Social interaction‐aware dynamical models and decision‐making for autonomous vehicles

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
27 Downloads (Pure)


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.

Original languageEnglish
Article number2300575
Number of pages23
JournalAdvanced Intelligent Systems
Early online date01 Dec 2023
Publication statusEarly online date - 01 Dec 2023


  • socially-aware decision making
  • interaction-aware autonomous driving
  • multi-agent interactions
  • behavioral models
  • pedestrians


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