Autonomous vehicle planning system design under perception limitation in pedestrian environment

Autonomous driving within the pedestrian environment is always challenging, as the perception ability is limited by the crowdedness and the planning process is constrained by the complicated human behaviors. In this paper, we present a vehicle planning system for self-driving with limited perception in the pedestrian environment. Acknowledging the difficulty of obstacle detection and tracking within the crowded pedestrian environment, only the raw LIDAR sensing data is employed for the purpose of traversability analysis and vehicle planning. The designed vehicle planning system has been experimentally validated to be robust and safe within the populated pedestrian environment.

[1]  Emilio Frazzoli,et al.  Synthetic 2D LIDAR for precise vehicle localization in 3D urban environment , 2013, 2013 IEEE International Conference on Robotics and Automation.

[2]  Emilio Frazzoli,et al.  Sampling-based algorithms for optimal motion planning , 2011, Int. J. Robotics Res..

[3]  R. Bishop,et al.  A survey of intelligent vehicle applications worldwide , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[4]  Torsten Kröger,et al.  Opening the door to new sensor-based robot applications—The Reflexxes Motion Libraries , 2011, 2011 IEEE International Conference on Robotics and Automation.

[5]  Marcelo H. Ang,et al.  Vehicle motion intention reasoning using cooperative perception on urban road , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[6]  David Hsu,et al.  Integrated perception and planning in the continuous space: A POMDP approach , 2013, Int. J. Robotics Res..

[7]  Sebastian Thrun,et al.  Stanley: The robot that won the DARPA Grand Challenge , 2006, J. Field Robotics.

[8]  Thierry Fraichard,et al.  Partial motion planning framework for reactive planning within dynamic environments , 2005, ICINCO.

[9]  Jaime Valls Miró,et al.  POMDP-based long-term user intention prediction for wheelchair navigation , 2008, 2008 IEEE International Conference on Robotics and Automation.

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

[11]  Richard Bishop.  Intelligent Vehicle Applications Worldwide , 2000, IEEE Intell. Syst..

[12]  Andreas Krause,et al.  Unfreezing the robot: Navigation in dense, interacting crowds , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Bernt Schiele,et al.  Pedestrian detection in crowded scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  Emilio Frazzoli,et al.  Intention-Aware Motion Planning , 2013, WAFR.

[15]  R. C. Coulter,et al.  Implementation of the Pure Pursuit Path Tracking Algorithm , 1992 .

[16]  Marcelo H. Ang,et al.  Motion planning using cooperative perception on urban road , 2013, 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM).

[17]  Alberto Broggi,et al.  Development of the control system for the Vislab Intercontinental Autonomous Challenge , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[18]  Sanjiv Singh,et al.  The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, George Air Force Base, Victorville, California, USA , 2009, The DARPA Urban Challenge.