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

Abstract

The application provides a kind of main car operation control method based on road environment traffic risk field identification.First, the motion trajectory and motion data of main car and the vehicle with abnormal driving behavior around main car are collected in real time by the vehicle-mounted platform of main car and form the feature set of abnormal driving behavior.Second, the driving risk of the vehicle with abnormal driving behavior is characterized by driving risk field, and the real-time risk type is judged.Third, the risk type and risk level of abnormal driving behavior are divided by the field potential of interaction risk field.Finally, for the identified abnormal driving behavior type and its risk type and risk level, the vehicle control system enters different working modes to control the longitudinal and lateral motion of main car, avoids driving risk and ensures driving safety.The application can make the vehicle avoid the potential risk brought by abnormal driving behavior during driving, reduce the probability of traffic accident and ensure the safety of vehicle driving.

Description

TECHNICAL FIELD

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

[0002] With the rapid development of the automobile industry, the research on the operation control of the automobile is paid more and more attention, and the operation control of the automobile has a crucial influence on the safety and stability of the driving process, which is also a key research direction. During the driving process, the vehicle often encounters abnormal driving behavior vehicles such as lane changing, overtaking and deceleration, which brings great road traffic risk and seriously interferes with the driving safety of the host vehicle, so it is necessary to carry out the research on the operation control of the vehicle based on the road environment traffic risk field identification of the driving risk of the abnormal driving behavior vehicles.

[0003] Most of the existing vehicle operation control methods control the vehicle to accelerate, decelerate and turn according to certain strategies according to the position, speed and other driving state information of the vehicle, so that the vehicle travels at the expected trajectory and speed, so as to achieve the purpose of safe driving. However, most of the existing related vehicle control researches are based on one-way vehicle lateral control or vehicle longitudinal control, and these researches often only consider the running state of the host vehicle and ignore the risk brought by the abnormal driving behavior vehicles in the road environment, and there are few strategies for using driving risk field and risk level table to control the vehicle in the existing technology. There are problems such as inaccurate abnormal driving behavior type identification, unclear driving risk level identification and poor control effect. Therefore, the present application proposes a host vehicle operation control method based on road environment traffic risk field identification, which realizes the lateral and longitudinal collaborative control of the host vehicle based on the consideration of the road environment traffic risk field to avoid risks, and has important significance for the development of vehicle control technology. SUMMARY

[0004] The present application discloses a host vehicle operation control method based on road environment traffic risk field identification, which aims to solve the problems of few strategies for using driving risk field and risk level table to control the vehicle, inaccurate abnormal driving behavior type identification, unclear driving risk level identification and poor control effect in the prior art.

[0005] To achieve the above-mentioned purpose, the present application discloses a host vehicle operation control method based on road environment traffic risk field identification, which specifically comprises the following steps:

[0006] S1, identifying the motion trajectory of the abnormal driving behavior vehicle through the vehicle-mounted platform of the host vehicle and forming an abnormal driving behavior feature set;

[0007] The abnormal driving behavior includes lane changing (CL), overtaking (OT) and deceleration (SC);

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

[0009] Further, when the vehicle is in lane changing (CL), ADB-T = CL, and in L-ADB, outside the time period of ADB-t < L-ADB-t < ADB-s, the L-ADB-l and L-ADB-la of the preceding vehicle are located in different lanes from the host 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 preceding vehicle are offset, and the L-ADB-v gradually increases or decreases, and the L-ADB-sb of the host vehicle and the preceding vehicle continuously increases or decreases, until the preceding vehicle and the host vehicle are located in the same lane;

[0010] That is, when the host vehicle and the preceding vehicle are located in different lanes, the preceding vehicle has lateral offset and acceleration or deceleration, and then the distance between the two vehicles becomes larger or smaller and they are located in the same lane, which is identified as lane changing abnormal driving behavior;

[0011] Further, when the vehicle is in overtaking (OT), ADB-T = OT, and in L-ADB, outside the time period of ADB-t < L-ADB-t < ADB-s, the L-ADB-l and L-ADB-la of the following vehicle are located in different lanes from the host 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 following vehicle are offset, and the L-ADB-v gradually increases, and then the L-ADB-sb of the two vehicles continuously decreases, and when the L-ADB-la of the host vehicle and the following vehicle is the same, the L-ADB-sb of the two vehicles reaches a minimum, and then the L-ADB-sb of the two vehicles continuously increases, at which time the following vehicle overtakes the host vehicle, and the L-ADB-l and L-ADB-la of the following vehicle are offset and the L-ADB-v gradually increases, until the two vehicles are located in the same lane;

[0012] That is, when the host vehicle and the following vehicle are located in different lanes, the trajectory of the following vehicle has lateral offset and acceleration behavior, the time distance between the two vehicles reaches a minimum, and then the distance between the two vehicles increases, and the trajectory of the following vehicle has lateral offset and acceleration behavior, at which time the following vehicle and the host vehicle are located in the same lane, which is identified as overtaking abnormal driving behavior.

[0013] Further, when the vehicle is in deceleration (SC), ADB-T = SC, in L-ADB, the time period of ADB-t < L-ADB-t < ADB-s, the L-ADB-l and L-ADB-la of the host 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 is continuously reduced and the L-ADB-l is unchanged;

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

[0015] S2, using the driving risk field of the vehicle to identify the risk of the vehicle with abnormal driving behavior around the host vehicle;

[0016] Among them, the driving risk field refers to the kinetic field of the influence degree of the moving object on the road to the 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 host vehicle and the vehicle around the host vehicle is defined as an interactive risk field;

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

[0018]

[0019]

[0020]

[0021] In the formula: x i , y i are the horizontal coordinate and the vertical coordinate of the single vehicle i, x k , y k are the horizontal coordinate and the vertical coordinate of point k; the x-axis is along the driving direction of the vehicle, and the y-axis is along the direction perpendicular to the driving direction of the vehicle; P f is the potential danger degree generated by the movement of the single vehicle i to the surrounding environment, the greater the field strength, the greater the potential danger; the field strength direction is the same as r ik ; r ik is the pseudo distance size from the single vehicle i to point k; P i is the road condition influence factor, M i is the equivalent mass of the single vehicle i; m i is the true mass of the single vehicle i; V i is the movement speed of the single vehicle i; θ i is the included angle between the movement direction of the single vehicle i and r ik ; a iis the acceleration of the 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] In the formula, x i , y i are the horizontal coordinate and the vertical coordinate of the vehicle i around the host vehicle, x j , y j are the horizontal coordinate and the vertical coordinate of the host vehicle j; P f-ji is the risk potential of the interactive risk field of the host vehicle j and the vehicle i around the host vehicle, and the greater the risk potential is, the greater the potential danger between the host vehicle and the vehicle around the host vehicle is; F ji is the field force borne by the host vehicle; Q j is the characteristic coefficient of the host vehicle j; P i is the road condition influence factor at (x i , y i ), M i is the equivalent mass of the vehicle i around the host vehicle; M j is the equivalent mass of the host vehicle j; V i is the motion speed of the vehicle i around the host vehicle; V j is the motion speed of the host vehicle j; r ji is the pseudo distance between the host vehicle j and the vehicle i around the host vehicle; θ i is the included angle between the motion direction of the vehicle i around the host vehicle and r ji ; φ j is the included angle between the motion direction of the host vehicle j and r ji ; λ1 is 0.056; λ2 is -0.169; λ3 is 2; λ4 is -0.029; α is 0.029; τ is 2.717;

[0028] The potential risk between the host vehicle and the vehicle around the host vehicle is predicted through the interactive risk field;

[0029] S3, according to the risk type information, identifying the risk type of the abnormal driving behavior and establishing a risk type table;

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

[0031] The risk type result of the judgment is divided into three types: slight (SL), general (OR), and serious (SE);

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

[0033] The risk type table is as shown in Table 1 below:

[0034] Table 1 Risk type table

[0035]

[0036] S4, according to the risk type table, a risk level table is prepared, and the host vehicle is informed 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: a risk level matrix table and a risk level acceptance table;

[0039] The risk level standard is 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 table

[0041]

[0042] The current risk level and the corresponding acceptance are explained according to the risk level acceptance criterion table, and the risk level acceptance criterion table is shown in Table 3 below;

[0043] Table 3 Risk level acceptance criterion table

[0044] Risk level Field Acceptance level Countermeasures Low ADB-RLI-rl = LL Acceptable Prepare to adjust countermeasures at any time Medium ADB-RLI-rl = IL Acceptable with conditions Take certain measures High ADB-RLI-rl = HL Not acceptable Immediately implement countermeasures

[0045] According to the risk level table, the risk type identification and risk level division are performed on the abnormal driving behavior feature set, and the risk level table is shown in Table 4 below;

[0046] Table 4 Risk level table

[0047]

[0048] S5, evading driving risk by using vehicle control system;

[0049] The vehicle control system comprises a lateral controller and a longitudinal controller; the lateral controller is a steering wheel steering controller, which controls the steering angle and rate of the steering wheel; the longitudinal controller is a throttle pedal controller and a brake pedal controller, which controls the opening and closing degree of the pedal;

[0050] The vehicle control system controls the controllers by using different working modes, which 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 the corresponding risk level is low; when the vehicle control system enters the B gear working mode, the lateral controller actively steers, 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 to decelerate and stop at the maximum braking strength.

[0052] The working mode response is shown in Table 5 as follows:

[0053] Table 5 Working mode response table

[0054]

[0055] That is, when ADB-T = CL, OT, SC, in L-ADB, take ADB-t < L-ADB-t < ADB-s time period, 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 light 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, which means that the L-ADB-sb of the two vehicles is greater than the latest braking point (L-ADB-LPTB), and 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 fADB-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) and 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 mode working mode, and the lateral controller actively controls the steering;

[0058] ADB-RTI-P3≤L-ADB-P f 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 mode working mode, and the vehicle enters the pre-collision active braking state to decelerate and stop at the maximum braking intensity;

[0059] The control principle is to preferentially use the longitudinal controller for braking, and when the active braking alone cannot complete the danger avoidance, the lateral controller is used for active steering; meanwhile, the control right of the driver has a higher priority, that is, during the active braking or active steering process, if the driver has an operation such as acceleration, braking or steering that is not consistent with the system working logic, the system exits the working immediately and returns the control right to the driver;

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

[0061] The step of establishing the state space equation takes the current speed v x and the expected speed v des of the vehicle as inputs, and derives the state space equation of the current acceleration a x and the expected acceleration a des of the vehicle;

[0062] The step of calculating the expected acceleration is based on the established state space equation, and the expected acceleration a des of the vehicle is solved;

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

[0064] ε v =v des -v x

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

[0066]

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

[0068] The state quantity X = [ε v a x ] T , the control quantity u = [a des ], and the state space equation can be obtained as:

[0069]

[0070] In the formula:

[0071] The expression of the quadratic performance function is:

[0072]

[0073] In the formula: R = [r]

[0074] Combined with the LQR theory, the optimal feedback of the control system can be obtained as:

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

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

[0077] The feedback gain matrix K = [k1 k2] is expressed as:

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

[0079] In the formula:

[0080] Therefore, combined with the feedback gain matrix K = [k1 k2] and the system state quantity, the desired acceleration a des of the vehicle can be obtained as: 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 using a PID control method based on acceleration feedback, control information of the accelerator pedal controller and the brake pedal controller is output, and driving at the desired speed is achieved through the 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, a "feedforward + feedback" approach is used to control the desired acceleration output by the upper-level controller, and a PID control algorithm is introduced for regulation. Second, the acceleration is converted into the desired throttle opening and brake pressure using a longitudinal calibration table. This longitudinal calibration table characterizes the vehicle's throttle and brake response characteristics at different speeds. Its inputs are speed and desired acceleration, and its output is the throttle opening or brake pressure value. After interpolation, this data is generated to create a calibration table containing all throttle and brake values, ensuring that a unique throttle and brake value corresponds to any given speed and acceleration.

[0084] Furthermore, the host vehicle's engine, transmission, and braking system respond based on information output from the longitudinal controller and the throttle-brake calibration table;

[0085] The vehicle's engine, transmission, and braking system respond based on information output from the longitudinal controller and the throttle-brake calibration table. In a specific implementation, the throttle and pedal control information output from the longitudinal controller and the throttle-brake calibration table is fed to the engine, which is connected to the transmission; and the brake pedal control information is fed to the braking system.

[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 desired 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, that is The vehicle's center of mass side slip angle β is small during driving, and the vehicle's 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 the vehicle 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 velocity.

[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 angle; m is the vehicle mass; V x is the longitudinal velocity; a and b are the distances from the vehicle's center of mass 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. Then, the performance objective function of the system is constructed and the optimal control input u(k) is determined 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 LQR controller, P is the solution of 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 LQR controller, u=delta, x=e rr , the optimal front wheel angle control law of the system is delta (k)=-Ke rr (k).

[0096] Further, by trajectory curvature k r and feedback matrix K, a feedforward controller is designed, and front wheel angle increment delta f is calculated; the feedforward PSO-LQR lateral controller outputs the final front wheel angle control amount u(k) to the steering wheel angle controller of the vehicle, and the lateral control is completed.

[0097] To eliminate the steady-state error, a feedforward controller needs to be added to the LQR controller, and the optimal front wheel angle control law is u(k)=-Ke rr (k)+delta f , wherein delta f is the front wheel angle increment output by the feedforward controller; when the system is stable, the steady-state error is:

[0098]

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

[0100] Let e d =0 to eliminate the steady-state error, and the front wheel angle increment delta f output by the feedforward controller is

[0101] When the system is stable, the steady-state error of the heading error is which can make the heading error theta-theta r =0, and the optimal front wheel angle control law u(k) is

[0102] Advantages of the present application

[0103] 1. The application utilizes the road environment traffic risk field to effectively identify the abnormal driving behavior vehicles around the host vehicle, determines the driving risk according to the risk level table, informs the host vehicle of the real-time risk, and designs the horizontal and vertical cooperative control system of the host vehicle, thereby solving the problems of inaccurate type identification of abnormal driving behavior vehicles, unclear driving risk level identification, and poor control system effect in the prior art. The control method of the application can effectively improve the stability and safety of vehicle horizontal and vertical control.

[0104] 2. The longitudinal controller of the application adopts a hierarchical control design. Compared with the existing longitudinal control method, the design first solves the expected acceleration for the lower layer control input based on the upper layer controller of the LQR algorithm, and then designs the lower layer controller based on the PID control method of the acceleration feedback, designs the throttle-brake calibration table to output the control information of the throttle and brake pedal controller, realizes the driving of the host vehicle according to the expected speed, and thereby realizes the longitudinal control target, can reduce the difficulty of longitudinal control, and ensure the real-time and effectiveness of the control.

[0105] 3. The lateral controller of the application fully considers the error parameters and trajectory curvature of the vehicle. Compared with the existing lateral control technology, the design first obtains the optimal front wheel steering angle control law through the LQR controller and calculates the front wheel steering angle increment through the design of the feedforward controller, and the feedforward LQR lateral controller outputs the final front wheel steering angle control quantity to the steering wheel angle controller, thereby completing the lateral control of the host vehicle. The design can quickly reduce the error between the actual driving trajectory of the host vehicle and the expected trajectory, and make the stability of the vehicle in the whole trajectory tracking process better, further improve the precision and stability of the lateral controller. BRIEF DESCRIPTION OF DRAWINGS

[0106] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0107] Figure 1 It is a flow chart of a host vehicle operation control method based on road environment traffic risk field identification;

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

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

[0110] Figure 4 a design diagram for a vehicle control system;

[0111] Figure 5 a longitudinal controller strategy diagram for a vehicle control system;

[0112] Figure 6 a longitudinal controller structure design diagram;

[0113] Figure 7 a lateral controller strategy diagram for a vehicle control system;

[0114] Figure 8 a lateral controller structure design diagram. DETAILED DESCRIPTION

[0115] In the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0116] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below in combination with the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0117] Embodiment 1

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

[0119] The abnormal driving behavior includes lane changing (CL), overtaking (OT), and deceleration (SC);

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

[0121] Further, when the vehicle is in lane changing (CL), ADB-T = CL, in L-ADB, outside the time period of ADB-t < L-ADB-t < ADB-s, the L-ADB-l and L-ADB-la of the preceding vehicle are located in different lanes from the host 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 preceding vehicle are offset and the L-ADB-v gradually increases or decreases, and the L-ADB-sb of the host vehicle and the preceding vehicle continuously increases or decreases until the preceding vehicle and the host vehicle are located in the same lane;

[0122] That is, when the host vehicle and the preceding vehicle are located in different lanes, the preceding vehicle has lateral offset and acceleration or deceleration, and then the distance between the two vehicles becomes larger or smaller and they are located in the same lane, which is identified as lane changing abnormal driving behavior;

[0123] Further, when the vehicle is in overtaking (OT), ADB-T = OT, in L-ADB, outside the time period of ADB-t < L-ADB-t < ADB-s, the L-ADB-l and L-ADB-la of the following vehicle are located in different lanes from the host 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 following vehicle are offset and the L-ADB-v gradually increases, after which the L-ADB-sb of the two vehicles continuously decreases and reaches a minimum when the L-ADB-la of the host vehicle and the following vehicle is the same, and then the L-ADB-sb of the two vehicles continuously increases, at which time the following vehicle overtakes the host vehicle and the L-ADB-l and L-ADB-la of the following vehicle are offset and the L-ADB-v gradually increases until the two vehicles are located in the same lane;

[0124] That is, when the host vehicle and the following vehicle are located in different lanes, the trajectory of the following vehicle has lateral offset and acceleration behavior, the time distance between the two vehicles reaches a minimum, and then the distance between the two vehicles increases and the trajectory of the following vehicle has lateral offset and acceleration behavior, at which time the following vehicle and the host vehicle are located in the same lane, which is identified as overtaking abnormal driving behavior;

[0125] Further, when the vehicle is in deceleration (SC), ADB-T = SC, in L-ADB, the time period of ADB-t < L-ADB-t < ADB-s, the L-ADB-l and L-ADB-la of the host vehicle and the preceding 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 is continuously reduced and the L-ADB-l remains unchanged;

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

[0127] S2, using the driving risk field of the vehicle to identify the risk of the vehicle with abnormal driving behavior around the host vehicle;

[0128] The driving risk field refers to the kinetic field of the influence degree of the moving object on the road on the 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 host vehicle and the vehicle around the host vehicle is defined as an interactive risk field;

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

[0130]

[0131]

[0132]

[0133] In the formula: x i , y i are the horizontal coordinate and the vertical coordinate of the single vehicle i, x k , y k are the horizontal coordinate and the vertical coordinate of point k; the x-axis is along the driving direction of the vehicle, and the y-axis is along the direction perpendicular to the driving direction of the vehicle; P f is the potential danger degree generated by the single vehicle i when moving to the surrounding environment, the greater the field strength, the greater the potential danger; the field strength direction is the same as r ik ; r ik is the pseudo distance size of the single vehicle i to point k; P i is the road condition influence factor, M i is the equivalent mass of the single vehicle i; m i is the real mass of the single vehicle i; V i is the motion speed of the single vehicle i; θ i is the included angle between the motion direction of the single vehicle i and r ik ; a i is the acceleration of the single vehicle i; λ1 takes 0.056; λ2 takes -0.169; λ3 takes 2; α takes 0.029; τ takes 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] In the formula, x i and y i are the horizontal coordinate and the vertical coordinate of the vehicle i around the host vehicle, x j and y j are the horizontal coordinate and the vertical coordinate of the host vehicle j; P f-ji is the risk potential of the interactive risk field of the host vehicle j and the vehicle i around the host vehicle, and the greater the risk potential is, the greater the potential danger between the host vehicle and the vehicle around the host vehicle is; F ji is the field force borne by the host vehicle; Q j is the characteristic coefficient of the host vehicle j; P i is the road condition influence factor at (x i , y i ); M i is the equivalent mass of the vehicle i around the host vehicle; M j is the equivalent mass of the host vehicle j; V i is the motion speed of the vehicle i around the host vehicle; V j is the motion speed of the host vehicle j; r ji is the pseudo distance between the host vehicle j and the vehicle i around the host vehicle; θ i is the included angle between the motion direction of the vehicle i around the host vehicle and r ji ; φ j is the included angle between the motion direction of the host vehicle j and r ji ; λ1 is 0.056; λ2 is-0.169; λ3 is 2; λ4 is-0.029; α is 0.029; and τ is 2.717.

[0140] S3, according to the risk type information, identifying the risk type of the abnormal driving behavior and establishing a risk type table;

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

[0142] The risk type result of the judgment is divided into three types: slight (SL), ordinary (OR), and serious (SE);

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

[0144] The risk type table is shown in Table 1 below:

[0145] Table 1 Risk type table

[0146]

[0147]

[0148] S4, according to the risk type table, a risk level table is prepared, and the host vehicle is informed 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), and high level (HL)

[0150] The risk level table includes a risk level matrix table and a risk level acceptance table.

[0151] The risk level standard is 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 table

[0153]

[0154] The current risk level and the corresponding acceptance are explained according to the risk level acceptance criterion table, and the risk level acceptance criterion table is shown in Table 3 below:

[0155] Table 3 Risk level acceptance criterion table

[0156] Risk level Field Acceptance level Countermeasures Low ADB-RLI-rl = LL Acceptable Prepare to adjust countermeasures at any time Medium ADB-RLI-rl = IL Acceptable with conditions Take certain measures High ADB-RLI-rl = HL Not acceptable Immediately implement countermeasures

[0157] According to the risk level table, the risk type identification and risk level division are performed on the abnormal driving behavior feature set, and the risk level table is shown in Table 4 below:

[0158] Table 4 Risk level table

[0159]

[0160]

[0161] S5、Utilize the vehicle control system to evade driving risk;

[0162] The vehicle control system comprises a lateral controller and a longitudinal controller; the lateral controller is a steering wheel steering controller, and the steering angle and speed of the steering wheel are controlled; the longitudinal controller is a throttle pedal controller and a brake pedal controller, and the opening and closing degree of the pedal is controlled;

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

[0164] Specifically, when the vehicle control system enters the A working mode, the longitudinal controller actively brakes, and the corresponding risk level is low; when the vehicle control system enters the B working mode, the lateral controller actively steers, and the corresponding risk level is medium; when the vehicle control system enters the C working mode, the corresponding risk level is high, and the vehicle enters the pre-collision active braking state to decelerate and stop at the maximum braking strength.

[0165] The working mode response is shown in Table 5 as follows:

[0166] Table 5 Working mode response table

[0167]

[0168] As shown in Figure 3 (b), that is, when ADB-T = CL, OT, SC, in L-ADB, take ADB-t < L-ADB-t < ADB-s time period, 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 light alarm to remind the driver to drive carefully;

[0169] As shown in Figure 3 (c), if ADB-RTI-P1 < L-ADB-P f < ADB-RTI-P2, ADB-RTI-rt = SL, ADB-RLI-rl = LL, indicating that the 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 working mode, that is, the longitudinal controller actively brakes;

[0170] As shown in Figure 3 (d), if ADB-RTI-P2 ≤ L-ADB-P fADB-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) and 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 mode working mode, and the lateral controller actively controls the steering;

[0171] As shown in Figure 3 (e), if ADB-RTI-P3≤L-ADB-P f , 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 mode working mode, and the vehicle enters the pre-collision active braking state to decelerate and stop at the maximum braking intensity;

[0172] As shown in Figure 4 , the control principle is to preferentially use the longitudinal controller for braking, and when only the longitudinal active braking cannot complete the danger avoidance, the lateral controller is used for active steering; at the same time, the control right of the driver has a higher priority, that is, during the active braking or active steering process, if the driver has an operation such as acceleration, braking or steering which is not consistent with the system working logic, the system exits the working immediately and returns the control right to the driver;

[0173] As shown in Figure 5 and Figure 6 , preferably, the longitudinal controller in the vehicle control system controls the vehicle to actively brake, the longitudinal controller adopts a hierarchical control design, including: an upper controller based on the LQR algorithm, which is used to solve the expected 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 an expected acceleration;

[0174] The state space equation establishing step takes the current speed v x and the expected speed v des of the vehicle as inputs, and derives a state space equation of the current acceleration a x and the expected acceleration a des of the vehicle;

[0175] The expected acceleration a des of the vehicle is calculated based on the established state space equation;

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

[0177] ε v =v des -v x

[0178] During the longitudinal control process, the vehicle's current acceleration and expected acceleration 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 theory, 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, 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 numerical values of the feedback gain matrix K; ε v is the speed error; a x is the current acceleration.

[0194] Further, the control information of the throttle pedal controller and the brake pedal controller is output by the lower controller adopting the PID control method based on acceleration feedback, and the driving according to the expected speed is realized through the throttle-brake calibration table.

[0195] The lower controller of the PID control method based on acceleration feedback functions to convert the expected acceleration output by the upper controller into expected throttle opening and brake pressure.

[0196] First, the expected acceleration output by the upper controller is controlled in the manner of "feedforward + feedback", and the PID control algorithm is introduced for adjustment; second, the acceleration is converted into expected throttle opening and brake pressure through the longitudinal calibration table. The longitudinal calibration table represents the response characteristics of the vehicle to the throttle and brake at different speeds, the input of which is the speed and expected acceleration, and the output is the throttle opening or brake pressure value. After interpolation, a calibration table containing all throttle and brake values is generated, so that at any given speed and acceleration, there is a unique throttle and brake value corresponding thereto.

[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 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. 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 with the transmission; the brake pedal controller information output is output to the brake device.

[0199] As Figure 7 and Figure 8 shown, preferably, the lateral controller in the vehicle control system actively controls the steering of the vehicle, and the lateral controller is a feedforward LQR control.

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

[0201] When the expected path is tracked, the lateral error e d and the heading error affect the tracking performance. The lateral error ed is the lateral error; e is the heading error; k r is the lateral error; e is the lateral error; e is the lateral error; e is the lateral error; e

[0202]

[0203] is the lateral error; e d is the lateral error; e is the lateral error; e r is the lateral error; e x is the lateral error; e y is the lateral error; e is the lateral error; e

[0204] is the lateral error; e is the lateral error; e is the lateral error; e is the lateral error; e

[0205] is the lateral error; e is the lateral error; e is the lateral error; e

[0206] is the lateral error; e is the lateral error; e rr is the lateral error; e d is the lateral error; e is the lateral error; e is the lateral error; e is the lateral error; e is the lateral error; e r is the lateral error; e x is the lateral error; e f is the lateral error; e r is the lateral error; e z is the lateral error; e is the lateral error; e

[0207] is the lateral error; e is the lateral error; e

[0208] First, the LQR is discretized using Euler method, and then the performance objective function of the system is constructed to determine the optimal control input u(k) 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, P is the solution of the 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] Further, the trajectory curvature k r and the feedback matrix K are used to design a feedforward controller to calculate the front wheel steering angle increment δ f ; the feedforward PSO-LQR lateral controller outputs the final front wheel steering angle control amount u(k) to the steering wheel angle controller of the vehicle to complete the lateral control.

[0210] To eliminate the steady-state error, a feedforward controller is added to the LQR controller, and the optimal front wheel steering 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 to eliminate the steady-state error, and the front wheel steering angle increment δ f output by the feedforward controller is

[0214] When the system is stable, the steady-state error of the heading error is which can make the heading error θ-θ r = 0, and 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. 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 F is the risk potential energy of the interaction risk field between the main vehicle j and the surrounding vehicles i. The greater the risk potential energy, the greater the potential danger between the main vehicle and the surrounding vehicles. 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 of λ1 is 0.056; λ2 is -0.169; λ3 is 2; λ4 is -0.029; α is 0.029; τ is 2.717; 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: 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.

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: 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.

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: 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.

5. The method for controlling the operation of a main vehicle based on identification of a road environment traffic risk field according to claim 4, 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.

6. The method for controlling the operation of a main vehicle based on identification of a road environment traffic risk field according to claim 4, 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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