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

luca Crosato, Chongfeng Wei*, Edmond Ho, Hubert P. H. Shum

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationIEEE International Conference on Human-Machine Systems: Proceedings
Publisher IEEE
ISBN (Electronic)978-1-6654-0170-8
DOIs
Publication statusPublished - 27 Oct 2021
Externally publishedYes

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