehicle's center of mass sideslip angle β is small during driving, and the body heading angle θ is proportional to the vehicle's yaw angle. The relationship is: The heading error is considered Define point Q as the projection point of the vehicle's center of mass on the desired trajectory, then the speed of moving along the desired trajectory at point Q is:

[0089]

[0090] Where: e d is the lateral error; is the heading error; k r is the curvature of the desired trajectory at point Q; V x is the longitudinal velocity; V y is the lateral speed.

[0091] The spatial state expression of the trajectory tracking error model is obtained as follows: in

[0092] u=[δ]

[0093] Where: is the first-order derivative of the error parameter; e rr is the error parameter; e d is the lateral error; is the heading error; is the first-order derivative of the lateral error; is the first-order derivative of the heading error; is the heading angle θ of the tangent line at the center point of the desired trajectory r The first derivative of ; δ is the front wheel steering angle; m is the vehicle mass; V x is the longitudinal velocity; a and b are the distances from the center of mass of the vehicle to the front and rear axles respectively; C f and C r are the cornering stiffness of the front and rear tires respectively; I z is the moment of inertia of the vehicle's center of mass rotating around the Z axis.

[0094] Furthermore, the LQR controller obtains the controller's optimal control feedback matrix and the optimal front wheel steering angle control law;

[0095] First, the Euler method is used to discretize the LQR. Second, the performance objective function of the system is constructed and the optimal control input u(k) is established to minimize the performance objective function J. Finally, the optimal control of the LQR controller is u(k) = -Kx(k), where K = (R + B T PB) -1 B TPA is the optimal control feedback matrix of the LQR controller, and P is the following Riccati equation PA+A T P-PBR -1 BP T +Q=0, Q is a semi-positive definite real symmetric constant matrix, R is a positive definite real symmetric constant matrix, R=[1]. Finally, in the linear system controlled by the LQR controller, u=δ, x=e rr , the optimal front wheel steering angle control law of the system is δ(k)=-Ke rr (k).

[0096] Furthermore, through the trajectory curvature k r The feedforward controller is designed with the feedback matrix K to calculate the front wheel steering angle increment δ f The feedforward PSO-LQR lateral controller outputs the final front wheel steering angle control value u(k) to the steering wheel angle controller of the vehicle to complete the lateral control.

[0097] In order to eliminate the steady-state error, a feedforward controller needs to be added to the LQR controller. At this time, the optimal front wheel angle control law is u(k) = -Ke rr (k)+δ f , where δ f is the front wheel steering angle increment output by the feedforward controller; when the system is stable, The steady-state error is:

[0098]

[0099] Where: k1 and k3 are the first and third values ​​of the LQR optimal control feedback matrix K respectively.

[0100] Let e d = 0 After eliminating the steady-state error, the feedforward controller outputs the front wheel angle increment δ f for

[0101] When the system is stable, the steady-state error of the heading error is Can make the heading error θ-θ r = 0, then the optimal front wheel steering angle control law u(k) is

[0102] Beneficial Effects of the Invention

[0103] 1. The present invention utilizes the traffic risk field of the road environment to effectively identify vehicles with abnormal driving behaviors around the main vehicle, determines the driving risk according to the risk level table and informs the main vehicle of the real-time risk, and designs the lateral and longitudinal coordinated control system of the main vehicle based on this, which solves the problems of inaccurate identification of the type of vehicles with abnormal driving behaviors, unclear identification of driving risk levels, and poor control system effects in the prior art. The control method of the present invention can effectively improve the stability and safety of the lateral and longitudinal control of the vehicle.

[0104] 2. The longitudinal controller of the present invention adopts a hierarchical control design. Compared with the existing longitudinal control method, the present design first solves the expected acceleration for the lower-level control input based on the upper-level controller of the LQR algorithm, and then designs the lower-level controller of the PID control method based on acceleration feedback. The throttle-brake calibration table is designed to output the control information of the throttle and brake pedal controllers, so that the main vehicle can travel at the expected speed, thereby achieving the longitudinal control goal, which can reduce the difficulty of longitudinal control and ensure the real-time and effectiveness of control.

[0105] 3. The lateral controller of the present invention fully considers the error parameters and trajectory curvature of the vehicle. Compared with the existing lateral control technology, this design first obtains the optimal front wheel angle control law through the LQR controller and calculates the front wheel angle increment by designing a feedforward controller. The feedforward LQR lateral controller outputs the final front wheel angle control amount to the steering wheel angle controller, thereby completing the lateral control of the main vehicle. This design can quickly reduce the error between the actual driving trajectory of the main vehicle and the expected trajectory, and make the vehicle more stable during the entire trajectory tracking process, further improving the accuracy and stability of the lateral controller. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0107] Figure 1 It is a flow chart of a method for controlling the operation of a main vehicle based on the identification of traffic risk fields in a road environment;

[0108] Figure 2 It is a specific design flow chart of a main vehicle operation control method based on the identification of road environment traffic risk field;

[0109] Figure 3 (a) Overall schematic diagram of the main vehicle operation control method. Figure 3 (b) is a schematic diagram of the state of triggering the sound and light alarm. Figure 3 (c) is a schematic diagram of active braking control state. Figure 3 (d) is a schematic diagram of active steering control state. Figure 3 (e) is a schematic diagram of the pre-collision active braking state;

[0110] Figure 4 Design diagrams for vehicle control systems;

[0111] Figure 5 It is the strategy diagram of the longitudinal controller of the vehicle control system;

[0112] Figure 6 This is the structural design diagram of the longitudinal controller;

[0113] Figure 7 It is the strategy diagram of the lateral controller of the vehicle control system;

[0114] Figure 8 This is the structural design diagram of the lateral controller. DETAILED DESCRIPTION

[0115] In the absence of conflict, the embodiments of the present invention and the features described in the embodiments may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0116] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0117] Example 1

[0118] like Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a main vehicle operation control method based on road environment traffic risk field identification, including: S1, identifying the motion trajectory of a vehicle with abnormal driving behavior through a vehicle-mounted platform of the main vehicle and forming an abnormal driving behavior feature set;

[0119] The abnormal driving behaviors include: lane changing (CL), overtaking (OT), and deceleration (SC);

[0120] The abnormal driving behavior feature set includes: abnormal driving behavior details (ADB), abnormal driving behavior location details (L-ADB); wherein ADB includes: abnormal driving behavior type (ADB-T), start time (ADB-t), end time (ADB-s); L-ADB includes: longitude (L-ADB-l), latitude (L-ADB-la), speed (L-ADB-v), time (L-ADB-t), vehicle body spacing size (L-ADB-sb), field potential of interactive risk field (L-ADB-P f );

[0121] Furthermore, when the vehicle is in the process of changing lanes (CL), ADB-T=CL. In L-ADB, except for the time period of ADB-t<L-ADB-t<ADB-s, the L-ADB-l and L-ADB-la of the leading vehicle are located in different lanes from the main vehicle, and the L-ADB-v and L-ADB-sb of the two vehicles remain unchanged; within the time period of ADB-t<L-ADB-t<ADB-s, the L-ADB-l and L-ADB-la of the leading vehicle are offset and L-ADB-v gradually increases or decreases, and the L-ADB-sb of the main vehicle and the leading vehicle continue to increase or decrease until the leading vehicle and the main vehicle are located in the same lane;

[0122] That is, when the main vehicle and the front vehicle are in different lanes, the front vehicle deviates laterally and accelerates or decelerates, and then the distance between the two vehicles increases or decreases and they are in the same lane, it is identified as abnormal lane change driving behavior;

[0123] Furthermore, when the vehicle is in overtaking (OT), ADB-T=OT, and in L-ADB, except for the time period of ADB-t<L-ADB-t<ADB-s, the L-ADB-l and L-ADB-la of the rear vehicle are located in different lanes from the main vehicle, and the L-ADB-v and L-ADB-sb of the two vehicles remain unchanged; in the time period of ADB-t<L-ADB-t<ADB-s, the L-ADB-l and L-ADB-la of the rear vehicle are offset and L-ADB-v gradually increases, and thereafter the L-ADB-sb of the two vehicles continuously decreases and when the L-ADB-la of the main vehicle and the rear vehicle are the same, the L-ADB-sb of the two vehicles reaches the minimum, and thereafter the L-ADB-sb of the two vehicles continuously increases, at which time the rear vehicle surpasses the main vehicle and the L-ADB-l and L-ADB-la of the rear vehicle are offset and L-ADB-v gradually increases until the two vehicles are located in the same lane;

[0124] That is, when the main vehicle and the rear vehicle are in different lanes, the rear vehicle's trajectory shifts laterally and accelerates. The distance between the two vehicles reaches the minimum, and then the distance between the two vehicles increases and the rear vehicle's trajectory shifts laterally and accelerates. At this time, the rear vehicle and the main vehicle are in the same lane, which is identified as abnormal overtaking driving behavior;

[0125] Furthermore, when the vehicle is in deceleration (SC), ADB-T = SC. In L-ADB, within the time period of ADB-t < L-ADB-t < ADB-s, the L-ADB-l and L-ADB-la of the main vehicle and the front vehicle remain unchanged and the L-ADB-l of the two vehicles are the same. At this time, the L-ADB-sb of the two vehicles continuously decreases and the L-ADB-l remains unchanged.

[0126] That is, when the main vehicle and the front vehicle are in the same lane, the distance between the two vehicles decreases sharply, and the movement trajectory of the front vehicle does not deviate, it is identified as abnormal deceleration driving behavior;

[0127] S2. Using the vehicle's driving risk field to identify the risk of vehicles with abnormal driving behaviors around the main vehicle;

[0128] The driving risk field refers to the kinetic energy field of the degree of influence of moving objects on the road on driving risk, wherein the driving risk field of a single vehicle is defined as a single-vehicle risk field, and the driving risk field between the main vehicle and the vehicles around the main vehicle is defined as an interactive risk field;

[0129] The specific expression of the bicycle risk field is as follows:

[0130]

[0131]

[0132]

[0133] Where: x i ,y i is the horizontal and vertical coordinates of a single vehicle i, x k ,y k are the horizontal and vertical coordinates of point k; the x-axis is along the direction of vehicle travel, and the y-axis is along the direction perpendicular to the direction of vehicle travel; P f is the potential danger level of a single vehicle i to the surrounding environment when it moves. The greater the field strength, the greater the potential danger. The direction of the field strength is related to r ik Same; r ik is the pseudo distance between a single vehicle i and point k; P i is the road condition influencing factor, M i is the equivalent mass of a single vehicle i; m i is the real mass of a single vehicle i; V i is the speed of a single vehicle i; θ i is the movement direction of a single vehicle i and r ik The angle of i is the acceleration of a single vehicle i; λ1 is 0.056; λ2 is -0.169; λ3 is 2; α is 0.029; τ is 2.717;

[0134] The specific expression of the interactive risk field is as follows:

[0135]

[0136]

[0137] Q j =M j exp(-λ4V j cosφ j )

[0138]

[0139] Where: x i ,y i is the horizontal and vertical coordinates of vehicle i around the main vehicle, x j ,y j is the horizontal and vertical coordinates of the main vehicle j; P f-ji F is the risk potential energy of the interaction risk field between the main vehicle j and the vehicles i around the main vehicle. The greater the risk potential energy, the greater the potential danger between the main vehicle and the surrounding vehicles. ji is the field force on the main vehicle; Q j is the characteristic coefficient of vehicle j; P i is (x i ,y i ) is the road condition influencing factor, M i M is the equivalent mass of vehicle i around the main vehicle; j is the equivalent mass of vehicle j; V i V is the movement speed of vehicle i around the main vehicle; j is the moving speed of vehicle j; r ji is the pseudo distance between the main vehicle j and the surrounding vehicles i; θ i The movement direction of vehicle i around the main vehicle and r ji The angle of j The moving direction of vehicle j and r ji The angle of λ1 is 0.056; λ2 is -0.169; λ3 is 2; λ4 is -0.029; α is 0.029; τ is 2.717;

[0140] S3. Identify the risk type of abnormal driving behavior based on the risk type information and establish a risk type table;

[0141] The risk type information (ADB-RTI) includes: risk type (ADB-RTI-rt), interactive risk field potential threshold (ADB-RTI-Pi (i=1, 2, 3));

[0142] The risk type results are divided into three types: mild (SL), average (OR), and severe (SE);

[0143] In the risk type information (ADB-RTI), ADB-RTI-P1=0.0003 is the field potential threshold for classifying ADB-RTI-rt=SL; ADB-RTI-P2=0.003 is the field potential threshold for classifying ADB-RTI-rt=OR; ADB-RTI-P3=0.03 is the field potential threshold for classifying ADB-RTI-rt=SE;

[0144] The risk types are shown in Table 1:

[0145] Table 1 Risk type table

[0146]

[0147]

[0148] S4. Prepare a risk level table based on the risk type table, and inform the main vehicle of the real-time risk according to the risk level table;

[0149] In the risk level information (ADB-RLI) in the risk level table, the risk level (ADB-RLI-rl) includes: low level (LL), intermediate level (IL), high level (HL)

[0150] The risk level table includes: risk level matrix table, risk level acceptance table;

[0151] The risk level standards are specified according to the risk level matrix table, and the risk level matrix table is shown in Table 2 below;

[0152] Table 2 Risk level matrix

[0153]

[0154] Explain the current risk level and corresponding acceptance status according to the risk level acceptance criteria table. The risk level acceptance criteria table is as follows Table 3;

[0155] Table 3 Risk level acceptance criteria

[0156] Risk Level Fields Acceptance level Countermeasures Low Level ADB-RLI-rl=LL Acceptable Be prepared to adjust response measures at any time intermediate ADB-RLI-rl=IL Conditional acceptance Certain measures need to be taken advanced ADB-RLI-rl=HL Unacceptable Immediate response measures are needed

[0157] According to the risk level table, the risk type and risk level of the abnormal driving behavior feature set are identified. The risk level table is shown in Table 4 below;

[0158] Table 4 Risk level table

[0159]

[0160]

[0161] S5. Use vehicle control systems to avoid driving risks;

[0162] The vehicle control system includes: a lateral controller and a longitudinal controller; wherein the lateral controller is a steering wheel steering controller, which is controlled by the steering angle and speed of the steering wheel; the longitudinal controller is an accelerator pedal controller and a brake pedal controller, which are controlled by the degree of opening and closing of the pedals;

[0163] like Figure 3 As shown in (a), the vehicle control system uses different working modes to control the controller, and the working modes are divided into A gear, B gear, and C gear;

[0164] Specifically, when the vehicle control system enters the A gear working mode, the longitudinal controller actively brakes and controls, and the corresponding risk level is low; when the vehicle control system enters the B gear working mode, the lateral controller actively steers and controls, and the corresponding risk level is medium; when the vehicle control system enters the C gear working mode, the corresponding risk level is high, and the vehicle enters the pre-collision active braking state, decelerating and stopping with maximum braking intensity.

[0165] The working mode response is shown in Table 5 below;

[0166] Table 5 Working mode response table

[0167]

[0168] like Figure 3 (b) shows that when ADB-T = CL, OT, SC, in L-ADB, within the time period of ADB-t < L-ADB-t < ADB-s, if L-ADB-P f =ADB-RTI-P1, the vehicle control system is in a critical state of entering the working mode, triggering an audible and visual alarm to remind the driver to drive carefully;

[0169] like Figure 3 As shown in (c), if ADB-RTI-P1<L-ADB-P f <ADB-RTI-P2, then ADB-RTI-rt=SL, ADB-RLI-rl=LL, indicating that L-ADB-sb of the two vehicles is greater than the latest braking point (L-ADB-LPTB). If the driver has no effective input, the vehicle control system enters the A gear working mode, that is, the longitudinal controller actively brakes;

[0170] like Figure 3 As shown in (d), if ADB-RTI-P2≤L-ADB-P f<ADB-RTI-P3, then ADB-RTI-rt=OR, ADB-RLI-rl=IL, indicating that the L-ADB-sb of the two vehicles is less than the latest braking point (L-ADB-LPTB), greater than the latest turning point (L-ADB-LPTS), and there is enough space in the target lane. If the driver has no effective input, the vehicle control system enters the B gear working mode, and the lateral controller actively controls the steering;

[0171] like Figure 3 As shown in (e), if ADB-RTI-P3≤L-ADB-P f , then ADB-RTI-rt=SE, ADB-RLI-rl=HL, indicating that the L-ADB-sb of the two vehicles is less than the latest turning point L-ADB-LPTS and there is not enough space in the target lane. The vehicle control system enters the C gear working mode, and the vehicle enters the pre-collision active braking state, decelerating and stopping with the maximum braking intensity;

[0172] like Figure 4 As shown, the control principle is to use the longitudinal controller for braking first. When the longitudinal active braking alone cannot avoid danger, the lateral controller is used for active steering. At the same time, the driver's control right has a higher priority. That is, during the active braking or active steering process, if the driver accelerates, brakes or steers, etc., which are inconsistent with the system's working logic, the system will immediately exit and return the control right to the driver.

[0173] like Figure 5 and Figure 6 As shown, preferably, the longitudinal controller in the vehicle control system performs active braking control on the vehicle, and the longitudinal controller adopts a hierarchical control design, including: an upper controller based on the LQR algorithm, solving the desired acceleration for the lower control input; a lower controller based on the PID control method of acceleration feedback; wherein the upper controller includes two steps of establishing a state space equation and calculating the desired acceleration;

[0174] The step of establishing the state space equation is to use the current speed v of the vehicle x and the expected speed v des As input, derive the current acceleration a of the vehicle x and the expected acceleration a des The state space equations of

[0175] The step of calculating the expected acceleration is based on the established state space equation to obtain the expected acceleration a of the vehicle. des ;

[0176] The current speed v of the vehicle x and the expected speed v des The speed error ε can be obtained v for:

[0177] ε v =v des -v x

[0178] During the longitudinal control process, the current acceleration and the expected acceleration of the vehicle satisfy the first-order inertia relationship, and the transfer function expression is:

[0179]

[0180] Where: K l is the control system gain; τ del The system delay time.

[0181] State quantity X = [ε v a x ] T , control quantity u=[a des ], the state space equation is:

[0182]

[0183] Where:

[0184] The quadratic performance function expression is:

[0185]

[0186] Where: R=[r]

[0187] Combining the LQR theoretical knowledge, the optimal feedback of the control system can be obtained as:

[0188] U=-R -1 B T P

[0189] The P matrix can be obtained by solving the Riccati equation PA+A T P-PBR -1 B T P+Q=0 is obtained.

[0190] Feedback gain matrix K = [k1 k2], expressed as:

[0191] K=lqr(A,B,Q,R)

[0192] Where: R=[r]

[0193] Therefore, combining the feedback gain matrix K = [k1 k2] and the system state quantity, the expected acceleration a of the vehicle can be obtained: des for a des =-(k1εv +k2a x ), where k1 and k2 are the first and second values ​​of the feedback gain matrix K respectively; ε v is the speed error; a x is the current acceleration.

[0194] Furthermore, by adopting a lower-layer controller of a PID control method based on acceleration feedback, control information of an accelerator pedal controller and a brake pedal controller is output, and driving at a desired speed is achieved through an accelerator-brake calibration table;

[0195] The lower controller of the acceleration feedback-based PID control method is used to convert the desired acceleration output by the upper controller into the desired throttle opening and brake pressure.

[0196] First, the desired acceleration output by the upper controller is controlled by the "feedforward + feedback" method, and the PID control algorithm is introduced for adjustment; secondly, the acceleration is converted into the desired throttle opening and brake pressure by means of a longitudinal calibration table. Among them, the longitudinal calibration table characterizes the vehicle's response characteristics to the throttle and brake at different speeds. Its input is the speed and the desired acceleration, and the output is the throttle opening or brake pressure value. After the data is interpolated, a calibration table containing all throttles and brakes is generated, so that at any given speed and acceleration, there is a unique throttle and brake value corresponding to it.

[0197] Further, the engine, transmission, and brake system of the host vehicle respond based on the output information of the longitudinal controller and the throttle-brake calibration table;

[0198] The main vehicle engine, transmission, and brake system respond based on the information output by the longitudinal controller and the throttle-brake calibration table. In the specific implementation method, the throttle and pedal controller information output by the longitudinal controller and the throttle-brake calibration table is output to the engine, and the engine is connected to the transmission; the output brake pedal controller information is output to the brake device.

[0199] like Figure 7 and Figure 8 As shown, preferably, the lateral controller in the vehicle control system performs active steering control on the vehicle, and the lateral controller is a feedforward LQR control;

[0200] Furthermore, the vehicle error parameter e is calculated based on the expected trajectory and vehicle state parameters through the trajectory tracking error model. rr and trajectory curvature k r ;

[0201] When tracking the desired path, there is a lateral error e d and heading error Affects tracking performance. Lateral error ed is the distance from the center of mass of the vehicle to the center of the desired path trajectory, and the heading error is the heading angle θ of the vehicle body and the heading angle θ of the tangent line at the center point of the desired trajectory r The difference is The vehicle's center of mass sideslip angle β is small during driving, and the body heading angle θ is proportional to the vehicle's yaw angle. The relationship is: The heading error is considered Define point Q as the projection point of the vehicle's center of mass on the desired trajectory, then the speed of moving along the desired trajectory at point Q is:

[0202]

[0203] Where: e d is the lateral error; is the heading error; k r is the curvature of the desired trajectory at point Q; V x is the longitudinal velocity; V y is the lateral speed.

[0204] The spatial state expression of the trajectory tracking error model is obtained as follows: in

[0205] u=[δ]

[0206] Where: is the first-order derivative of the error parameter; e rr is the error parameter; e d is the lateral error; is the heading error; is the first-order derivative of the lateral error; is the first-order derivative of the heading error; is the heading angle θ of the tangent line at the center point of the desired trajectory r The first derivative of ; δ is the front wheel steering angle; m is the vehicle mass; V x is the longitudinal velocity; a and b are the distances from the center of mass of the vehicle to the front and rear axles respectively; C f and C r are the cornering stiffness of the front and rear tires respectively; I z is the moment of inertia of the vehicle's center of mass rotating around the Z axis.

[0207] Furthermore, the LQR controller obtains the controller's optimal control feedback matrix and the optimal front wheel steering angle control law;

[0208] First, the Euler method is used to discretize the LQR. Second, the performance objective function of the system is constructed and the optimal control input u(k) is established to minimize the performance objective function J. Finally, the optimal control of the LQR controller is u(k) = -Kx(k), where K = (R + B T PB) -1 B T PA is the optimal control feedback matrix of the LQR controller, and P is the following Riccati equation PA+A T P-PBR -1 BP T +Q=0, Q is a semi-positive definite real symmetric constant matrix, R is a positive definite real symmetric constant matrix, R=[1]. Finally, in the linear system controlled by the LQR controller, u=δ, x=e rr , the optimal front wheel steering angle control law of the system is δ(k)=-Ke rr (k).

[0209] Furthermore, through the trajectory curvature k r The feedforward controller is designed with the feedback matrix K to calculate the front wheel steering angle increment δ f The feedforward PSO-LQR lateral controller outputs the final front wheel steering angle control value u(k) to the steering wheel angle controller of the vehicle to complete the lateral control.

[0210] In order to eliminate the steady-state error, a feedforward controller needs to be added to the LQR controller. At this time, the optimal front wheel angle control law is u(k) = -Ke rr (k)+δ f , where δ f is the front wheel steering angle increment output by the feedforward controller; when the system is stable, The steady-state error is:

[0211]

[0212] Where: k1 and k3 are the first and third values ​​of the LQR optimal control feedback matrix K respectively.

[0213] Let e d = 0 After eliminating the steady-state error, the feedforward controller outputs the front wheel angle increment δ f for

[0214] When the system is stable, the steady-state error of the heading error is Can make the heading error θ-θ r = 0, then the optimal front wheel steering angle control law u(k) is

Claims

1. A method for controlling the operation of a main vehicle based on identification of traffic risk fields in a road environment, characterized in that: The following steps are involved: S1. Identify the motion trajectory of vehicles with abnormal driving behavior through the onboard platform of the host vehicle and form a set of abnormal driving behavior features; Abnormal driving behaviors include: lane changing, overtaking, and deceleration; The abnormal driving behavior feature set includes: abnormal driving behavior details, abnormal driving behavior location details; S2. Use the vehicle's driving risk field to identify vehicles with abnormal driving behaviors around the main vehicle; The driving risk field is a kinetic energy field that indicates the degree of influence of moving objects on the road on driving risk. S3. Identify the risk type of abnormal driving behavior based on the risk type information and establish a risk type table; S4. Prepare a risk level table based on the risk type table, and inform the main vehicle of the real-time risk according to the risk level table; S5. Use vehicle control systems to avoid driving risks.

2. The method for controlling the operation of a main vehicle based on identification of a road environment traffic risk field according to claim 1, characterized in that: The driving risk field of a single vehicle in the driving risk field is a single vehicle risk field, and an interactive risk field is composed of multiple single vehicle risk fields. The specific expression of the single vehicle risk field is as follows: M i =m i (1.566×10 -14 In i 6.687 +0.3345) Where: x i 、y i is the horizontal and vertical coordinates of a single vehicle i, x k 、y k are the horizontal and vertical coordinates of point k; the x-axis is along the direction of vehicle travel, and the y-axis is perpendicular to the direction of vehicle travel; P f is the potential danger level of a single vehicle i to the surrounding environment when it moves. The greater the field strength, the greater the potential danger. The direction of the field strength is related to r ik Same; r ik is the pseudo distance between a single vehicle i and point k; P i is the road condition influencing factor, M i is the equivalent mass of a single vehicle i; m i is the true mass of a single vehicle i; V i is the speed of a single vehicle i; θ i The moving direction of a single vehicle i and r ik Angle; a i is the acceleration of a single vehicle i; λ1 is 0.056; λ2 is -0.169; λ3 is 2; α is 0.029; τ is 2.717; The specific expression of the interactive risk field is as follows: Q j =M j exp(-λ4V j cosφ j ) Where: x i 、y i x is the horizontal and vertical coordinates of vehicle i around the main vehicle, j 、y j is the horizontal and vertical coordinates of the main vehicle j; P f-ji The risk potential energy of the interaction risk field between the main vehicle j and the surrounding vehicles i is larger. The larger the risk potential energy, the greater the potential danger between the main vehicle and the surrounding vehicles. F ji The field force on the main vehicle; Q j is the characteristic coefficient of vehicle j; P i is (x i ,y i ) road condition influencing factor, M i M is the equivalent mass of vehicle i around the main vehicle; j is the equivalent mass of vehicle j; V i V is the speed of vehicle i around the main vehicle; j is the speed of vehicle j; r ji The pseudo distance between the main vehicle j and the surrounding vehicles i; θ i The movement direction of vehicle i around the main vehicle and r ji The angle between j The moving direction of vehicle j and r ji The angle is 0.056 for λ1, -0.169 for λ2, 2 for λ3, -0.029 for λ4, 0.029 for α, and 2.717 for τ.

3. The method for controlling the operation of a main vehicle based on identification of a road environment traffic risk field according to claim 1, characterized in that: According to the risk type information, the risk type of abnormal driving behavior is identified and a risk type table is established, wherein the risk type information includes: risk type, interactive risk field potential threshold, and the risk type is judged by the interactive risk field potential threshold and is divided into three types: minor, general, and severe.

4. The method for controlling the operation of a main vehicle based on identification of a road environment traffic risk field according to claim 1, characterized in that: The risk level table is prepared based on the risk type table, and the real-time risk is notified to the main vehicle according to the risk level table, wherein the risk level table is divided into three levels: low, medium, and high, corresponding to the three risk types of slight, general, and severe respectively.

5. The method for controlling the operation of a main vehicle based on identification of a road environment traffic risk field according to claim 1, characterized in that: In the aforementioned use of a vehicle control system to avoid driving risks, the vehicle control system consists of two parts: a lateral controller and a longitudinal controller. The operating modes are divided into three different levels: A gear, B gear, and C gear. When the vehicle control system enters the A gear operating mode, the longitudinal controller actively performs braking control, corresponding to a low risk level; when the vehicle control system enters the B gear operating mode, the lateral controller actively performs steering control, corresponding to a medium risk level; When the vehicle control system enters the C gear working mode, the corresponding risk level is high, and the vehicle enters the pre-collision active braking state, slowing down and stopping with the maximum braking intensity.

6. The method for controlling the operation of a main vehicle based on identification of a road environment traffic risk field according to claim 5, characterized in that: The lateral controller is a steering wheel steering controller, which is controlled by the steering angle and speed of the steering wheel. The lateral controller is a feedforward LQR control.

7. The method for controlling the operation of a main vehicle based on identification of a road environment traffic risk field according to claim 5, characterized in that: The longitudinal controllers are the accelerator pedal controller and the brake pedal controller, which control the vehicle displacement, longitudinal speed and longitudinal acceleration by controlling the degree of opening and closing of the pedals. This is achieved by controlling the accelerator pedal controller and the brake pedal controller. The longitudinal controller adopts a hierarchical control design, including: an upper-level controller based on the LQR algorithm, and a lower-level controller based on the PID control method of acceleration feedback.

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