School of Systems, Engineering, University of Reading rkala.99k.org April, 2013 Motion Planning for Multiple Autonomous Vehicles Rahul Kala Literature Review

Motion Planning for Multiple Autonomous Vehicles Organization rkala.99k.org Literature Review Intelligent Vehicles Mobile Robotics Optimization – based RRT and Related Graph Search, Roadmap, Hierarchical Reactive Intelligent Transportation Systems Routing and Congestion Avoidance Start Time Prediction

Motion Planning for Multiple Autonomous Vehicles Trajectory Planning Current Intelligent Vehicles algorithms cannot be used as: Lane prone Simple obstacle frameworks Non-cooperative Current Mobile Robotics algorithms cannot be used as: Narrowly bounded roads Road structure Overtaking and Vehicle Following behaviours Unknown time of emergence rkala.99k.org

Motion Planning for Multiple Autonomous Vehicles Intelligent Management of the Transportation System Key sub-problems: Routing Congestion Avoidance Start Time Prediction Key modelling differences from the literature Diversity: Speed based and task based rkala.99k.org

Motion Planning for Multiple Autonomous Vehicles Intelligent Vehicles rkala.99k.org Key Approaches RRT Static obstacle avoidance Delaunay Triangles Static obstacle avoidance in a structured environment Elastic Bands Static obstacle avoidance, following a vehicle Cooperative overtaking Optimization based overtaking model Lane change decision making Decide the lane of travel Overtaking trajectory Lane change trajectories to overtake Overtaking decision making Whether to overtake or not, probabilistic decision making

Motion Planning for Multiple Autonomous Vehicles Optimization based Variations Centralized Decentralized Cooperative Co-evolution rkala.99k.org Methods Genetic Algorithm, Swarm Algorithm and Variants Optimizing trajectory Multi-Resolution Coarser optimization at the start and finer at the end Pre-computation Database of common situation- based trajectories

Motion Planning for Multiple Autonomous Vehicles RRT and Related rkala.99k.org Methods Multiple instance based Run multiple times and combine the results, attempt to get global optimality Generalized sampling RRT expansion using vehicle’s control model Heuristics in RRT generation Guide RRT expansion through/towards the best areas or goal Retraction based RRT Solution to the narrow corridor problem

Motion Planning for Multiple Autonomous Vehicles Graph Search, Roadmap and Hierarchical rkala.99k.org Method Multi-Layer Planning Map represented in multiple granularities which the algorithm operates 2-Layer Planning One algorithm for coarser level, whose output calls another algorithm for finer level Distributed roadmap building Multiple agents at different locations build partial maps which are integrated Adaptive roadmaps Sampled roadmap adapts to the change in environment

Motion Planning for Multiple Autonomous Vehicles Reactive Methods Distance maximization based Logic set based Velocity Obstacles Potential Methods Fuzzy based rkala.99k.org

Motion Planning for Multiple Autonomous Vehicles Routing and Congestion Avoidance rkala.99k.org Methods Anticipatory Systems Congestion is anticipated and preventive measures are taken Digital Pheromone Pheromone left at roads while the vehicle moves, indicates the number of vehicles and hence the congestion Reservation Reserve a road, lane, intersection Hierarchical Planning Road network map seen as multiple connected communities/sub-areas

Motion Planning for Multiple Autonomous Vehicles Start Time Prediction rkala.99k.org Methods Markovian Process Road network map modelled as a markovian process and searched Travel Time Prediction Extrapolate recorded data to get future snapshot Stochastic Graph Search Probabilistic search across all possible routes

Motion Planning for Multiple Autonomous Vehiclesrkala.99k.org Thank You Acknowledgements: Commonwealth Scholarship Commission in the United Kingdom British Council