A multi-axle vehicle real-time motion planning method based on event triggering mechanism

Abstract

The application discloses a kind of multi-axle car real-time motion planning method based on event trigger mechanism, to realize the more real-time motion planning process of multi-axle car based on scene, specifically includes the following steps: (1) using sensor obtains the initial information of multi-axle car and obstacle, including obstacle relative distance, obstacle speed, obstacle heading angle, car position, car heading angle, car speed;(2) design a kind of multi-axle car motion state transition module based on event trigger mechanism;(3) real-time generation motion planning path for different motion states of multi-axle car, generate smooth curve from the current position of multi-axle car to target point as candidate path based on the initial information of multi-axle car and obstacle, construct cost function to obtain the best path.The application introduces time trigger mechanism into multi-axle car real-time motion planning and constructs collision detection circle to multi-axle car, more quickly respond to environmental change while further guarantee driving safety, can be used in dynamic scene.

Description

Technical Field

[0001] This invention belongs to the field of automotive motion planning technology, specifically a real-time motion planning method for multi-axle vehicles based on an event-triggered mechanism. Background Technology

[0002] Motion planning is a crucial component of autonomous vehicles. The planning process should respond to environmental changes as real-time as possible to ensure safety. Traditional hierarchical sequential motion planning systems execute at a fixed frequency, and the results cannot respond to input changes in real time. Considering the unique dimensions of multi-axle vehicles, an event-triggered mechanism is introduced into real-time motion planning for multi-axle vehicles. This mechanism allows for targeted invocation of motion state transition modules based on the current scenario, resulting in faster response to environmental changes and higher planning efficiency.

[0003] Existing patents, such as CN110162046B (an invention patent), propose a path-following method for unmanned vehicles based on event-triggered model predictive control, and CN116540742A (an invention patent), propose a dynamic path planning and obstacle avoidance method and system for unmanned vehicles. The former, within a vehicle kinematics modeling framework, uses global position information and kinematic constraints to solve for the path-following controller using model predictive control and introduces an event-triggered mechanism. The latter, by setting a road baseline, can generate multiple candidate paths and obtain the optimal path without iteration. Both require high accuracy in both road and model data; even small model errors can easily lead to computational planning errors. Summary of the Invention

[0004] This invention aims to achieve a more real-time scene-based motion planning process for multi-axle vehicles. It proposes a real-time motion planning method for multi-axle vehicles based on an event triggering mechanism. This method uses sensors to acquire initial information about the multi-axle vehicle and obstacles, then uses the multi-axle vehicle motion state transition module to trigger events independently, and finally generates motion planning paths in real time for different motion states of the multi-axle vehicle. This method responds faster to environmental changes while further ensuring driving safety and is more applicable to dynamic scenarios.

[0005] To solve the above technical problems, the present invention is implemented using the following technical solution:

[0006] A real-time motion planning method for multi-axle vehicles based on an event-triggered mechanism includes the following steps:

[0007] S1: Use sensors to acquire initial information about the multi-axle vehicle and obstacles, including relative distance to obstacles, obstacle speed, obstacle heading angle, vehicle position, vehicle heading angle, and vehicle speed;

[0008] S2: Design a multi-axle vehicle motion state transition module based on an event-triggered mechanism;

[0009] S3: Generate motion planning paths in real time for different motion states of multi-axle vehicles. Based on the initial information of the multi-axle vehicle and obstacles, generate a cluster of smooth curves from the current position of the multi-axle vehicle to the target point as candidate paths, and construct a cost function to obtain the optimal path.

[0010] In a preferred embodiment, the vehicle motion state in step S2 includes the initial state S. i Cruise status S c Overtaking state S o Rear lane inspection status S l and braking state S b :

[0011] 1) Initial state S i Initialize all parameters and calculate the valid path. If no valid path is found, the vehicle remains in place and the system is re-initialized.

[0012] 2) Cruise Status S c The vehicle follows a predefined global path and travels normally.

[0013] 3) Overtaking state S o : Make overtaking decisions based on obstacles on the overall path ahead;

[0014] 4) Rear lane inspection status S l : Detect whether there are obstacles in the "cruise" area;

[0015] 5) Braking state S b When there is no valid driving route, the vehicle shall immediately stop moving;

[0016] The motion state transition conditions based on the event-triggered mechanism are:

[0017]

[0018] In the formula, s represents the longitudinal displacement of the vehicle.

[0019] l — Lateral displacement of the vehicle

[0020] d w Road width

[0021] θ — Angle between the vehicle body and the x-axis

[0022] N – Number of target candidate points.

[0023] In the preferred technical solution, the multi-axle vehicle motion path generation process in step S3 specifically includes:

[0024] S31: Generate candidate paths based on the multi-axle vehicle's starting point and target point, using the vehicle's current position L0(s0,l0) as the starting point and the target candidate point P. e (s e ,l e The result is obtained by calculation using formula (2);

[0025] P e (s e ,l e ) = P e (s0+max(Δs min ,v·τ chg ),d w / N) (2)

[0026] In the formula, Δs min ——Minimum value of Δs

[0027] v - vehicle speed

[0028] τ chg —Time increment

[0029] S32: Establishing constraints for the vehicle's lateral displacement l based on different motion states:

[0030]

[0031] In the formula, k1 (k1∈[0,1]) is the gain factor associated with the l-axis.

[0032] k2 (k2≥0) – a factor that distinguishes the driving range under different conditions.

[0033] S33: Establish safety boundary constraints for multi-axle vehicles: Use the width of each axle of the multi-axle vehicle as the diameter to draw a collision detection circle, cut the multi-axle vehicle body into n circumscribed rectangles, and use formula (4) to apply boundary constraints to the four corner points of all rectangles.

[0034]

[0035] In the formula, l pn —The positions of the four corner points of the nth collision detection circle

[0036] l n — Height of the center of mass of the nth expansion circle above the ground

[0037] d an — Distance from the center of mass of the nth expansion circle to the front of the vehicle

[0038] d bn — Distance from the center of mass of the nth expansion circle to the rear of the vehicle

[0039] w — width of multi-axle vehicle

[0040] S34: Construct the cost function W(x) using formula (5);

[0041] W(x) = w ref f ref (x)+w smo f smo (x)+w fin f fin (x) (5)

[0042] In the formula, x is the candidate path label.

[0043] f ref (x), f smo (x), f fin (x) — Reference line cost function, smoothing cost function, final state cost function

[0044] w ref w smo w fin — Weights of the reference line cost function, weights of the smoothing cost function, and weights of the final state cost function

[0045] S35: Combining the constraints of steps S32 and S33, calculate the cost function in formula (5) and obtain the path corresponding to the minimum cost as the real-time output of the multi-axle vehicle planning path.

[0046] Compared with the prior art, the advantages of the present invention are:

[0047] 1. The multi-axle vehicle real-time motion planning method based on event triggering mechanism described in this invention introduces event triggering mechanism into multi-axle vehicles and establishes a corresponding motion transfer module. Compared with traditional fixed frequency sampling planning, the event triggering mechanism only works when a predetermined event occurs, effectively reducing the amount of motion planning calculation.

[0048] 2. The multi-axle vehicle real-time motion planning method based on event triggering mechanism described in this invention generates a cluster of smooth curves from the current position of the multi-axle vehicle to the target point as candidate paths based on the initial information of the multi-axle vehicle and obstacles, allowing for real-time selection of effective paths.

[0049] 3. The event-triggered mechanism-based real-time motion planning method for multi-axle vehicles described in this invention addresses the large size of multi-axle vehicles by establishing safety boundary constraints for multi-axle vehicles using the width of each axle as the diameter of a collision detection circle, thereby further improving the safety of multi-axle vehicles during operation. Attached Figure Description

[0050] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0051] Figure 1This is a flowchart of a real-time motion planning method for a multi-axle vehicle based on an event-triggered mechanism, as described in this invention.

[0052] Figure 2 This is a schematic diagram of candidate paths in a multi-axle vehicle real-time motion planning method based on an event-triggered mechanism as described in this invention.

[0053] Figure 3 This is a schematic diagram of the collision detection circle in the real-time motion planning method for a multi-axle vehicle based on an event-triggered mechanism described in this invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0055] The invention will now be further described with reference to the accompanying drawings.

[0056] See Figure 1 This invention provides a real-time motion planning method for multi-axle vehicles based on an event-triggered mechanism, specifically including the following steps:

[0057] S1: Use sensors to acquire initial information about the multi-axle vehicle and obstacles, including relative distance to obstacles, obstacle speed, obstacle heading angle, vehicle position, vehicle heading angle, and vehicle speed;

[0058] S2: Design a multi-axle vehicle motion state transition module based on an event-triggered mechanism;

[0059] S3: Generate motion planning paths in real time for different motion states of multi-axle vehicles. Based on the initial information of the multi-axle vehicle and obstacles, generate a cluster of smooth curves from the current position of the multi-axle vehicle to the target point as candidate paths, and construct a cost function to obtain the optimal path.

[0060] The vehicle motion state in step S2 includes the initial state S. i Cruise status S c Overtaking state S o Rear lane inspection status S l and braking state S b :

[0061] 1) Initial state S i Initialize all parameters and calculate the valid path. If no valid path is found, the vehicle remains in place and the system is re-initialized.

[0062] 2) Cruise Status S cThe vehicle follows a predefined global path and travels normally.

[0063] 3) Overtaking state S o : Make overtaking decisions based on obstacles on the overall path ahead;

[0064] 4) Rear lane inspection status S l : Detect whether there are obstacles in the "cruise" area;

[0065] 5) Braking state S b When there is no valid driving route, the vehicle shall immediately stop moving;

[0066] The motion state transition conditions based on the event-triggered mechanism are:

[0067]

[0068] In the formula, s represents the longitudinal displacement of the vehicle.

[0069] l — Lateral displacement of the vehicle

[0070] d w Road width

[0071] θ — Angle between the vehicle body and the x-axis

[0072] N – Number of target candidate points.

[0073] The multi-axle vehicle motion path generation process in step S3 is as follows:

[0074] S31: See also Figure 2 Candidate paths are generated based on the starting point and target point of the multi-axle vehicle, with the vehicle's current position L0(s0,l0) as the starting point and the target candidate point P as the target. e (s e ,l e The result is obtained by calculation using formula (2);

[0075] P e (s e ,l e ) = P e (s0+max(Δs min ,v·τ chg ),d w / N) (2)

[0076] In the formula, Δs min ——Minimum value of Δs

[0077] v - vehicle speed

[0078] τ chg —Time increment

[0079] S32: Establishing constraints for the vehicle's lateral displacement l based on different motion states:

[0080]

[0081] In the formula, k1 (k1∈[0,1]) is the gain factor associated with the l-axis.

[0082] k2 (k2≥0) – a factor that distinguishes the driving range under different conditions.

[0083] S33: See also Figure 3 Establish safety boundary constraints for multi-axle vehicles: use the width of each axle of the multi-axle vehicle as the diameter to draw a collision detection circle, cut the multi-axle vehicle body into n circumscribed rectangles, and use formula (4) to apply boundary constraints to the four corner points of all rectangles.

[0084]

[0085] In the formula, l pn —The positions of the four corner points of the nth collision detection circle

[0086] l n — Height of the center of mass of the nth expansion circle above the ground

[0087] d an — Distance from the center of mass of the nth expansion circle to the front of the vehicle

[0088] d bn — Distance from the center of mass of the nth expansion circle to the rear of the vehicle

[0089] w — width of multi-axle vehicle

[0090] S34: Construct the cost function W(x) using formula (5);

[0091] W(x) = w ref f ref (x)+w smo f smo (x)+w fin f fin (x) (5)

[0092] In the formula, x is the candidate path label.

[0093] f ref (x), f smo (x), f fin (x) — Reference line cost function, smoothing cost function, final state cost function

[0094] w ref w smo w fin — Weights of the reference line cost function, weights of the smoothing cost function, and weights of the final state cost function

[0095] S35: Combining the constraints of steps S32 and S33, calculate the cost function in formula (5) and obtain the path corresponding to the minimum cost as the real-time output of the multi-axle vehicle planning path.

[0096] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A real-time motion planning method for a multi-axle vehicle based on an event-triggered mechanism, characterized in that, Includes the following steps: S1: Use sensors to acquire initial information about the multi-axle vehicle and obstacles, including relative distance to obstacles, obstacle speed, obstacle heading angle, vehicle position, vehicle heading angle, and vehicle speed; S2: Design a multi-axle vehicle motion state transition module based on an event-triggered mechanism, as follows: Vehicle motion states include initial state Cruise status Overtaking Rear lane inspection status and braking state : 1) Initial state Initialize all parameters and calculate the valid path. If no valid path is found, the vehicle remains in place and the system is re-initialized. 2) Cruise Status The vehicle follows a predefined global path and travels normally. 3) Overtaking situation : Make overtaking decisions based on obstacles on the overall path ahead; 4) Rear lane inspection status : Detect whether there are obstacles in the "cruise" area; 5) Braking status When there is no valid driving route, the vehicle shall immediately stop moving; The motion state transition conditions based on the event-triggered mechanism are: （1）； In the formula, —Longitudinal displacement of the vehicle; — Lateral displacement of the vehicle; —Road width; —The angle between the vehicle body and the horizontal axis; —Number of target candidate points; S3: Real-time motion planning paths are generated for multi-axle vehicles under different motion states. Based on the initial information of the multi-axle vehicle and obstacles, a family of smooth curves from the current position of the multi-axle vehicle to the target point is generated as candidate paths. A cost function is constructed to obtain the optimal path, as detailed below: The specific process of generating the motion path for a multi-axle vehicle is as follows: S31: Generate candidate paths based on the multi-axle vehicle's starting point and target point, using the vehicle's current position. As a starting point, target candidate points Calculated using formula (2); （2）； In the formula, —— The minimum value; —Speed; —Time increment; S32: Establishing constraints for the vehicle's lateral displacement l based on different motion states: （3）； In the formula, --and Axis-dependent gain factor; —Factors that differentiate driving range under different conditions; S33: Establish safety boundary constraints for multi-axle vehicles: Draw collision detection circles with the width of each axle of the multi-axle vehicle as the diameter, and cut the multi-axle vehicle body into... For each bounding rectangle, use formula (4) to apply boundary constraints to the four corner points of all rectangles; （4）； In the formula, ——No. The positions of the four corner points of the collision detection circle; ——No. The height of the center of mass of the expanding circle above the ground; ——No. The distance from the center of mass of the expansion circle to the front of the vehicle; ——No. The distance from the center of mass of the expansion circle to the rear of the vehicle; —Multi-axle vehicle width; S34: Construct the cost function using formula (5) ; （5）； In the formula, —Candidate path label; , , —Reference line cost function, smoothing cost function, final state cost function; , , —Reference line cost function weights, smoothing cost function weights, and final state cost function weights; S35: Combining the constraints of steps S32 and S33, calculate the cost function in formula (5) and obtain the path corresponding to the minimum cost as the real-time output of the multi-axle vehicle planning path.

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