High-Definition Map Combined Local Motion Planning and Obstacle Avoidance for Autonomous Driving

Abstract

Local motion planning plays an important role in an autonomous driving system. And applying mature local motion planning methods to real traffic scenarios with regular constraints is one of the keys to the applications of autonomous vehicles. In this paper, we present a local motion planning method combined with High-Definition (HD) maps. Through the HD map defined by OpenStreetMap, the local motion planner can obtain the prior knowledge of traffic scenarios and achieve path planning and optimization accordingly. In order to improve the safety and comfort of the obstacle avoiding process, we also propose an inertia-like path selection algorithm based on this planning method. We evaluated the proposed method on our designed autonomous driving experimental platform `Pioneer' and participated in the 2018 Intelligent Vehicles Future Challenge. In the competition, the `Pioneer' successfully completed all the races and won the championship without any manual intervention.