Design Space of Behaviour Planning for Autonomous Driving

We explore the complex design space of behaviour planning for autonomous driving. Design choices that successfully address one aspect of behaviour planning can critically constrain others. To aid the design process, in this work we decompose the design space with respect to important choices arising from the current state of the art approaches, and describe the resulting trade-offs. In doing this, we also identify interesting directions of future work.

[1]  Ron Alterovitz,et al.  Motion Planning Under Uncertainty Using Differential Dynamic Programming in Belief Space , 2011, ISRR.

[2]  Calin Belta,et al.  Automatic deployment of autonomous cars in a Robotic Urban-Like Environment (RULE) , 2009, 2009 IEEE International Conference on Robotics and Automation.

[3]  Krzysztof Czarnecki,et al.  Trajectory prediction of traffic agents at urban intersections through learned interactions , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[4]  Zachary Chase Lipton,et al.  Combating Deep Reinforcement Learning's Sisyphean Curse with Intrinsic Fear , 2016, 1611.01211.

[5]  Joshué Pérez,et al.  Fuzzy logic steering control of autonomous vehicles inside roundabouts , 2015, Appl. Soft Comput..

[6]  Michael Felsberg,et al.  Visual autonomous road following by symbiotic online learning , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

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

[8]  Raúl Quintero,et al.  Pedestrian path prediction using body language traits , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[9]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[10]  Julius Ziegler,et al.  Lanelets: Efficient map representation for autonomous driving , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[11]  Marcelo H. Ang,et al.  Situation-aware decision making for autonomous driving on urban road using online POMDP , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[12]  Lingli Yu,et al.  Autonomous overtaking decision making of driverless bus based on deep Q-learning method , 2017, 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[13]  Thomas P. Moran,et al.  Questions, Options, and Criteria: Elements of Design Space Analysis , 1991, Hum. Comput. Interact..

[14]  Sebastian Thrun,et al.  Apprenticeship learning for motion planning with application to parking lot navigation , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  S. Balakirsky,et al.  Implementing a Rule-based System to Represent Decision Criteria for On-Road Autonomous Navigation , 2004 .

[16]  E. D. Dickmanns,et al.  A system architecture for autonomous visual road vehicle guidance , 1997, Proceedings of Conference on Intelligent Transportation Systems.

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

[18]  Ufuk Topcu,et al.  Environment-Independent Task Specifications via GLTL , 2017, ArXiv.

[19]  Johann Marius Zöllner,et al.  Learning how to drive in a real world simulation with deep Q-Networks , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[20]  Bernhard Schölkopf,et al.  Probabilistic movement modeling for intention inference in human–robot interaction , 2013, Int. J. Robotics Res..

[21]  Zachary Chase Lipton.  The mythos of model interpretability , 2016, ACM Queue.

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

[23]  Matthias Schreier,et al.  Environment representations for automated on-road vehicles , 2018, Autom..

[24]  Ana L. C. Bazzan,et al.  Individual versus Difference Rewards on Reinforcement Learning for Route Choice , 2014, 2014 Brazilian Conference on Intelligent Systems.

[25]  Xin Zhang,et al.  End to End Learning for Self-Driving Cars , 2016, ArXiv.

[26]  S. Shankar Sastry,et al.  A learning based approach to control synthesis of Markov decision processes for linear temporal logic specifications , 2014, 53rd IEEE Conference on Decision and Control.

[27]  Klaus C. J. Dietmayer,et al.  Stationary Detection of the Pedestrian?s Intention at Intersections , 2013, IEEE Intelligent Transportation Systems Magazine.

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

[29]  Amnon Shashua,et al.  Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving , 2016, ArXiv.

[30]  William Whittaker,et al.  Tartan Racing: A multi-modal approach to the DARPA Urban Challenge , 2007 .

[31]  Mayank Bansal,et al.  ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst , 2018, Robotics: Science and Systems.

[32]  Jürgen Schmidhuber,et al.  World Models , 2018, ArXiv.

[33]  Wei Zhan,et al.  A non-conservatively defensive strategy for urban autonomous driving , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[34]  Sergey Levine,et al.  Uncertainty-Aware Reinforcement Learning for Collision Avoidance , 2017, ArXiv.

[35]  Marco Pavone,et al.  Learning Sampling Distributions for Robot Motion Planning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[36]  C. G. Keller,et al.  Will the Pedestrian Cross? A Study on Pedestrian Path Prediction , 2014, IEEE Transactions on Intelligent Transportation Systems.

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

[38]  Fang Zhang,et al.  Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Driving , 2016, ArXiv.

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

[40]  Razvan Pascanu,et al.  Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.

[41]  Gregory D. Hager,et al.  Combining neural networks and tree search for task and motion planning in challenging environments , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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

[43]  David Silver,et al.  Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain , 2010, Int. J. Robotics Res..

[44]  Jürgen Schmidhuber,et al.  Evolving large-scale neural networks for vision-based reinforcement learning , 2013, GECCO '13.

[45]  Christoph Stiller,et al.  Automated Driving in Uncertain Environments: Planning With Interaction and Uncertain Maneuver Prediction , 2018, IEEE Transactions on Intelligent Vehicles.

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

[47]  Klaus C. J. Dietmayer,et al.  Adaptive learning based on guided exploration for decision making at roundabouts , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[48]  Michael Felsberg,et al.  Biologically Inspired Online Learning of Visual Autonomous Driving , 2014, BMVC.

[49]  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).

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

[51]  Etienne Perot,et al.  Deep Reinforcement Learning framework for Autonomous Driving , 2017, Autonomous Vehicles and Machines.

[52]  David Hsu,et al.  Intention-aware online POMDP planning for autonomous driving in a crowd , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[53]  Nico Kaempchen,et al.  Highly Automated Driving on Freeways in Real Traffic Using a Probabilistic Framework , 2012, IEEE Transactions on Intelligent Transportation Systems.

[54]  Geoffrey J. Gordon,et al.  A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning , 2010, AISTATS.

[55]  Alexey Dosovitskiy,et al.  End-to-End Driving Via Conditional Imitation Learning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[56]  Peter King,et al.  Odin: Team VictorTango's entry in the DARPA Urban Challenge , 2008, J. Field Robotics.

[57]  Yuval Tassa,et al.  Continuous control with deep reinforcement learning , 2015, ICLR.

[58]  Joseph Gonzalez,et al.  Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning , 2017, ArXiv.

[59]  Shlomo Zilberstein,et al.  Online Decision-Making for Scalable Autonomous Systems , 2017, IJCAI.

[60]  Kyo Chul Kang,et al.  Feature-Oriented Domain Analysis (FODA) Feasibility Study , 1990 .

[61]  U. Ozguner,et al.  Human Driver Model and Driver Decision Making for Intersection Driving , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[62]  John M. Dolan,et al.  Learning On-Road Visual Control for Self-Driving Vehicles With Auxiliary Tasks , 2018, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

[63]  Jianfeng Gao,et al.  Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear , 2016, ArXiv.

[64]  Dean Pomerleau,et al.  ALVINN, an autonomous land vehicle in a neural network , 2015 .

[65]  Yann LeCun,et al.  Off-Road Obstacle Avoidance through End-to-End Learning , 2005, NIPS.

[66]  Christian Laugier,et al.  Interaction-aware driver maneuver inference in highways using realistic driver models , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[67]  Ashutosh Saxena,et al.  High speed obstacle avoidance using monocular vision and reinforcement learning , 2005, ICML.

[68]  Wei Zhan,et al.  A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning , 2017, Volume 3: Modeling and Validation; Multi-Agent and Networked Systems; Path Planning and Motion Control; Tracking Control Systems; Unmanned Aerial Vehicles (UAVs) and Application; Unmanned Ground and Aerial Vehicles; Vibration in Mechanical Systems; Vibrat.

[69]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.