A Point-based MDP for Robust Single-Lane Autonomous Driving Behavior under Uncertainties

摘要：

In this paper, a point-based Markov Decision Process (QMDP) algorithm is used for robust single-lane autonomous driving behavior control under uncertainties. Autonomous vehicle decision making is modeled as a Markov Decision Process (MDP), then extended to a QMDP framework. Based on MDP/QMDP, three kinds of uncertainties are taken into account: sensor noise, perception constraints and surrounding vehicles’ behavior. In simulation, the QMDP-based reasoning framework makes the autonomous vehicle perform with differing levels of conservativeness corresponding to different perception confidence levels. Road tests also indicate that the proposed algorithm helps the vehicle in avoiding potentially unsafe situations under these uncertainties. In general, the results indicate that the proposed QMDP-based algorithm makes autonomous driving more robust to limited sensing ability and occasional sensor failures.

作者: Junqing Wei John M. Dolan Jarrod M. Snider Bakhtiar Litkouhi

作者单位: Department of Electrical and Computer Engineering,Carnegie Mellon University,Pittsburgh,PA 15213 USA The Robotics Institute and Department of Electrical and Computer Engineering,Carnegie Mellon Univers GM-CMU Autonomous Driving Collaborative Research Lab,GM R&D Center,Warren,MI 48090 USA

会议类型: 国际会议

会议名称: 2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

会议地点: 上海

会议语种:英文

页码: 2586-2592

在线出版日期: 2011-05-09（万方平台首次上网日期，不代表论文的发表时间）