Optimizing traffic dynamics in an evolving transportation landscape is
crucial, particularly in scenarios where autonomous vehicles (AVs) with varying
levels of autonomy coexist with human-driven cars. This paper presents a novel
approach to optimizing choices of AVs using Proximal Policy Optimization (PPO),
a reinforcement learning algorithm. We learned a policy to minimize traffic
jams (i.e., minimize the time to cross the scenario) and to minimize pollution
in a roundabout in Milan, Italy. Through empirical analysis, we demonstrate
that our approach can reduce time and pollution levels. Furthermore, we
qualitatively evaluate the learned policy using a cutting-edge cockpit to
assess its performance in near-real-world conditions. To gauge the practicality
and acceptability of the policy, we conducted evaluations with human
participants using the simulator, focusing on a range of metrics like traffic
smoothness and safety perception. In general, our findings show that
human-driven vehicles benefit from optimizing AVs dynamics. Also, participants
in the study highlighted that the scenario with 80\% AVs is perceived as safer
than the scenario with 20\%. The same result is obtained for traffic smoothness
perception.

优化自动驾驶车辆的交通动态是至关重要的，本文通过采用强化学习算法 —— 近端策略优化（PPO），得出用于最小化交通拥堵和污染的自动驾驶车辆选择的新方法，并通过实证分析证明该方法可以降低时间和污染水平。