Motion Planning for Autonomous vehicles

April, 2013Motion Planning for Multiple Autonomous Vehicles Rahul KalaLiterature ReviewSchool of Systems, Engineering, University of Readingrkala.99k.orgMotion Planning for Multiple Autonomous VehiclesOrganizationrkala.99k.org

Motion Planning for Multiple Autonomous VehiclesTrajectory PlanningCurrent Intelligent Vehicles algorithms cannot be used as:Lane proneSimple obstacle frameworksNon-cooperative

Current Mobile Robotics algorithms cannot be used as:Narrowly bounded roadsRoad structureOvertaking and Vehicle Following behavioursUnknown time of emergencerkala.99k.org

Motion Planning for Multiple Autonomous VehiclesIntelligent Management of the Transportation SystemKey sub-problems:RoutingCongestion Avoidance Start Time Prediction

Key modelling differences from the literatureDiversity: Speed based and task basedrkala.99k.org

Motion Planning for Multiple Autonomous VehiclesIntelligent Vehiclesrkala.99k.org

Key ApproachesRRTStatic obstacle avoidance Delaunay TrianglesStatic obstacle avoidance in a structured environmentElastic BandsStatic obstacle avoidance, following a vehicleCooperative overtakingOptimization based overtaking modelLane change decision makingDecide the lane of travelOvertaking trajectoryLane change trajectories to overtakeOvertaking decision makingWhether to overtake or not, probabilistic decision makingMotion Planning for Multiple Autonomous VehiclesOptimization basedVariationsCentralizedDecentralized Cooperative Co-evolutionrkala.99k.org

MethodsGenetic Algorithm, Swarm Algorithm and VariantsOptimizing trajectoryMulti-ResolutionCoarser optimization at the start and finer at the endPre-computationDatabase of common situation-based trajectoriesMotion Planning for Multiple Autonomous VehiclesRRT and Relatedrkala.99k.org

MethodsMultiple instance basedRun multiple times and combine the results, attempt to get global optimalityGeneralized samplingRRT expansion using vehicles control modelHeuristics in RRT generationGuide RRT expansion through/towards the best areas or goalRetraction based RRTSolution to the narrow corridor problemMotion Planning for Multiple Autonomous VehiclesGraph Search, Roadmap and Hierarchicalrkala.99k.org

MethodMulti-Layer PlanningMap represented in multiple granularities which the algorithm operates2-Layer PlanningOne algorithm for coarser level, whose output calls another algorithm for finer levelDistributed roadmap building

Multiple agents at different locations build partial maps which are integratedAdaptive roadmapsSampled roadmap adapts to the change in environmentMotion Planning for Multiple Autonomous VehiclesReactive MethodsDistance maximization basedLogic set basedVelocity ObstaclesPotential MethodsFuzzy based

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Motion Planning for Multiple Autonomous VehiclesRouting and Congestion Avoidancerkala.99k.org

MethodsAnticipatory SystemsCongestion is anticipated and preventive measures are takenDigital PheromonePheromone left at roads while the vehicle moves, indicates the number of vehicles and hence the congestionReservationReserve a road, lane, intersectionHierarchical PlanningRoad network map seen as multiple connected communities/sub-areasMotion Planning for Multiple Autonomous VehiclesStart Time Predictionrkala.99k.org

MethodsMarkovian ProcessRoad network map modelled as a markovian process and searchedTravel Time PredictionExtrapolate recorded data to get future snapshotStochastic Graph SearchProbabilistic search across all possible routesMotion Planning for Multiple Autonomous Vehiclesrkala.99k.org

Thank YouAcknowledgements:Commonwealth Scholarship Commission in the United Kingdom British CouncilMotion Planning for Multiple Autonomous Vehicles