Abstract
The existing automatic parking algorithms often neglect the unknown obstacles in the parking environment, which causes a hidden danger to the safety of the automatic parking system. Therefore, this paper proposes parking space detection and path planning based on the VIDAR method (vision-IMU-based detection and range method) to solve the problem. In the parking space detection stage, the generalized obstacles are detected based on VIDAR to determine the obstacle areas, and then parking lines are detected by the Hough transform to determine the empty parking space. Compared with the parking detection method based on YOLO v5, the experimental results demonstrate that the proposed method has higher accuracy in complex parking environments with unknown obstacles. In the path planning stage, the path optimization algorithm of the A ∗ algorithm combined with the Bezier curve is used to generate smooth curves, and the environmental information is updated in real time based on VIDAR. The simulation results show that the method can make the vehicle efficiently avoid the obstacles and generate a smooth path in a dynamic parking environment, which can well meet the safety and stationarity of the parking requirements.
Highlights
In addition to parking space detection technology, path planning is a key technology to realize automatic parking of intelligent vehicles. e purpose of path planning is to find a feasible and safe path from the current state to the target state. e existing path planning algorithms applied to the automatic parking systems are mainly divided into two types: geometric methods and graph methods. e geometric method considers the minimum turning radius under the geometric constraints of the environment and solves the problem by combining geometric primitives, such as straight lines, circles, and spirals [10,11,12,13,14]
E graph-searching algorithm applied to the automatic parking system is developed from the existing robot path planning algorithm. e graph-searching methods include Hybrid-A∗ or RRT [18,19,20]. ese methods first construct a graph from the environment and find an optimal path in the graph. ese methods apply vehicle kinematics constraints based on the existing path planning methods of mobile robots. e most common planning methods for mobile robots are the traditional grid method based on the heuristic genetic algorithm, the artificial potential field method [21,22,23], and the A∗ algorithm based on node search [24]
VIDAR is introduced into parking space detection to detect the generalized obstacles, which significantly reduces the missed detection of unknown obstacles and improves the accuracy of parking space detection
Summary
VIDAR is used to detect generalized obstacles in complex parking space environment, which can effectively improve the accuracy of parking space detection. When the vehicle exits from the vertical parking space (see Figure 9(b)), it will first move along the long axis parking line for a distance dv and move to the target point through an arc path with a radius of R3 and an arc angle of θ3. When the vehicle exits from the tilted parking space (see Figure 9(c)), it will first move along the long axis parking line for a certain distance dt and move to the target point through an arc path with a radius of R4 and an arc angle of θ4. E overall steps of parking space detection and path planning based on VIDAR are as follows: Image at t. Parking experiments were conducted in a grid diagram with generalized obstacles, and the A∗ algorithm based on node search was used to plan the path. When the distance between the nodes is too small, the vehicle curvature constraint formula cannot be satisfied, which will increase unnecessary calculation and overall (d)
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