Human-centric Autonomous Driving in an AV-Pedestrian Interactive Environment Using SVO

Crosato, Luca, Wei, Chongfeng, Ho, Edmond and Shum, Hubert P.H. (2021) Human-centric Autonomous Driving in an AV-Pedestrian Interactive Environment Using SVO. In: IEEE ICHMS 2021: 2nd IEEE International Conference on Human-Machine Systems: Human Centered Systems for our Digital World, 8-10 Sep 2021, Magdeburg, Germany. (In Press)

IEEE_ICHMS_2021_paper_14.pdf - Accepted Version

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As Autonomous Vehicles (AV) are becoming a reality, the design of efficient motion control algorithms will have to deal with the unpredictable and interactive nature of other road users. Current AV motion planning algorithms suffer from the freezing robot problem, as they often tend to overestimate collision risks. To tackle this problem and design AV that behave human-like, we integrate a concept from Psychology called Social Value Orientation into the Reinforcement Learning (RL) framework. The addition of a social term in the reward function design allows us to tune the AV behaviour towards the pedestrian from a more reckless to an extremely prudent one. We train the vehicle agent with a state of the art RL algorithm and show that Social Value Orientation is an effective tool to obtain pro-social AV behaviour.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Autonomous Vehicle-Pedestrian Interaction, Autonomous Vehicle, Reinforcement Learning, Human-Robot Interaction (HRI), Social Value Orientation, Social Behaviour
Subjects: G600 Software Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Faculties > Engineering and Environment > Mechanical and Construction Engineering
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Depositing User: John Coen
Date Deposited: 28 Jul 2021 12:18
Last Modified: 22 Oct 2021 11:26

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