TRAJECTORY PLANNING FOR AUTONOMOUS VEHICLE IN UNCERTAIN ENVIRONMENT USING EVIDENTIAL GRID

Abstract This paper considers the trajectory planning problem for an autonomous vehicle given uncertain knowledge about the surrounding environment. We propose to use evidential occupancy grid to deal with sensor uncertainties. Our aim is to develop a planning approach based on clothoid tentacles allowing a vehicle to move autonomously and safely in an environment which is not perfectly known. First, we generate a set of clothoid tentacles in the egocentered reference frame related to the vehicle, then we evaluate each tentacle using several criteria including occupancy criterion with evidential grid.

[1]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.

[2]  Mathias Perrollaz,et al.  Computing occupancy grids from multiple sensors using linear opinion pools , 2012, 2012 IEEE International Conference on Robotics and Automation.

[3]  Véronique Berge-Cherfaoui,et al.  An evidential sensor model for Velodyne scan grids , 2014, 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV).

[4]  Véronique Berge-Cherfaoui,et al.  A Markov Decision Process-based approach for trajectory planning with clothoid tentacles , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[5]  Victor Aitken,et al.  Evidential mapping for mobile robots with range sensors , 2006, IEEE Transactions on Instrumentation and Measurement.

[6]  Hugh F. Durrant-Whyte,et al.  An evidential approach to map-building for autonomous vehicles , 1998, IEEE Trans. Robotics Autom..

[7]  Christian Laugier,et al.  Bayesian Occupancy Filtering for Multitarget Tracking: An Automotive Application , 2006, Int. J. Robotics Res..

[8]  Franz Kummert,et al.  A Generic Concept of a System for Predicting Driving Behaviors , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[9]  Rüdiger Dillmann,et al.  Probabilistic decision-making under uncertainty for autonomous driving using continuous POMDPs , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[10]  Véronique Berge-Cherfaoui,et al.  Integrating safety distances with trajectory planning by modifying the occupancy grid for autonomous vehicle navigation , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[11]  Panos E. Trahanias,et al.  Real-time hierarchical POMDPs for autonomous robot navigation , 2007, Robotics Auton. Syst..

[12]  Philippe Smets,et al.  Decision making in the TBM: the necessity of the pignistic transformation , 2005, Int. J. Approx. Reason..

[13]  Véronique Berge-Cherfaoui,et al.  Credibilist occupancy grids for vehicle perception in dynamic environments , 2011, 2011 IEEE International Conference on Robotics and Automation.

[14]  Michael Himmelsbach,et al.  Driving with tentacles: Integral structures for sensing and motion , 2008 .

[15]  Weiwen Deng,et al.  Trajectory planning for vehicle autonomous driving with uncertainties , 2014, Proceedings 2014 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS).

[16]  Michael Himmelsbach,et al.  Autonomous Off-Road Navigation for MuCAR-3 , 2011, KI - Künstliche Intelligenz.

[17]  Julien Moras,et al.  Grilles de perception évidentielles pour la navigation robotique en milieu urbain. (Evidential perception grids for robotics navigation in urban environment) , 2013 .

[18]  Rüdiger Dillmann,et al.  Learning Driver Behavior Models from Traffic Observations for Decision Making and Planning , 2015, IEEE Intelligent Transportation Systems Magazine.

[19]  Jonathan P. How,et al.  Threat-aware path planning in uncertain urban environments , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Ali Charara,et al.  Local Trajectory Planning and Tracking For Autonomous Vehicle Navigation Using Clothoid Tentacles Method , 2015 .

[21]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[22]  John M. Dolan,et al.  Motion planning under uncertainty for on-road autonomous driving , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).