Search-Based Optimal Motion Planning for Automated Driving

This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of several hundred meters and thus enabling automated driving in urban conditions. This is achieved through several features. Firstly, a convenient geometrical representation of both the search space and driving constraints enables the use of classical path planning approach. Thus, a wide variety of constraints can be tackled simultaneously (other vehicles, traffic lights, etc.). Secondly, an exact cost-to-go map, obtained by solving a relaxed problem, is then used by A*-based algorithm with model predictive flavour in order to compute the optimal motion trajectory. The algorithm takes into account both distance and time horizons. The approach is validated within a simulation study with realistic traffic scenarios. We demonstrate the capability of the algorithm to devise plans both in fast and slow driving conditions, even when full stop is required.

[1]  Barys Shyrokau,et al.  Performance benchmark of state-of-the-art lateral path-following controllers , 2018, 2018 IEEE 15th International Workshop on Advanced Motion Control (AMC).

[2]  Maxim Likhachev,et al.  Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles , 2008, Int. J. Robotics Res..

[3]  Ardalan Vahidi,et al.  Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time , 2011, IEEE Transactions on Control Systems Technology.

[4]  Jonas Sjöberg,et al.  Strategic decision making for automated driving on two-lane, one way roads using model predictive control , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[5]  Michael Stolz,et al.  Energy Efficient Driving in Dynamic Environment: Considering Other Traffic Participants and Overtaking Possibility , 2017 .

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

[7]  Alois Knoll,et al.  Kinodynamic motion planning with Space-Time Exploration Guided Heuristic Search for car-like robots in dynamic environments , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[8]  Bin Ran,et al.  Modeling and Analysis of the Lane-Changing Execution in Longitudinal Direction , 2016, IEEE Transactions on Intelligent Transportation Systems.

[9]  David González,et al.  A Review of Motion Planning Techniques for Automated Vehicles , 2016, IEEE Transactions on Intelligent Transportation Systems.

[10]  Julius Ziegler,et al.  Spatiotemporal state lattices for fast trajectory planning in dynamic on-road driving scenarios , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Julius Ziegler,et al.  Trajectory planning for Bertha — A local, continuous method , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

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

[13]  Luke Fletcher,et al.  A perception-driven autonomous urban vehicle , 2008 .

[14]  Emilio Frazzoli,et al.  A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles , 2016, IEEE Transactions on Intelligent Vehicles.

[15]  Sterling J. Anderson,et al.  An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios , 2010 .

[16]  GianAntonio Magnani,et al.  Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning , 2013, Springer Briefs in Electrical and Computer Engineering.

[17]  Michael Stolz,et al.  Novel model-based heuristics for energy optimal motion planning of an autonomous vehicle using A , 2017, ArXiv.

[18]  Sebastian Thrun,et al.  Junior: The Stanford entry in the Urban Challenge , 2008, J. Field Robotics.

[19]  Thierry Fraichard,et al.  Dynamic trajectory planning with dynamic constraints: A 'state-time space' approach , 1993, Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93).

[20]  Markus Maurer,et al.  Probabilistic online POMDP decision making for lane changes in fully automated driving , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[21]  Bart van Arem,et al.  Policy and society related implications of automated driving: A review of literature and directions for future research , 2017, J. Intell. Transp. Syst..

[22]  Lydia E. Kavraki,et al.  Motion Planning , 2011, Springer Handbook of Robotics, 2nd Ed..

[23]  Sebastian Thrun,et al.  Junior: The Stanford entry in the Urban Challenge , 2008, J. Field Robotics.

[24]  Julius Ziegler,et al.  Optimal trajectory generation for dynamic street scenarios in a Frenét Frame , 2010, 2010 IEEE International Conference on Robotics and Automation.

[25]  Carlos Canudas de Wit,et al.  Eco-driving in urban traffic networks using traffic signal information , 2013, 52nd IEEE Conference on Decision and Control.

[26]  Luke Fletcher,et al.  A perception‐driven autonomous urban vehicle , 2008, J. Field Robotics.

[27]  Matthias Althoff,et al.  Formalising Traffic Rules for Accountability of Autonomous Vehicles , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[28]  D. Dolgov.  Practical Search Techniques in Path Planning for Autonomous Driving , 2008 .

[29]  S. LaValle,et al.  Motion Planning , 2008, Springer Handbook of Robotics.

[30]  Meng Wang,et al.  Game theoretic approach for predictive lane-changing and car-following control , 2015 .

[31]  Florent Altché,et al.  Optimal trajectory planning for autonomous driving integrating logical constraints: An MIQP perspective , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[32]  Michael Stolz,et al.  Energy-Efficient Driving in Dynamic Environment: Globally Optimal MPC-like Motion Planning Framework , 2018 .

[33]  Julius Ziegler,et al.  The combinatorial aspect of motion planning: Maneuver variants in structured environments , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[34]  Stephen Jones,et al.  Traffic light assistant system for optimized energy consumption in an electric vehicle , 2014, 2014 International Conference on Connected Vehicles and Expo (ICCVE).