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Adaptive and Collaborative Agent-based Traffic Regulation Using Behavior Trees

Abstract : In this paper, we propose a self-adaptive approach to build a smart traffic light management dealing with intersections. This approach relies on the multiagent systems architecture, suitable to support a distributed and collaborative mechanism of regulation while taking into account dynamic changes in the traffic flow. In our solution, the agents model the intersections and can decide how long is the duration of traffic lights according to their perception of the traffic flow. Each intersection agent uses a behavior tree to update the traffic light status (i.e. switch from green to red lights and vice-versa), changing the duration of each status dynamically, according to the number of cars perceived in each intersection. We also demonstrate how dynamic traffic control policies can be used in a collaborative scenario to regulate traffic flow.
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Submitted on : Wednesday, July 8, 2020 - 11:14:45 AM
Last modification on : Tuesday, March 23, 2021 - 9:28:02 AM
Long-term archiving on: : Monday, November 30, 2020 - 2:07:02 PM


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Arthur Casals, Assia Belbachir, Amal El Fallah-Seghrouchni. Adaptive and Collaborative Agent-based Traffic Regulation Using Behavior Trees. AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, May 2020, Auckland, New Zealand. pp.1789-1791, ⟨10.5555/3398761.3398983⟩. ⟨hal-02893353⟩



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