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. While optimizing Reinforcement Learning (RL) policies for such scenarios is becoming more and more common, little has been said about realistic evaluations of such trained policies. This paper presents an evaluation of the effects of AVs penetration among human drivers in a roundabout scenario, considering both quantitative and qualitative aspects. In particular, we learn 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 the presence of AVs} 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 conduct 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 highlight that the scenario with 80% AVs is perceived as safer than the scenario with 20%. The same result is obtained for traffic smoothness perception.

翻译：在日益发展的交通格局中优化交通动态至关重要，尤其是在自动驾驶车辆（AV）以不同自主化水平与人类驾驶车辆共存的情景下。虽然为此类场景优化强化学习（RL）策略已日渐普遍，但对这些训练策略的现实评估却鲜有探讨。本文针对意大利米兰一处环形交叉口，从定量与定性两个方面，评估了人类驾驶员中AV渗透率的影响。具体而言，我们学习了一种策略，旨在最小化交通拥堵（即缩短通过场景的时间）并降低污染。通过实证分析，我们证明了AV的存在能够减少时间消耗和污染水平。此外，我们利用前沿驾驶模拟舱对学习策略进行了定性评估，以考察其在接近真实环境条件下的性能。为衡量该策略的实用性和可接受性，我们邀请人类参与者使用模拟器进行评估，重点关注交通流畅度和安全感知等指标。总体而言，研究结果表明，人类驾驶车辆能够从优化AV动态中获益。同时，研究参与者强调，AV占比80%的场景比20%的场景被认为更安全，交通流畅度感知也得出相同结论。