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Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation

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

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Luca Crosato

Edmund S.L. Ho

Chongfeng Wei


Motion control algorithms in the presence of pedestrians are critical for the development of safe and reliable Autonomous Vehicles (AVs). Traditional motion control algorithms rely on manually designed decision-making policies which neglect the mutual interactions between AVs and pedestrians. On the other hand, recent advances in Deep Reinforcement Learning allow for the automatic learning of policies without manual designs. To tackle the problem of decision-making in the presence of pedestrians, the authors introduce a framework based on Social Value Orientation and Deep Reinforcement Learning (DRL) that is capable of generating decision-making policies with different driving styles. The policy is trained using stateof- the-art DRL algorithms in a simulated environment. A novel computationally-efficient pedestrian model that is suitable for DRL training is also introduced. We perform experiments to validate our framework and we conduct a comparative analysis of the policies obtained with two different model-free Deep Reinforcement Learning Algorithms. Simulations results show how the developed model exhibits natural driving behaviours, such as short-stopping, to facilitate the pedestrian’s crossing.


Crosato, L., Shum, H. P., Ho, E. S., & Wei, C. (2023). Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation. IEEE Transactions on Intelligent Vehicles, 8(2), 1339-1349.

Journal Article Type Article
Acceptance Date Jul 2, 2022
Online Publication Date Jul 11, 2022
Publication Date 2023-02
Deposit Date Jul 4, 2022
Publicly Available Date Jul 4, 2022
Journal IEEE Transactions on Intelligent Vehicles
Print ISSN 2379-8858
Electronic ISSN 2379-8904
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 8
Issue 2
Pages 1339-1349


Accepted Journal Article (4.9 Mb)

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