Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation
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...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10445226/ |
| _version_ | 1797271521243168768 |
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| author | Laura Ferrarotti Massimiliano Luca Gabriele Santin Giorgio Previati Gianpiero Mastinu Massimiliano Gobbi Elena Campi Lorenzo Uccello Antonino Albanese Praveen Zalaya Alessandro Roccasalva Bruno Lepri |
| author_facet | Laura Ferrarotti Massimiliano Luca Gabriele Santin Giorgio Previati Gianpiero Mastinu Massimiliano Gobbi Elena Campi Lorenzo Uccello Antonino Albanese Praveen Zalaya Alessandro Roccasalva Bruno Lepri |
| author_sort | Laura Ferrarotti |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T14:04:46Z |
| format | Article |
| id | doaj.art-3bc2762e7cb94c6bbe50fc4ff6a9cb9e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T14:04:46Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3bc2762e7cb94c6bbe50fc4ff6a9cb9e2024-03-07T00:00:19ZengIEEEIEEE Access2169-35362024-01-0112326933270510.1109/ACCESS.2024.337046910445226Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative EvaluationLaura Ferrarotti0https://orcid.org/0000-0002-8099-5516Massimiliano Luca1https://orcid.org/0000-0001-6964-9877Gabriele Santin2Giorgio Previati3https://orcid.org/0000-0001-6450-1566Gianpiero Mastinu4https://orcid.org/0000-0001-5601-9059Massimiliano Gobbi5https://orcid.org/0000-0002-2631-8856Elena Campi6https://orcid.org/0009-0005-0740-1178Lorenzo Uccello7https://orcid.org/0009-0008-1080-3874Antonino Albanese8https://orcid.org/0000-0002-7477-6384Praveen Zalaya9Alessandro Roccasalva10Bruno Lepri11https://orcid.org/0000-0003-1275-2333Mobile and Social Computing Laboratory, Fondazione Bruno Kessler, Trento, ItalyMobile and Social Computing Laboratory, Fondazione Bruno Kessler, Trento, ItalyMobile and Social Computing Laboratory, Fondazione Bruno Kessler, Trento, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Milan, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Milan, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Milan, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Milan, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, Milan, ItalyItaltel S.p.A., Milan, ItalyCentro Ricerche Fiat, Turin, ItalyCentro Ricerche Fiat, Turin, ItalyMobile and Social Computing Laboratory, Fondazione Bruno Kessler, Trento, ItalyOptimizing 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.https://ieeexplore.ieee.org/document/10445226/Transportationautonomous vehiclesurban mobilityreinforcement learning |
| spellingShingle | Laura Ferrarotti Massimiliano Luca Gabriele Santin Giorgio Previati Gianpiero Mastinu Massimiliano Gobbi Elena Campi Lorenzo Uccello Antonino Albanese Praveen Zalaya Alessandro Roccasalva Bruno Lepri Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation IEEE Access Transportation autonomous vehicles urban mobility reinforcement learning |
| title | Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation |
| title_full | Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation |
| title_fullStr | Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation |
| title_full_unstemmed | Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation |
| title_short | Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation |
| title_sort | autonomous and human driven vehicles interacting in a roundabout a quantitative and qualitative evaluation |
| topic | Transportation autonomous vehicles urban mobility reinforcement learning |
| url | https://ieeexplore.ieee.org/document/10445226/ |
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