As a strategy to reduce travel delay and enhance energy efficiency,
platooning of connected and autonomous vehicles (CAVs) at non-signalized
intersections has become increasingly popular in academia. However, few studies
have attempted to model the relation between the optimal platoon size and the
traffic conditions around the intersection. To this end, this study proposes an
adaptive platoon based autonomous intersection control model powered by deep
reinforcement learning (DRL) technique. The model framework has following two
levels: the first level adopts a First Come First Serve (FCFS) reservation
based policy integrated with a nonconflicting lane selection mechanism to
determine vehicles' passing priority; and the second level applies a deep
Q-network algorithm to identify the optimal platoon size based on the real-time
traffic condition of an intersection. When being tested on a traffic
micro-simulator, our proposed model exhibits superior performances on travel
efficiency and fuel conservation as compared to the state-of-the-art methods.

本研究提出了一种基于深度强化学习的自适应编队交叉口控制模型，该模型采用第一到达优先策略和深度 Q 算法来决定车辆通行的优先级和最佳编队大小，该模型相较于现有方法在旅行效率和燃油节省方面表现出卓越的性能。

基于深度强化学习的自适应编队和预约交叉口控制建模

Modeling Adaptive Platoon and Reservation Based Autonomous Intersection Control: A Deep Reinforcement Learning Approach

We propose a reliable intersection control mechanism for strategic autonomous
and connected vehicles (agents) in non-cooperative environments. Each agent has
access to his/her earliest possible and desired passing times, and reports a
passing time to the intersection manager, who allocates the intersection
temporally to the agents in a First-Come-First-Serve basis. However, the agents
might have conflicting interests and can take actions strategically. To this
end, we analyze the strategic behaviors of the agents and formulate Nash
equilibria for all possible scenarios. Furthermore, among all Nash equilibria
we identify a socially optimal equilibrium that leads to a fair intersection
allocation, and correspondingly we describe a strategy-proof intersection
mechanism, which achieves reliable intersection control such that the strategic
agents do not have any incentive to misreport their passing times
strategically.

我们提出了一种可靠的交叉口控制机制，用于战略自主联网车辆（代理程序）在非合作环境中。