Learning Cooperative Lane Selection Strategies for Highways

David E. Moriarty, Pat Langley

This paper presents a novel approach to traffic management by coordinating driver behaviors. Current traffic management systems do not consider lane organization of the cars and only affect traffic flows by controlling traffic signals or ramp meters. However, drivers should be able to increase traffic throughput and more consistently maintain desired speeds by selecting lanes intelligently. We pose the problem of intelligent lane selection as a challenging and potentially rewarding problem for artificial intelligence, and we propose a methodology that uses supervised and reinforcement learning to form distributed control strategies. Initial results are promising and demonstrate that intelligent lane selection can better approximate desired speeds and reduce the total number of lane changes.


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