Towards Predictive Driving through Blind Intersections

This paper presents an approach for predictive driving when facing blind intersections based on expert data. Expert drivers anticipate and avoid potential dangerous situations. In most cases these complex behaviors cannot be reproduced by state of the art planning approaches. We present an analysis of expert drivers while passing through blind intersections, we extract useful driving features to model the intersection and propose a cost function based on those features. We use inverse reinforcement learning to extract a feature-based cost function and learn its parameters from driver data. Finally, feature weights are computed based on collected expert driving data (using 211 trajectories). Evaluation was performed in terms of trajectory and speed using modified Hausdorff distance. Experimental results show that the planner is capable of computing trajectories comparable to those ones of the expert drivers when facing blind intersections.

[1]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[2]  Anil K. Jain,et al.  A modified Hausdorff distance for object matching , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[3]  Maxim Likhachev,et al.  D*lite , 2002, AAAI/IAAI.

[4]  Katrin Strandemar,et al.  On Objective Measures for Ride Comfort Evaluation , 2005 .

[5]  Anind K. Dey,et al.  Maximum Entropy Inverse Reinforcement Learning , 2008, AAAI.

[6]  Benjamin Kuipers,et al.  High performance control for graceful motion of an intelligent wheelchair , 2008, 2008 IEEE International Conference on Robotics and Automation.

[7]  Siddhartha S. Srinivasa,et al.  Planning-based prediction for pedestrians , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Jonathan P. How,et al.  Threat assessment design for driver assistance system at intersections , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[9]  Helge J. Ritter,et al.  Recognition of situation classes at road intersections , 2010, 2010 IEEE International Conference on Robotics and Automation.

[10]  J. Lenk,et al.  Modeling Lateral and Longitudinal Control of Human Drivers with Multiple Linear Regression Models , 2011 .

[11]  Franz Kummert,et al.  Behavior prediction at multiple time-scales in inner-city scenarios , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[12]  Juan Liu,et al.  An Efficient Computational Architecture for a Collision Early-Warning System for Vehicles, Pedestrians, and Bicyclists , 2011, IEEE Transactions on Intelligent Transportation Systems.

[13]  Martial Hebert,et al.  Activity Forecasting , 2012, ECCV.

[14]  Julius Ziegler,et al.  Optimal trajectories for time-critical street scenarios using discretized terminal manifolds , 2012, Int. J. Robotics Res..

[15]  Dizan Vasquez,et al.  A survey on motion prediction and risk assessment for intelligent vehicles , 2014, ROBOMECH Journal.

[16]  Horst-Michael Groß,et al.  Combining behavior and situation information for reliably estimating multiple intentions , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[17]  Norihiro Hagita,et al.  Visibility analysis for autonomous vehicle comfortable navigation , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[18]  Wolfram Burgard,et al.  Learning driving styles for autonomous vehicles from demonstration , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[19]  A. Morton.  Shared Agency: A Planning Theory of Acting Together , 2015 .

[20]  Atsushi Watanabe,et al.  Including human factors for planning comfortable paths , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[21]  Edwin Olson,et al.  Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: Theory and experiment , 2015, Autonomous Robots.

[22]  Edwin Olson,et al.  MPDM: Multipolicy decision-making in dynamic, uncertain environments for autonomous driving , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[23]  Hirokatsu Kataoka,et al.  Predicting driving behavior using inverse reinforcement learning with multiple reward functions towards environmental diversity , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[24]  Morales Saiki Luis Yoichi,et al.  Comfortable Autonomous Navigation Based on Collision Prediction in Blind Occluded Regions. , 2015 .

[25]  Wolfram Burgard,et al.  Inverse reinforcement learning of behavioral models for online-adapting navigation strategies , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[26]  Francesco Borrelli,et al.  A Learning-Based Framework for Velocity Control in Autonomous Driving , 2016, IEEE Transactions on Automation Science and Engineering.

[27]  Wolfram Burgard,et al.  Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics , 2016, AISTATS.

[28]  Eijiro Takeuchi,et al.  Accurate Analysis of Expert and Elderly Driving at Blind Corners for Proactive Advanced Driving Assistance Systems , 2016 .

[29]  Pongsathorn Raksincharoensak,et al.  Longitudinal and lateral motion planning method for avoidance of multi-obstacles in urban environments based on inverse collision probability , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[30]  Hannes Sommer,et al.  Predicting actions to act predictably: Cooperative partial motion planning with maximum entropy models , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[31]  Masamichi Shimosaka,et al.  Fast Inverse Reinforcement Learning with Interval Consistent Graph for Driving Behavior Prediction , 2017, AAAI.

[32]  Eijiro Takeuchi,et al.  Proactive driving modeling in blind intersections based on expert driver data , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[33]  Eijiro Takeuchi,et al.  Autonomous predictive driving for blind intersections , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[34]  Christoph Stiller,et al.  Decision making for autonomous driving considering interaction and uncertain prediction of surrounding vehicles , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[35]  Eijiro Takeuchi,et al.  Robust localization using 3D NDT scan matching with experimentally determined uncertainty and road marker matching , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).