Drift-assisted electric vehicle chassis collaborative obstacle avoidance method

Abstract

The invention discloses a drifting-assisted electric vehicle chassis collaborative obstacle avoidance method, and relates to the technical field of automobile safety, and the drifting-assisted electric vehicle chassis collaborative obstacle avoidance method comprises the steps: obtaining related vehicle parameters, and obtaining the current state quantity of a vehicle during real-time driving; acquiring obstacle information, and calculating collision time; judging whether drift-assisted obstacle avoidance is needed in the current working condition according to the collision time; drifting obstacle avoidance trajectory planning is carried out on the current scene, and a reference obstacle avoidance path and a corresponding vehicle reference state quantity are obtained; performing drifting motion control by adopting an inner and outer ring control algorithm based on the vehicle reference state quantity, and outputting transverse and longitudinal tire forces as virtual control quantities; and transverse and longitudinal tire forces are distributed, and the virtual control quantity is converted into the real input quantity of an actuator. According to the method, the drifting-assisted emergency obstacle avoidance strategy is introduced under the limiting condition, the problem that a traditional braking and steering obstacle avoidance method cannot deal with the special working condition is solved, and the safety and maneuverability of the vehicle under the limiting condition are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of automobile safety technology, and in particular to a drift-assisted electric vehicle chassis collaborative obstacle avoidance method. Background Art

[0002] With the continuous development of vehicle intelligent technology, more and more driving assistance systems aimed at improving vehicle motion performance have been widely used. Among these systems, improving vehicle safety remains the most important goal. At present, modern vehicle active safety systems represented by direct yaw control (DYC) and active steering system (AFS) have significantly improved the stability of driving vehicles. By controlling the yaw characteristics, the vehicle is kept within a safe range, avoiding the occurrence of side slip and tail swing out of control, thereby effectively enhancing driving safety. However, the essence of existing active safety systems is to limit the dynamic state of the vehicle to a preset "stable" area, usually by sacrificing vehicle maneuverability to enhance the safety of the vehicle. Although this approach is conservative, it also leads to excessive restrictions on the vehicle's extreme handling performance and is unable to cope with some extreme working conditions.

[0003] Inspired by the extreme driving skills of professional racing drivers, drifting technology provides a feasible idea for improving the safety of vehicles under extreme conditions. Drifting is a special high-center-of-mass side slip angle driving condition that can maintain stability when the vehicle is close to losing control and improve handling through appropriate lateral slip. By using drifting technology, the maneuverability of the vehicle can be significantly improved, and it can play an excellent advantage in some extreme scenarios, such as turning on low-attachment roads and cornering in narrow spaces. In addition, drifting can help the vehicle quickly adjust its body posture, which helps to adjust the vehicle's future movement trajectory within a limited time and space, thereby achieving emergency avoidance control under high maneuverability, providing new possibilities for ensuring driving safety under extreme driving conditions.

[0004] In the prior art, although some patent documents mention the use of drift technology to avoid obstacles, the existing drift control algorithms are mostly oriented towards steady-state drift, and the research objects are mostly concentrated on rear-wheel drive vehicles. They can only track simple arc trajectories, and cannot track dynamic drift trajectories with complex speed, posture, and paths.

[0005] In addition, traditional trajectory planning methods usually rely on simplified kinematic models for trajectory generation, often ignoring the vehicle dynamic response. During extreme drift, the tire force will break through the linear range and enter the saturation state. At this time, the tire force presents significant nonlinear characteristics, accompanied by the complex coupling phenomenon of longitudinal slip and lateral side deviation, which cannot effectively guarantee the safety and accuracy of planning.

[0006] Therefore, technicians in this field are committed to developing a drift-assisted electric vehicle chassis collaborative obstacle avoidance method, aiming to solve special working conditions that traditional braking and steering obstacle avoidance methods cannot cope with, and can significantly improve the safety and maneuverability of the vehicle under extreme conditions. Summary of the invention

[0007] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is how to achieve emergency avoidance control under high maneuverability by introducing drift assistance in extreme scenarios that traditional obstacle avoidance strategies cannot solve.

[0008] To achieve the above object, the present invention provides a drift-assisted electric vehicle chassis coordinated obstacle avoidance method, the method comprising the following steps:

[0009] S1. Obtain relevant vehicle parameters and obtain the current state of the vehicle in real-time driving;

[0010] S2, obtain obstacle information and calculate collision time;

[0011] S3, judging whether the current working condition requires drift-assisted obstacle avoidance according to the collision time, if yes, proceeding to step S4, if no, taking steering or braking to avoid obstacles;

[0012] S4, performing drift obstacle avoidance trajectory planning for the current scene, obtaining a reference obstacle avoidance path and a corresponding vehicle reference state;

[0013] S5. Based on the vehicle reference state quantity, adopt the inner and outer loop control algorithm to perform drift motion control, and output the lateral and longitudinal tire forces as virtual control quantities;

[0014] S6. Distribute the lateral and longitudinal tire forces and convert the virtual control quantity into the real input quantity of the actuator.

[0015] Furthermore, the current state of the vehicle includes the current speed of the vehicle, the current position information of the vehicle, the current yaw rate of the vehicle, and the current sideslip angle of the center of mass of the vehicle; the obstacle information includes the current distance between the vehicle and the obstacle in front, and the obstacle speed.

[0016] Furthermore, the collision time is calculated as follows:

[0017] TTC=S / (V0-V obs )

[0018] Among them, S is the current distance between the vehicle and the obstacle in front, V0 is the current speed of the vehicle, and V obs is the obstacle speed.

[0019] Furthermore, the step S3 further includes:

[0020] S301. Calculate the distance D required for current braking to avoid obstacles: D = V0 * V0 / (2μg);

[0021] S302. If D < S, take braking to avoid obstacles; if D > S, determine whether the collision time is less than the collision time threshold, and enter step S303;

[0022] S303. If the collision time is less than the collision time threshold, take drift-assisted obstacle avoidance; if the collision time is greater than or equal to the collision time threshold, take steering obstacle avoidance; the collision time threshold is 1.4 s.

[0023] Further, step S4 further includes:

[0024] S401. Obtain the current vehicle position information, obstacle information, and lane boundary information by combining the upper-layer perception information with the road map information;

[0025] S402. Input the current vehicle state quantity as the initial value of the planning algorithm, and input the obstacle information and the lane boundary information as the constraints of the algorithm to establish an optimal control model;

[0026] S403. Use the Gaussian pseudospectral method to convert the optimal control problem into a nonlinear optimization problem, and call the interior point method solver to solve it;

[0027] S404. Solve to obtain the reference obstacle avoidance path and the vehicle reference state quantity.

[0028] Further, the vehicle reference state quantity includes the desired vehicle speed, the desired yaw rate, and the desired centroidal side slip angle.

[0029] Further, the inner and outer loop control algorithms include: the outer loop control algorithm realizes the tracking control of the reference obstacle avoidance path; the inner loop control algorithm realizes the stability of the drift dynamics state quantity based on the error dynamics and the dynamic inversion method.

[0030] Further, step S5 further includes:

[0031] S501. Perform kinematic expectation calculation, and calculate the desired heading angle and the desired heading angular velocity based on the current vehicle position information and the reference obstacle avoidance path;

[0032] S502. Correct the current vehicle yaw rate;

[0033] S503. Perform error dynamics calculation, receive the desired yaw rate input by the outer loop, as well as the desired centroidal side slip angle and the desired vehicle speed, and combine the current dynamic state of the vehicle to design the expected value of the dynamic differential quantity;

[0034] S504, performing dynamic inversion, based on the expected value of the dynamic differential component, converting the dynamic inversion problem into an optimization problem, solving it based on a sequential quadratic programming algorithm, and obtaining the lateral and longitudinal tire forces.

[0035] Furthermore, the step S502 also includes: based on the expected kinematic state quantity obtained in the step S501, combined with the current state quantity of the vehicle, closed-loop feedback is performed on the lateral position error and heading angle error of the current state, so as to realize real-time correction of the current yaw angular velocity of the vehicle, thereby ensuring that the vehicle can accurately track the predetermined trajectory.

[0036] Furthermore, in step S6, the virtual control amount is converted into motor torque and front wheel steering angle based on the tire inverse model.

[0037] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0038] 1. The present invention realizes the coordinated work of different obstacle avoidance strategies by selecting appropriate obstacle avoidance strategies (drifting, steering or braking) according to the collision time, thereby improving the active safety and responsiveness of electric vehicles in complex obstacle avoidance scenarios;

[0039] 2. The present invention provides a trajectory planning method for drift obstacle avoidance under extreme working conditions, which fully considers the nonlinear characteristics of the vehicle under extreme working conditions and can freely design the cost function, and ultimately can simultaneously plan the obstacle avoidance path and the state quantity in the obstacle avoidance process;

[0040] 3. The present invention is aimed at the intelligent chassis overdrive system and designs an active controllable dynamic drift control technology suitable for the chassis of electric vehicles; by constructing an inner and outer loop drift control framework, the outer loop is responsible for tracking and controlling the planned path, while the inner loop uses error dynamics and dynamic inversion methods to achieve the stability of the drift dynamic state quantity, thereby ensuring the controllability and stability of the drift process in a complex environment.

[0041] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a method flow chart of a preferred embodiment of the present invention;

[0043] Figure 2 It is a TTC-based obstacle avoidance strategy decision flow chart of a preferred embodiment of the present invention;

[0044] Figure 3 is a flow chart of a drift obstacle avoidance planning method according to a preferred embodiment of the present invention;

[0045] Figure 4 It is a flowchart of the drift motion control method of a preferred embodiment of the present invention. Specific embodiments

[0046] The following introduces multiple preferred embodiments of the present invention with reference to the accompanying drawings of the specification to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0047] In the drawings, components with the same structure are denoted by the same reference numerals, and components with similar structures or functions everywhere are denoted by similar reference numerals. The size and thickness of each component shown in the drawings are arbitrarily shown, and the present invention does not limit the size and thickness of each component. To make the illustration clearer, the thickness of some components in the drawings is appropriately exaggerated.

[0048] This embodiment provides a method for collaborative obstacle avoidance of an electric vehicle chassis assisted by drift, as Figure 1 shown, including the following steps:

[0049] S1. Obtain relevant vehicle parameters and obtain the current state variables of the vehicle during real-time driving; the relevant vehicle parameters include wheelbase, track width, overall vehicle length, overall vehicle width, center of mass height, overall vehicle mass, moment of inertia, etc.; the current state variables of the vehicle include the current vehicle speed, the current position information of the vehicle, and the current yaw rate of the vehicle.

[0050] S2. Obtain obstacle information and calculate the time to collision (TTC); the obstacle information includes the current distance between the vehicle and the obstacle ahead and the obstacle speed.

[0051] S3. Determine whether drift-assisted obstacle avoidance is required for the current working condition according to the time to collision. If so, go to step S4; if not, take steering obstacle avoidance or braking obstacle avoidance. As Figure 2 shown, specifically including:

[0052] S301. According to the current distance S between the vehicle and the obstacle ahead measured by the millimeter-wave radar, and the obstacle speed V obs ;

[0053] S302. Read the current vehicle speed V0 and calculate the time to collision TTC = S / (V0 - V obs ); calculate the distance D required for current braking obstacle avoidance = V0 * V0 / (2μg).

[0054] S303. If D < S, take braking obstacle avoidance; if D > S, determine whether the time to collision is less than the time to collision threshold, and go to step S304;

[0055] S304: If the collision time is less than the collision time threshold, drift-assisted obstacle avoidance is adopted; if the collision time is greater than or equal to the collision time threshold, steering is adopted to avoid the obstacle; the collision time threshold is 1.4s.

[0056] This embodiment traverses and calculates the safe distances for obstacle avoidance at different vehicle speeds and different strategies, and finally derives the collision time threshold by combining the distance and the vehicle speed.

[0057] S4, plan the drift obstacle avoidance trajectory for the current scene, and obtain a feasible reference obstacle avoidance path and the corresponding vehicle reference state; the vehicle reference state includes the expected vehicle speed, the expected yaw rate, and the expected center of mass sideslip angle. Figure 3 As shown, specifically including:

[0058] S401, obtaining vehicle current position information, obstacle information, and lane boundary information through upper layer perception information combined with road map information;

[0059] S402, input the current state of the vehicle as the initial value of the planning algorithm, input obstacle information and lane boundary information as constraints of the algorithm, and establish an optimal control model;

[0060] S403, using Gaussian pseudo-spectral method to convert the optimal control problem into a nonlinear optimization problem, and calling an interior point method solver to solve it;

[0061] S404: Obtain a reference obstacle avoidance path and a reference state of the vehicle.

[0062] S5. Based on the planned vehicle reference state, the inner and outer loop control algorithms are used to control the drift motion, and the lateral and longitudinal tire forces are output as virtual control quantities. The outer loop control algorithm realizes the tracking control of the reference obstacle avoidance path; the inner loop control algorithm realizes the stability of the drift dynamic state based on the error dynamics and dynamic inversion method. Figure 4 As shown, specifically including:

[0063] S501, performing kinematic expectation calculation, and calculating the expected heading angle and expected heading angular velocity at the current position of the vehicle based on the current position information of the vehicle and the planned reference obstacle avoidance path;

[0064] S502, perform yaw rate correction, based on the expected kinematic state obtained in step S501, combined with the current state of the vehicle, perform closed-loop feedback on the lateral position error and heading angle error of the current state, realize real-time correction of the yaw rate, and ensure that the vehicle can accurately track the predetermined trajectory; wherein the expected kinematic state includes the expected heading angle, the expected heading angular velocity, and the vehicle reference state.

[0065] S503, performing error dynamics calculation, receiving the expected yaw rate input by the outer loop, and the expected center of mass sideslip angle and expected vehicle speed obtained by trajectory planning, and designing the expected value of the dynamic differential component in combination with the current dynamic state of the vehicle.

[0066] S504, performing dynamic inversion, based on the expected value of the dynamic differential component, converting the dynamic inversion problem into an optimization problem, solving it based on a sequential quadratic programming algorithm, and obtaining the lateral and longitudinal tire forces.

[0067] S6. Based on the tire inverse model, the lateral and longitudinal tire forces are distributed, and the virtual control quantity is converted into the real input quantity of the actuator, namely the motor torque and the front wheel steering angle.

[0068] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A drift-assisted electric vehicle chassis cooperative obstacle avoidance method, characterized in that: The method includes the following steps: S1. Obtain relevant vehicle parameters and the current vehicle state variables during real-time driving; S2. Obtain obstacle information and calculate the collision time; S3. Determine whether drift-assisted obstacle avoidance is required for the current working condition based on the collision time. If so, proceed to step S4. If not, adopt steering obstacle avoidance or braking obstacle avoidance; S4. Plan a drift obstacle avoidance trajectory for the current scenario to obtain a reference obstacle avoidance path and the corresponding vehicle reference state variables; S5. Based on the vehicle reference state variables, use an inner and outer loop control algorithm to perform drift motion control and output the longitudinal and lateral tire forces as virtual control variables; S6. Distribute the longitudinal and lateral tire forces to convert the virtual control variables into the actual input variables of the actuator.

2. The drift-assisted electric vehicle chassis coordinated obstacle avoidance method according to claim 1, characterized in that: The current vehicle state variables include the current vehicle speed, the current vehicle position information, the current vehicle yaw rate, and the current vehicle centroid side slip angle; the obstacle information includes the current distance between the vehicle and the obstacle ahead and the obstacle speed.

3. The drift-assisted electric vehicle chassis coordinated obstacle avoidance method according to claim 3, characterized in that: The calculation method of the collision time is as follows: TTC=S / (V0-V obs ) Among them, S is the current distance between the vehicle and the obstacle in front, V0 is the current speed of the vehicle, and V obs is the obstacle speed.

4. The drift-assisted electric vehicle chassis coordinated obstacle avoidance method according to claim 3, characterized in that: Step S3 further includes: S301. Calculate the distance D required for current braking obstacle avoidance, D = V0*V0 / (2μg); S302. If D < S, adopt braking obstacle avoidance; if D > S, determine whether the collision time is less than the collision time threshold and proceed to step S303; S303. If the collision time is less than the collision time threshold, adopt drift-assisted obstacle avoidance; if the collision time is greater than or equal to the collision time threshold, adopt steering obstacle avoidance; the collision time threshold is 1.4 s.

5. The drift-assisted electric vehicle chassis coordinated obstacle avoidance method according to claim 2, characterized in that: Step S4 further includes: S401. Obtain the current vehicle position information, obstacle information, and lane boundary information by combining upper-layer perception information with road map information; S402. Input the current vehicle state variables as the initial values of the planning algorithm, and input the obstacle information and the lane boundary information as the constraints of the algorithm to establish an optimal control model; S403. Use the Gaussian pseudospectral method to convert the optimal control problem into a nonlinear optimization problem, and call an interior point method solver to solve it; S404. Obtain the reference obstacle avoidance path and the vehicle reference state variables through solution.

6. The drift-assisted electric vehicle chassis coordinated obstacle avoidance method according to claim 5, characterized in that: The vehicle reference state variables include the desired vehicle speed, the desired yaw rate, and the desired centroid side slip angle.

7. The drift-assisted electric vehicle chassis coordinated obstacle avoidance method according to claim 6, characterized in that: The inner and outer loop control algorithm includes: the outer loop control algorithm realizes the tracking control of the reference obstacle avoidance path; the inner loop control algorithm realizes the stability of the drift dynamics state variables based on the error dynamics and the dynamics inversion method.

8. The drift-assisted electric vehicle chassis coordinated obstacle avoidance method according to claim 7, characterized in that: Step S5 further includes: S501. Perform kinematic expectation calculation, and calculate the desired heading angle and the desired heading angular velocity based on the current vehicle position information and the reference obstacle avoidance path; S502. Correct the current vehicle yaw rate; S503. Perform error dynamics calculation, receive the desired yaw rate input by the outer loop, as well as the desired centroid side slip angle and the desired vehicle speed, and design the expected value of the dynamic differential component in combination with the current dynamic state of the vehicle. S504, performing dynamic inversion, based on the expected value of the dynamic differential component, converting the dynamic inversion problem into an optimization problem, solving it based on a sequential quadratic programming algorithm, and obtaining the lateral and longitudinal tire forces.

9. The drift-assisted electric vehicle chassis coordinated obstacle avoidance method according to claim 8, characterized in that: The step S502 also includes: based on the expected kinematic state quantity obtained in the step S501, combined with the current state quantity of the vehicle, closed-loop feedback is performed on the lateral position error and heading angle error of the current state, so as to realize real-time correction of the current yaw angular velocity of the vehicle and ensure that the vehicle can accurately track the predetermined trajectory.

10. The drift-assisted electric vehicle chassis coordinated obstacle avoidance method according to claim 1, characterized in that: In step S6, the virtual control amount is converted into the motor torque and the front wheel steering angle based on the tire inverse model.

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