Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation

• URL: http://arxiv.org/abs/2309.08254v2
• Date: Fri, 23 Feb 2024 19:47:45 GMT
• Title: Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation
• Authors: Laura Ferrarotti, Massimiliano Luca, Gabriele Santin, Giorgio Previati, Gianpiero Mastinu, Massimiliano Gobbi, Elena Campi, Lorenzo Uccello, Antonino Albanese, Praveen Zalaya, Alessandro Roccasalva, Bruno Lepri
• Abstract summary: We learn a policy to minimize traffic jams and to minimize pollution in a roundabout in Milan, Italy. We qualitatively evaluate the learned policy using a cutting-edge cockpit to assess its performance in near-real-world conditions. Our findings show that human-driven vehicles benefit from optimizing AVs dynamics.
• Score: 34.67306374722473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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