Improved RRT vehicle path planning method based on tabu search in V2X environment

Abstract

The invention discloses a vehicle path planning method in a V2X environment, and particularly relates to an improved RRT path planning method based on a dynamic search step size of tabu search. The method mainly comprises the following steps: initializing a starting point and an ending point, taking the starting point as a root node of a tree, and generating a search map according to real-time road and obstacle information by using a V2X technology; randomly generating points within a limited angle, generating a new node according to a dynamic search step length, detecting whether the new node intersects with an obstacle in the map, and if so, abandoning the node; if the current node is not intersected with the new node, adding the new node into the RRT spanning tree, adding a path between the current node and the new node into the path RRT spanning tree, and adding the accessed nodes into a taboo list to avoid re-accessing the nodes in subsequent search; checking whether the new node is close to the target point, if so, stopping searching, and finishing path planning; and repeating the above steps until the path connecting the starting point and the target point is found or the maximum number of iterations is reached.

Description

Technical Field

[0001] The present invention patent discloses a vehicle path planning method in a V2X environment, and in particular relates to an improved RRT path planning method with a dynamic search step based on taboo search. Background Art

[0002] Due to the large changes in traffic volume on highways, the high speed of vehicles, the complex and changeable environment, and the need for fast response and high reliability in lane change path planning to ensure safety during high-speed driving, traditional path planning algorithms currently have problems such as low planning efficiency and limited application scenarios, and cannot adapt to changes in different traffic environments and vehicle states. Redundant calculations and low efficiency in the planning process. At the same time, traditional algorithms have certain limitations in application scenarios of complex road conditions and high-density traffic, and it is difficult to meet the needs of efficient and safe lane changes.

[0003] For the problem of highway path planning, there are existing methods based on grid search, random sampling and geometric paths. The path planning algorithm based on grid search is to rasterize the known surrounding environment map, generate a raster map containing obstacle information and feasible domain information, and then use the search algorithm to find a drivable path with the lowest cost from the starting point to the target point. The random sampling method randomly generates sample points in the configuration space, connects the starting point, sample points, and target points for collision detection, and finally determines a collision-free path as the final trajectory. The geometric path planning algorithm refers to generating a smooth curve cluster through a certain function or interpolation fitting method, and then selecting an optimal path from the curve cluster based on a series of specific evaluation indicators.

[0004] In the existing random sampling RRT algorithm, in a dynamic environment, the obstacle position and space occupancy will change over time, which requires the algorithm to be able to update the path in real time to avoid collision or find a better path. In a complex environment, the tree structure is expanded by random sampling, resulting in low planning efficiency and failing to meet actual needs. Therefore, an efficient and accurate highway lane change path planning method is urgently needed. Summary of the invention

[0005] To solve the above problems, the present invention provides an improved RRT vehicle path planning method based on taboo search in a V2X environment, which aims to reduce the safety risk of vehicles changing lanes on highways, improve the efficiency of vehicle path planning, and better cope with complex traffic environments and dynamically changing road conditions.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme:

[0007] An embodiment of the present invention provides an improved RRT vehicle path planning method based on taboo search in a V2X environment, comprising the following steps:

[0008] Step 1: Set the starting point and target point of the algorithm, and use the starting point as the root node of the path tree. Define a planning map based on the actual driving conditions of the vehicle, including road conditions and obstacle distribution;

[0009] Step 2: Extend from the current node to the limited direction angle area with a dynamic search step size to generate a new node;

[0010] Step 3: Check whether the newly generated node collides with obstacles in the map. If a collision occurs, the node is discarded. If the new node is valid, it is added to the RRT generation tree and the path from the current node to the new node is recorded. At the same time, the current node is added to the taboo list to avoid revisiting these nodes in subsequent searches.

[0011] Step 4: Evaluate the distance between the new node and the target point. If the new node is close enough to the target point, terminate the search process and complete the path planning.

[0012] Step 5: If no path is found or the maximum number of iterations is not reached, repeat the above steps until a feasible path from the starting point to the target point is found or the preset iteration limit is reached.

[0013] Based on the above technical solutions, preferably, the taboo list in the taboo search algorithm is used to store nodes that have been explored to avoid repeated exploration of the same nodes during a short-term search process; the moving criterion checks whether the newly generated candidate solution is in the taboo list, and if so, other feasible neighborhood solutions are given priority.

[0014] On the basis of the above technical solution, preferably, the planning map is constructed based on data collected from the vehicle's own sensors using V2X communication technology, and maps drawn by radar, lidar, and cameras.

[0015] Based on the above technical solution, preferably, the dynamic search step is a search step dynamically calculated based on the speed of the current target vehicle, the direction angle between the node and the target point, and the spatial proportion of the obstacle. The initial value of the search step is set to step initial , and the next search step is calculated according to the formula:

[0016]

[0017] Where δ is the obstacle density factor, s n Represents the total area of ​​all obstacles in the area, s t represents the size of the entire map, β is the direction influencing factor, cosθ refers to the cosine value of the angle between the vector between the current node and the target point and the positive direction of the coordinate axis; θ thThe maximum steering angle range is limited.

[0018] Compared with the prior art, the present invention has the following beneficial technical effects:

[0019] 1. By using V2X communication to obtain real-time vehicle position and speed information, the improved RRT path planning method can dynamically adjust the path search strategy, significantly improving the real-time response capability of path planning, thereby more effectively responding to traffic changes and vehicle dynamics;

[0020] 2. The improved RRT method based on taboo search can optimize the path based on dynamic traffic flow and real-time vehicle position, effectively reduce traffic congestion, improve overall road utilization efficiency, and shorten vehicle lane change time;

[0021] 3. Combined with V2X technology, this method can obtain the status information of surrounding vehicles in real time, avoid potential collision risks, effectively improve the safety of path planning, and ensure traffic safety during driving;

[0022] 4. The improved RRT algorithm combined with the taboo search strategy can effectively handle large-scale vehicles and complex road networks, has good system scalability and adaptability, and is suitable for the development needs of future intelligent transportation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the specific implementation of the present invention is further described in detail below with reference to the accompanying drawings, wherein:

[0024] Figure 1 It is a flow chart of an improved RRT vehicle path planning method based on taboo search in a V2X environment of the present invention;

[0025] Figure 2 is a schematic diagram of the distribution of all obstacles in one embodiment of the present invention;

[0026] Figure 3 It is a schematic diagram of the original RRT expansion constructed;

[0027] Figure 4 is the final path planned in the example according to the method of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical scheme and advantages of the present invention more clear, the present invention is further described in detail in conjunction with the illustrations. The described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figure 1 The embodiment of the present invention discloses an improved RRT vehicle path planning method based on taboo search in a V2X environment, comprising the following steps:

[0030] Step 1: Set the starting point and target point of the algorithm, and determine the planning map according to the road conditions and the distribution of obstacles. Figure 2 shown.

[0031] Step 2: Plan a collision-free vehicle path. The original RRT expansion method expands toward the target point according to a fixed search step size. Figure 3 As shown, because the sampling points generated by the access are random and the sampling point expansion step is fixed, the search efficiency is low and the generated path does not meet the vehicle execution requirements. Therefore, a dynamic search step is introduced to limit the path expansion direction according to the set maximum steering angle. The dynamic step calculation formula is:

[0032]

[0033] Where δ is the obstacle density factor, s n Represents the total area of ​​all obstacles in the area, s t represents the size of the entire map, β is the direction influencing factor, cosθ refers to the cosine value of the angle between the vector between the current node and the target point and the positive direction of the coordinate axis; θ th The maximum steering angle range is limited.

[0034] Step 3: Check whether the newly generated node collides with obstacles in the map. If a collision occurs, discard the node. If the new node is valid, add it to the RRT generation tree and record the path from the current node to the new node. At the same time, define a taboo list, add the discarded nodes and valid nodes to the taboo list, and set the taboo time. Every time a new node is generated, check whether the node is in the taboo list. If not, add the node to the list and update the taboo list.

[0035] Step 4: Calculate the distance between the new node and the target point, and compare it with the preset threshold to decide whether to terminate the search. If the distance is less than or equal to the threshold, the algorithm believes that a path close enough to the target has been found, and then stops searching and constructs a complete path by backtracking. Finally, a complete path is planned as follows: Figure 4 shown.

Claims

1. An improved RRT vehicle path planning method based on taboo search in a V2X environment, characterized in that: include: Step 1: Set the starting point and target point of the algorithm, and use the starting point as the root node of the path tree. Define a planning map based on the actual driving conditions of the vehicle, including road conditions and obstacle distribution; Step 2: Extend from the current node to the limited direction angle area with a dynamic search step size to generate a new node; Step 3: Check whether the newly generated node collides with obstacles in the map. If a collision occurs, the node is discarded. If the new node is valid, it is added to the RRT generation tree and the path from the current node to the new node is recorded. At the same time, the current node is added to the taboo list to avoid revisiting these nodes in subsequent searches. Step 4: Evaluate the distance between the new node and the target point. If the new node is close enough to the target point, terminate the search process and complete the path planning. Step 5: If no path is found or the maximum number of iterations is not reached, repeat the above steps until a feasible path from the starting point to the target point is found or the preset iteration limit is reached.

2. The improved RRT vehicle path planning method based on taboo search in a V2X environment as claimed in claim 1, characterized in that: The planning map is constructed based on data collected from the vehicle's own sensors using V2X communication technology, and maps drawn by radar, lidar, and cameras.

3. The improved RRT vehicle path planning method based on taboo search in a V2X environment as claimed in claim 1, characterized in that: The dynamic search step length described in step 2 is a search step length dynamically calculated based on the speed of the current target vehicle, the direction angle between the node and the target point, and the spatial proportion of the obstacle.

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