Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Crosato, Luca; Tian, Kai; Shum, Hubert P.H.; Ho, Edmond S.L.; Wang, Yafei; Wei, Chongfeng

Abstract

Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the AV to be able to understand and predict the behaviour of human road users. In this literature review, the current state of IAAD research is surveyed. Commencing with an examination of terminology, attention is drawn to challenges and existing models employed for modeling the behaviour of drivers and pedestrians. Next, a comprehensive review is conducted on various techniques proposed for interaction modeling, encompassing cognitive methods, machine‐learning approaches, and game‐theoretic methods. The conclusion is reached through a discussion of potential advantages and risks associated with IAAD, along with the illumination of pivotal research inquiries necessitating future exploration.

Citation

Crosato, L., Tian, K., Shum, H. P., Ho, E. S., Wang, Y., & Wei, C. (2023). Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles. Advanced Intelligent Systems, https://doi.org/10.1002/aisy.202300575

• Journal Article Type: Article
• Acceptance Date: Nov 6, 2023
• Online Publication Date: Dec 1, 2023
• Publication Date: Dec 1, 2023
• Deposit Date: Nov 30, 2023
• Publicly Available Date: Dec 8, 2023
• Journal: Advanced Intelligent Systems
• Print ISSN: 2640-4567
• Electronic ISSN: 2640-4567
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1002/aisy.202300575
• Keywords: socially-aware decision making, interaction-aware autonomous driving, multi-agent interactions, behavioral models, pedestrians
• Public URL: https://durham-repository.worktribe.com/output/1963750