Heuristic based evolution for the coordination of autonomous vehicles in the absence of speed lanes |
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Affiliation: | 1. Institute for Future Energy Consumer Needs and Behavior (FCN), School of Business and Economics / E.ON Energy Research Center, RWTH Aachen University, Mathieustraße 10, Aachen, 52074 Germany;2. Department of Business Economics / Marketing Section, Erasmus School of Economics, Erasmus University Rotterdam, P.O. Box 1738, Rotterdam, 3000 DR The Netherlands;3. Department of Economics, Maastricht University, P.O. Box 616, Maastricht, 6200 MD The Netherlands |
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Abstract: | The current state of the art in the planning and coordination of autonomous vehicles is based upon the presence of speed lanes. In a traffic scenario where there is a large diversity between vehicles the removal of speed lanes can generate a significantly higher traffic bandwidth. Vehicle navigation in such unorganized traffic is considered. An evolutionary based trajectory planning technique has the advantages of making driving efficient and safe, however it also has to surpass the hurdle of computational cost. In this paper, we propose a real time genetic algorithm with Bezier curves for trajectory planning. The main contribution is the integration of vehicle following and overtaking behaviour for general traffic as heuristics for the coordination between vehicles. The resultant coordination strategy is fast and near-optimal. As the vehicles move, uncertainties may arise which are constantly adapted to, and may even lead to either the cancellation of an overtaking procedure or the initiation of one. Higher level planning is performed by Dijkstra's algorithm which indicates the route to be followed by the vehicle in a road network. Re-planning is carried out when a road blockage or obstacle is detected. Experimental results confirm the success of the algorithm subject to optimal high and low-level planning, re-planning and overtaking. |
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Keywords: | Unmanned ground vehicles Autonomous vehicles Traffic simulation Multi-robot systems Multi-robot coordination Motion planning |
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