ICRA 2011 Paper Abstract


Paper WeA103.5

Wei, Junqing (Carnegie Mellon University), Dolan, John M. (Carnegie Mellon University), Snider, Jarrod (Carnegie Mellon University), Litkouhi, Bakhtiar (GM R&D Center)

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

Scheduled for presentation during the Regular Sessions "Autonomous Navigation III" (WeA103), Wednesday, May 11, 2011, 09:20−09:35, Room 3D

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on July 5, 2020

Keywords Autonomous Navigation, Intelligent Transportation Systems, Motion and Path Planning


In this paper, a point-based Markov Decision Process (QMDP) algorithm is proposed for a robust single-lane autonomous driving behavior control. 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.



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