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

Crosato, Luca, Shum, Hubert P. H., Ho, Edmond and Wei, Chongfeng (2023) Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation. IEEE Transactions on Intelligent Vehicles, 8 (2). pp. 1339-1349. ISSN 2379-8858

AAM.pdf - Accepted Version

Download (4MB) | Preview
Official URL:


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.

Item Type: Article
Uncontrolled Keywords: Artificial Intelligence, Control and Optimization, Automotive Engineering
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 27 Jul 2022 13:47
Last Modified: 05 Apr 2023 13:15

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics