Cooperative Collision Avoidance for Coalitions of Connected and Autonomous Vehicles
Cooperative Collision Avoidance for Coalitions of Connected and Autonomous Vehicles
- New search for: Patel, Dhruvkumar
- New search for: Bansal, Rishita
- New search for: Zalila-Wenkstern, Rym
- New search for: Patel, Dhruvkumar
- New search for: Bansal, Rishita
- New search for: Zalila-Wenkstern, Rym
In this paper, we discuss collision avoidance for Connected and Autonomous Vehicles (CAVs) on a highway. CAVs are clustered into coalitions each managed by a leader. Within a coalition, collision avoidance is addressed using a Monte Carlo Tree Search (MCTS)-based approach. We propose algorithms for collision avoidance across coalitions. After an initial assessment of the impact of a potential collision on an affected coalition, leaders cooperate to define action plans that are free of intra-coalition and inter-coalition conflicts. The algorithms were validated through extensive realistic simulations in a multi-agent-based traffic simulator. Experimental results discuss the reliability and scalability of the algorithms for coalitions of different sizes. Moreover, we present an analysis to select the optimal coalition size and the optimal number of coalitions given a total number of CAVs.
Document information
Cooperative Collision Avoidance for Coalitions of Connected and Autonomous Vehicles
2022-10-08
1344883 byte
Conference paper
Electronic Resource
English
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