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

Crosato, Luca; Wei, Chongfeng; Ho, Edmond S.L.; Shum, Hubert P.H.

doi:10.1109/ichms53169.2021.9582640

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

Crosato, Luca; Wei, Chongfeng; Ho, Edmond S.L.; Shum, Hubert P.H.

Authors

Luca Crosato

Chongfeng Wei

Edmond S.L. Ho

Dr Hubert Shum hubert.shum@durham.ac.uk
Associate Professor

Abstract

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.

Citation

Crosato, L., Wei, C., Ho, E. S., & Shum, H. P. (2021). Human-centric Autonomous Driving in an AV-Pedestrian Interactive Environment Using SVO. . https://doi.org/10.1109/ichms53169.2021.9582640

Conference Name	IEEE ICHMS 2021 - 2nd IEEE International Conference on Human-Machine Systems
Conference Location	Magdeburg, Germany
Start Date	Sep 8, 2021
End Date	Sep 10, 2021
Acceptance Date	Jul 6, 2021
Online Publication Date	Oct 27, 2021
Publication Date	2021
Deposit Date	Jul 13, 2021
Publicly Available Date	Sep 11, 2021
Publisher	Institute of Electrical and Electronics Engineers
ISBN	9781665401708
DOI	https://doi.org/10.1109/ichms53169.2021.9582640

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