# Social Interaction-Aware Dynamical Models and Decision Making for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2310.18891 · HTML: https://mlsanity.com/paper/2310.18891/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2023.

## Built on by (most cited first, 13 of 13)

- [Context-aware Multi-task Learning for Pedestrian Intent and Trajectory Prediction](https://mlsanity.com/paper/2407.17162/) (2024)
- [Agile Decision-Making and Safety-Critical Motion Planning for Emergency Autonomous Vehicles](https://mlsanity.com/paper/2409.08665/) (2024)
- [MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving](https://mlsanity.com/paper/2512.03795/) (2025)
- [Modeling 3D Pedestrian-Vehicle Interactions for Vehicle-Conditioned Pose Forecasting](https://mlsanity.com/paper/2602.08962/) (2026)
- [Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception](https://mlsanity.com/paper/2402.04370/) (2024)
- [MulCPred: Learning Multi-modal Concepts for Explainable Pedestrian Action Prediction](https://mlsanity.com/paper/2409.09446/) (2024)
- [Towards Developing Socially Compliant Automated Vehicles: Advances, Expert Insights, and A Conceptual Framework](https://mlsanity.com/paper/2501.06089/) (2025)
- [Understanding Driver Cognition and Decision-Making Behaviors in High-Risk Scenarios: A Drift Diffusion Perspective](https://mlsanity.com/paper/2503.12637/) (2025)
- [Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow](https://mlsanity.com/paper/2506.14502/) (2025)
- [Task Allocation for Autonomous Machines using Computational Intelligence and Deep Reinforcement Learning](https://mlsanity.com/paper/2508.20688/) (2025)
- [IRL-DAL: Safe and Adaptive Trajectory Planning for Autonomous Driving via Energy-Guided Diffusion Models](https://mlsanity.com/paper/2601.23266/) (2026)
- [Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videos](https://mlsanity.com/paper/2606.18824/) (2026)
- [Choose Your Game Wisely: Measuring Game-Theoretic Structures in Real-World Vehicle Interactions](https://mlsanity.com/paper/2608.25917/) (2026)

## Abstract (arXiv)

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 autonomous vehicle 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 in this work. Commencing with an examination of terminology, attention is drawn to challenges and existing models employed for modelling the behaviour of drivers and pedestrians. Next, a comprehensive review is conducted on various techniques proposed for interaction modelling, 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.