Motion planning under uncertainty for on-road autonomous driving

We present a motion planning framework for autonomous on-road driving considering both the uncertainty caused by an autonomous vehicle and other traffic participants. The future motion of traffic participants is predicted using a local planner, and the uncertainty along the predicted trajectory is computed based on Gaussian propagation. For the autonomous vehicle, the uncertainty from localization and control is estimated based on a Linear-Quadratic Gaussian (LQG) framework. Compared with other safety assessment methods, our framework allows the planner to avoid unsafe situations more efficiently, thanks to the direct uncertainty information feedback to the planner. We also demonstrate our planner's ability to generate safer trajectories compared to planning only with a LQG framework.

I. Introduction

Autonomous vehicle technology has made considerable progress over the past few years [1], [2], [3]. Its main purpose is to improve driving safety by decreasing traffic accidents, which are mostly caused by human mistakes or distractions. However, autonomous vehicle technology also poses new challenges. One important problem is how to plan a safe trajectory for a vehicle, given uncertain knowledge about the surrounding environment and the vehicle's own state.