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

Crosato, Luca and Wei, Chongfeng and Ho, Edmond S. L. and Shum, Hubert P. H. (2021) 'Human-centric Autonomous Driving in an AV-Pedestrian Interactive Environment Using SVO.', IEEE ICHMS 2021 - 2nd IEEE International Conference on Human-Machine Systems Magdeburg / Virtual, 8-10 Sept 2021.

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.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://www.ichms2021.de/
Publisher statement:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:06 July 2021
Date deposited:14 July 2021
Date of first online publication:2021
Date first made open access:11 September 2021

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