Cooperative Automated Driving System (ADS)-equipped Vehicles (CoAVs) can be a promising solution to traffic congestion. We present a reinforcement learning strategy using Genetic Fuzzy Systems (GFS) for cooperative merge of vehicles onto a highway in high density, mixed-autonomy traffic conditions. The CoAVs are trained to make their own decisions purely based on information available from its surrounding vehicles, thus making this a decentralized system. The GFS module in each CoAV makes recommendations on the acceleration and lane-change decisions. The CoAVs are trained on different ADS behavioral parameters, such as aggressiveness of lane change, different CoAV market penetration rates (MPRs) and traffic congestion levels. The results show the effectiveness of increasing the MPR. It is noticed that the CoAVs trained with different ADS behavioral parameters can generate completely different cooperative maneuvers. The results also show that the trained CoAVs can cooperate even with human-driven vehicles to improve the overall traffic performance.

配备协作式自动驾驶系统 (ADS) 的车辆 (CoAV) 可以成为解决交通拥堵的有希望的解决方案。我们提出了一种使用遗传模糊系统 (GFS) 的强化学习策略，用于在高密度、混合自主交通条件下将车辆协同合并到高速公路上。CoAV 接受过训练，可以完全根据其周围车辆提供的信息做出自己的决定，从而使其成为一个分散的系统。每个 CoAV 中的 GFS 模块就加速和变道决策提出建议。CoAV 接受了不同 ADS 行为参数的训练，例如换道的积极性、不同的 CoAV 市场渗透率 (MPR) 和交通拥堵水平。结果显示了提高 MPR 的有效性。值得注意的是，使用不同 ADS 行为参数训练的 CoAV 可以产生完全不同的合作机动。结果还表明，经过训练的 CoAV 甚至可以与人类驾驶的车辆合作，以提高整体交通性能。