# Social Interactions for Autonomous Driving: A Review and Perspectives

arXiv: https://arxiv.org/abs/2208.07541 · HTML: https://mlsanity.com/paper/2208.07541/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2022.

## Built on by (most cited first, 27 of 27)

- [GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving](https://mlsanity.com/paper/2303.05760/) (2023)
- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors](https://mlsanity.com/paper/2305.04412/) (2023)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [Towards Knowledge-driven Autonomous Driving](https://mlsanity.com/paper/2312.04316/) (2023)
- [DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social Experiences](https://mlsanity.com/paper/2406.03008/) (2024)
- [Hybrid-Prediction Integrated Planning for Autonomous Driving](https://mlsanity.com/paper/2402.02426/) (2024)
- [Vision-based Multi-future Trajectory Prediction: A Survey](https://mlsanity.com/paper/2302.10463/) (2023)
- [Occupancy Prediction-Guided Neural Planner for Autonomous Driving](https://mlsanity.com/paper/2305.03303/) (2023)
- [Disentangled Neural Relational Inference for Interpretable Motion Prediction](https://mlsanity.com/paper/2401.03599/) (2024)
- [Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout](https://mlsanity.com/paper/2605.05092/) (2026)
- [Bringing Diversity to Autonomous Vehicles: An Interpretable Multi-vehicle Decision-making and Planning Framework](https://mlsanity.com/paper/2302.06803/) (2023)
- [IR-STP: Enhancing Autonomous Driving with Interaction Reasoning in Spatio-Temporal Planning](https://mlsanity.com/paper/2311.02850/) (2023)
- [Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regression](https://mlsanity.com/paper/2404.16023/) (2024)
- [How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games](https://mlsanity.com/paper/2210.13064/) (2022)
- [STF: Spatial Temporal Fusion for Trajectory Prediction](https://mlsanity.com/paper/2311.18149/) (2023)
- [TPK: Trustworthy Trajectory Prediction Integrating Prior Knowledge For Interpretability and Kinematic Feasibility](https://mlsanity.com/paper/2505.06743/) (2025)
- [AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design Anywhere](https://mlsanity.com/paper/2304.12921/) (2023)
- [Evolving Testing Scenario Generation Method and Intelligence Evaluation Framework for Automated Vehicles](https://mlsanity.com/paper/2306.07142/) (2023)
- [Human-Like Implicit Intention Expression for Autonomous Driving Motion Planning: A Method Based on Learning Human Intention Priors](https://mlsanity.com/paper/2307.15950/) (2023)
- [Cross-cultural analysis of pedestrian group behaviour influence on crossing decisions in interactions with autonomous vehicles](https://mlsanity.com/paper/2408.03003/) (2024)
- [Cross-cultural Deployment of Autonomous Vehicles Using Data-light Inverse Reinforcement Learning](https://mlsanity.com/paper/2504.11506/) (2025)
- [Sky-Drive: A Distributed Multi-Agent Simulation Platform for Human-AI Collaborative and Socially-Aware Future Transportation](https://mlsanity.com/paper/2504.18010/) (2025)
- [Complementary Learning System Empowers Online Continual Learning of Vehicle Motion Forecasting in Smart Cities](https://mlsanity.com/paper/2508.19597/) (2025)
- [A Comparative Study of Graph Neural Network Layer Selection for Interaction Modelling in Driving Trajectory Prediction](https://mlsanity.com/paper/2606.14956/) (2026)
- [Choose Your Game Wisely: Measuring Game-Theoretic Structures in Real-World Vehicle Interactions](https://mlsanity.com/paper/2608.25917/) (2026)

## Abstract (arXiv)

No human drives a car in a vacuum; she/he must negotiate with other road users to achieve their goals in social traffic scenes. A rational human driver can interact with other road users in a socially-compatible way through implicit communications to complete their driving tasks smoothly in interaction-intensive, safety-critical environments. This paper aims to review the existing approaches and theories to help understand and rethink the interactions among human drivers toward social autonomous driving. We take this survey to seek the answers to a series of fundamental questions: 1) What is social interaction in road traffic scenes? 2) How to measure and evaluate social interaction? 3) How to model and reveal the process of social interaction? 4) How do human drivers reach an implicit agreement and negotiate smoothly in social interaction? This paper reviews various approaches to modeling and learning the social interactions between human drivers, ranging from optimization theory and graphical models to social force theory and behavioral & cognitive science. We also highlight some new directions, critical challenges, and opening questions for future research.