Abstract
The Jump Point Search (JPS) algorithm is adopted for local path planning of the driverless car under urban environment, and it is a fast search method applied in path planning. Firstly, a vector Geographic Information System (GIS) map, including Global Positioning System (GPS) position, direction, and lane information, is built for global path planning. Secondly, the GIS map database is utilized in global path planning for the driverless car. Then, the JPS algorithm is adopted to avoid the front obstacle, and to find an optimal local path for the driverless car in the urban environment. Finally, 125 different simulation experiments in the urban environment demonstrate that JPS can search out the optimal and safety path successfully, and meanwhile, it has a lower time complexity compared with the Vector Field Histogram (VFH), the Rapidly Exploring Random Tree (RRT), A*, and the Probabilistic Roadmaps (PRM) algorithms. Furthermore, JPS is validated usefully in the structured urban environment.
Highlights
The driverless car is aimed at relieving a burden for drivers in an urban environment; it can regulate driving behavior, such as changing lanes frequently and arbitrarily
A feasible trajectory is based on quadratic programming (QP); it is proposed in [3] for path planning in three-dimensional space
Though the car can avoid static or dynamic obstacles, it’d better to drive in the center of lane a method [4] is proposed based on vision, long-distance lane perception and front vehicle location detection, but it is applied to a special environment
Summary
The driverless car is aimed at relieving a burden for drivers in an urban environment; it can regulate driving behavior, such as changing lanes frequently and arbitrarily. Though the car can avoid static or dynamic obstacles, it’d better to drive in the center of lane a method [4] is proposed based on vision, long-distance lane perception and front vehicle location detection, but it is applied to a special environment (two-lane highways). The driverless car system is applied to public transportation, but in a more dynamic urban environment. A vision-based approach is addressed to identify the leader vehicle [9], and a path planning algorithm is proposed for autonomous driving in a complex urban environment [10]. This research does not focus on real time local path planning in the structured urban environment; this paper will introduce a novel path planning method based on JPS in that environment.
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