Abstract
A global reference path generated by a path search algorithm based on a road-level driving map cannot be directly used to complete the efficient autonomous path-following motion of autonomous vehicles due to the large computational load and insufficient path accuracy. To solve this problem, this paper proposes a lane-level bidirectional hybrid path planning method based on a high-definition map (HD map), which effectively completes the high-precision reference path planning task. First, the global driving environment information is extracted from the HD map, and the lane-level driving map is constructed. Real value mapping from the road network map to the driving cost is realized based on the road network information, road markings, and driving behavior data. Then, a hybrid path search method is carried out for the search space in a bidirectional search mode, where the stopping conditions of the search method are determined by the relaxation region in the two search processes. As the search process continues, the dimension of the relaxation region is updated to dynamically adjust the search scope to maintain the desired search efficiency and search effect. After the completion of the bidirectional search, the search results are evaluated and optimized to obtain the reference path with the optimal traffic cost. Finally, in an HD map based on a real scene, the path search performance of the proposed algorithm is compared with that of the simple bidirectional Dijkstra algorithm and the bidirectional BFS search algorithm. The results show that the proposed path search algorithm not only has a good optimization effect, but also has a high path search efficiency.
Highlights
Given the source point and target point, a road-level driving map can help a vehicle select the optimal path, which can be used as a reference to guide human drivers to the destination [1]
This paper proposes a bidirectional hybrid path search (BHPS) method based on an high-definition map (HD map)
A bidirectional hybrid global path search method based on HD maps is proposed, and this method realizes the lane-level path planning task
Summary
Given the source point and target point, a road-level driving map can help a vehicle select the optimal path, which can be used as a reference to guide human drivers to the destination [1]. Since the road-level reference path does not have high-precision position information, the guidance information provided for the automatic driving vehicle is too vague to be directly used for the motion control of the autonomous vehicle. Autonomous vehicles mainly conduct the exploration and testing of local path planning, and lateral and longitudinal motion control based on recorded trajectories. This will make it difficult to deploy the algorithm in an autonomous vehicle. The trajectory planning method based on a high-definition map (HD map) can provide clear and delicate lane-level path guidance information for autonomous vehicles and improve the control performance [2]. HD map based environment information and trajectory planning method have become a necessary demand for autonomous driving above L3 level [3,4]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
