Trajectory Planning for Autonomous Highway Driving Using the Adaptive Potential Field

In this paper, we propose a novel autonomous highway driving framework using optimal trajectory generation with an adaptive potential field model. While the existing potential field models for trajectory generation are hard to consider dynamic aspects of obstacles, the proposed potential field model overcomes such limitation by changing the risk size of the potential field. To express the risk function of moving object, the constant time gap policy is utilized. By combining the adaptive potential field and optimal trajectory generation scheme, the proposed driving framework successfully makes autonomous vehicles perform a variety of highway driving functionalities such as lane keeping, lane changing, distance keeping and collision avoidance in critical situations.