Unsignalized intersection cooperation has been be proved to be able to eliminate green time loss of signalized intersections and improve traffic efficiency. Most of the existing research about unsignalized intersection cooperation considers fixed lane turning, where only specific turning behavior of vehicles is allowed on each lane. Given that traffic volume abd the proportion of vehicles with different turning expectation may change with time, fixed lane turning may lead to inefficiency at intersetions. This paper proposes a multi-lane unsignalized intersection cooperation method that considers variable lane turning. The two-dimensional distribution of vehicles is calculated and vehicles that are not in conflict are scheduled to pass the intersection simultaneously. The formation reconfiguration method is utilized to achieve collision-free longitudinal and lateral position adjustment of vehicles. Simulations are conducted at different input traffic volumes and turning proportion of in-coming vehicles, and the results indicate that our method outperformances the fixed-lane-turning unsignalized cooperation method and the signalized method.
For further details, please refer to:
[1] Cai M, Xu Q, Chen C, et al. Formation Control for Multiple Connected and Automated Vehicles on Multi-lane Roads[J]. arXiv e-prints, 2021: arXiv: 2103.10287v1. (https://arxiv.org/abs/2103.10287)
[2] Cai M, Xu Q, Chen C, et al. Formation Control for Connected and Automated Vehicles on Multi-lane Roads: Relative Motion Planning and Conflict Resolution[J]. arXiv preprint arXiv:2103.10287v3, 2021. (https://arxiv.org/abs/2103.10287)
[3] Cai M, Chen C, Wang J, et al. Formation Control with Lane Preference for Connected and Automated Vehicles in Multi-lane Scenarios[J]. arXiv preprint arXiv:2106.11763, 2021. (https://arxiv.org/abs/2106.11763)
[4] Cai M, Xu Q, Chen C, et al. Multi-lane Unsignalized Intersection Cooperation with Flexible Lane Direction based on Multi-vehicle Formation Control[J]. arXiv preprint arXiv:2108.11112, 2021. (https://arxiv.org/abs/2108.11112)
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This work was supported by the National Key Research and Development Program of China under Grant 2018YFE0204302, the National Natural Science Foundation of China under Grant 52072212, and Intel Collaborative Research Institute Intelligent and Automated Connected Vehicles.