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.

[1]  Sebastian Thrun,et al.  Towards fully autonomous driving: Systems and algorithms , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[2]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[3]  Hongbin Zha,et al.  A real-time motion planner with trajectory optimization for autonomous vehicles , 2012, 2012 IEEE International Conference on Robotics and Automation.

[4]  Matthias Althoff,et al.  Model-Based Probabilistic Collision Detection in Autonomous Driving , 2009, IEEE Transactions on Intelligent Transportation Systems.

[5]  Jin-Woo Lee,et al.  Motion planning for autonomous driving with a conformal spatiotemporal lattice , 2011, 2011 IEEE International Conference on Robotics and Automation.

[6]  John M. Dolan,et al.  A behavioral planning framework for autonomous driving , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[7]  T. Kanade,et al.  Monte Carlo road safety reasoning , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[8]  Matthias Althoff,et al.  Set-based computation of vehicle behaviors for the online verification of autonomous vehicles , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[9]  Pieter Abbeel,et al.  LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information , 2010, Int. J. Robotics Res..

[10]  Matthias Althoff,et al.  Comparison of Markov Chain Abstraction and Monte Carlo Simulation for the Safety Assessment of Autonomous Cars , 2011, IEEE Transactions on Intelligent Transportation Systems.

[11]  Ragunathan Rajkumar,et al.  Towards a viable autonomous driving research platform , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[12]  John M. Dolan,et al.  A point-based MDP for robust single-lane autonomous driving behavior under uncertainties , 2011, 2011 IEEE International Conference on Robotics and Automation.

[13]  M. Althoff,et al.  Safety Assessment of Autonomous Cars using Verification Techniques , 2007, 2007 American Control Conference.

[14]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008, J. Field Robotics.

[15]  John M. Dolan,et al.  On-Road Motion Planning for Autonomous Vehicles , 2012, ICIRA.

[16]  Nicholas Roy,et al.  Rapidly-exploring Random Belief Trees for motion planning under uncertainty , 2011, 2011 IEEE International Conference on Robotics and Automation.