School of Systems, Engineering, University of Reading

rkala.99k.orgApril, 2013

Motion Planning for Multiple Autonomous Vehicles

Rahul Kala

Literature Review

Motion Planning for Multiple Autonomous Vehicles

Organization

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Literature Review

Intelligent Vehicles

Mobile Robotics

Optimization –based

RRT and Related

Graph Search,

Roadmap, Hierarchical

Reactive

Intelligent Transportatio

n Systems

Routing and Congestion Avoidance

Start Time Prediction

Motion Planning for Multiple Autonomous Vehicles

Trajectory PlanningCurrent 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 SystemKey sub-problems:• Routing• Congestion Avoidance • Start Time Prediction

Key modelling differences from the literature• Diversity: Speed based and task based

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Motion Planning for Multiple Autonomous Vehicles

Intelligent Vehicles

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Key ApproachesRRT Static obstacle avoidance Delaunay Triangles Static obstacle avoidance in

a structured environmentElastic Bands Static obstacle avoidance,

following a vehicleCooperative overtaking Optimization based

overtaking modelLane 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

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MethodsGenetic 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

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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

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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

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Motion Planning for Multiple Autonomous Vehicles

Routing and Congestion Avoidance

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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

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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 Vehicles rkala.99k.org

Thank You

• Acknowledgements:• Commonwealth Scholarship Commission in the United Kingdom • British Council