Safe Path Planning with Gaussian Process Regulated Risk Map

Government data identifies driver behaviour errors as a factor in 94% of car crashes, and autonomous vehicles (AVs), which avoids risky driver behaviours completely, are expected to reduce the number of road crashes significantly. Thus, one of the central focuses of developing AVs is to ensure safety during navigation. However, in reality, AV safety has been far below its expectation, and so far, no government has allowed for complete autonomous driving without human supervision. This paper proposes a dynamic safe path planning algorithm for AVs with Gaussian process regulated risk map. By reasonably assuming that the output of the object detection and tracking module follows a multi-variate Gaussian distribution, we put forward a safe path planning paradigm with Gaussian process regulated risk map, ensuring safety with high confidence. Both simulation results and in-vehicle tests demonstrate the effectiveness of the proposed algorithm.

[1]  Sue Whitesides,et al.  Risk Averse Motion Planning for a Mobile Robot , 2011 .

[2]  Mahmood Naderan-Tahan,et al.  Efficient and safe path planning for a Mobile Robot using genetic algorithm , 2009, 2009 IEEE Congress on Evolutionary Computation.

[3]  Paul Weng,et al.  Finding Risk-Averse Shortest Path with Time-dependent Stochastic Costs , 2016, MIWAI.

[4]  Ouahiba Azouaoui,et al.  Real-time Safe Path Planning for Robot Navigation in Unknown Dynamic Environments , 2016 .

[5]  Javier Alonso-Mora,et al.  Trajectory optimization for autonomous overtaking with visibility maximization , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[6]  Yisong Yue,et al.  Safe Exploration and Optimization of Constrained MDPs Using Gaussian Processes , 2018, AAAI.

[7]  Franck Guillemard,et al.  TRAJECTORY PLANNING FOR AUTONOMOUS VEHICLE IN UNCERTAIN ENVIRONMENT USING EVIDENTIAL GRID , 2017 .

[8]  Wilhelm Dangelmaier,et al.  Risk Averse Shortest Path Planning in Uncertain Domains , 2006, 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06).

[9]  Santokh Singh,et al.  Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey , 2015 .

[10]  Gregory Dudek,et al.  Efficient Terrain Driven Coral Coverage Using Gaussian Processes for Mosaic Synthesis , 2016, 2016 13th Conference on Computer and Robot Vision (CRV).

[11]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[12]  S. LaValle.  Rapidly-exploring random trees : a new tool for path planning , 1998 .

[13]  Gregory Dudek,et al.  Multi-robot exploration and rendezvous on graphs , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Emilio Frazzoli,et al.  A Spatial-Temporal Approach for Moving Object Recognition with 2D LIDAR , 2014, ISER.

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

[16]  Marcelo H. Ang,et al.  Context-Aware Intention and Trajectory Prediction for Urban Driving Environment , 2018, ISER.

[17]  Andreas Krause,et al.  Safe Exploration in Finite Markov Decision Processes with Gaussian Processes , 2016, NIPS.

[18]  Christian S. Jensen,et al.  Multi-Cost Optimal Route Planning Under Time-Varying Uncertainty , 2013 .

[19]  Daniela Rus,et al.  Context and Intention Aware Planning for Urban Driving , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[20]  Chang Liu,et al.  Path planning for autonomous vehicles using model predictive control , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[21]  Bo Li,et al.  SECOND: Sparsely Embedded Convolutional Detection , 2018, Sensors.

[22]  Wolfram Burgard,et al.  Robust Monte Carlo localization for mobile robots , 2001, Artif. Intell..

[23]  Chen Zhang,et al.  Vehicle Detection, Tracking and Behavior Analysis in Urban Driving Environments Using Road Context , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[24]  Andreas Krause,et al.  Safe Model-based Reinforcement Learning with Stability Guarantees , 2017, NIPS.

[25]  Daniela Rus,et al.  Navigating Congested Environments with Risk Level Sets , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

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