Using Physiological Metrics to Improve Reinforcement Learning for Autonomous Vehicles

Michael Fleicher, Oren Musicant, Amos Azaria

פרסום מחקרי: פרק בספר / בדוח / בכנספרסום בספר כנסביקורת עמיתים

2 ציטוטים ‏(Scopus)

תקציר

Thanks to recent technological advances Autonomous Vehicles (AVs) are becoming available at some locations. Safety impacts of these devices have, however, been difficult to assess. In this paper we utilize physiological metrics to improve the performance of a reinforcement learning agent attempting to drive an autonomous vehicle in simulation. We measure the performance of our reinforcement learner in several aspects, including the amount of stress imposed on potential passengers, the number of training episodes required, and a score measuring the vehicle's speed as well as the distance successfully traveled by the vehicle, without traveling off-track or hitting a different vehicle. To that end, we compose a human model, which is based on a dataset of physiological metrics of passengers in an autonomous vehicle. We embed this model in a reinforcement learning agent by providing negative reward to the agent for actions that cause the human model an increase in heart rate. We show that such a 'passenger-aware' reinforcement learner agent does not only reduce the stress imposed on hypothetical passengers, but, quite surprisingly, also drives safer and its learning process is more effective than an agent that does not obtain rewards from a human model.

שפה מקוריתאנגלית
כותר פרסום המארחProceedings - 2022 IEEE 34th International Conference on Tools with Artificial Intelligence, ICTAI 2022
עורכיםMarek Reformat, Du Zhang, Nikolaos G. Bourbakis
מוציא לאורIEEE Computer Society
עמודים1223-1230
מספר עמודים8
מסת"ב (אלקטרוני)9798350397444
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 2022
אירוע34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022 - Virtual, Online, סין
משך הזמן: 31 אוק׳ 20222 נוב׳ 2022

סדרות פרסומים

שםProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
כרך2022-October
ISSN (מודפס)1082-3409

כנס

כנס34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022
מדינה/אזורסין
עירVirtual, Online
תקופה31/10/222/11/22

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