# Social Attention for Autonomous Decision-Making in Dense Traffic

arXiv: https://arxiv.org/abs/1911.12250 · HTML: https://mlsanity.com/paper/1911.12250/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

## Built on by (most cited first, 30 of 39)

- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.08052/) (2025)
- [Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph](https://mlsanity.com/paper/2203.01821/) (2022)
- [CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-scale Reinforcement Learning in Autonomous Driving](https://mlsanity.com/paper/2502.19908/) (2025)
- [Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models](https://mlsanity.com/paper/2204.02392/) (2022)
- [TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving](https://mlsanity.com/paper/2203.16792/) (2022)
- [A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning](https://mlsanity.com/paper/2101.06778/) (2021)
- [Control-Aware Prediction Objectives for Autonomous Driving](https://mlsanity.com/paper/2204.13319/) (2022)
- [DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks](https://mlsanity.com/paper/2108.05030/) (2021)
- [Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness](https://mlsanity.com/paper/2309.09726/) (2023)
- [Dream to Drive with Predictive Individual World Model](https://mlsanity.com/paper/2501.16733/) (2025)
- [Addressing Action Oscillations through Learning Policy Inertia](https://mlsanity.com/paper/2103.02287/) (2021)
- [HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments](https://mlsanity.com/paper/2411.12150/) (2024)
- [Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic](https://mlsanity.com/paper/2202.09644/) (2022)
- [Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers](https://mlsanity.com/paper/2205.00562/) (2022)
- [Parameterized Decision-making with Multi-modal Perception for Autonomous Driving](https://mlsanity.com/paper/2312.11935/) (2023)
- [Learning interaction rules from multi-animal trajectories via augmented behavioral models](https://mlsanity.com/paper/2107.05326/) (2021)
- [Vision Transformer for Learning Driving Policies in Complex Multi-Agent Environments](https://mlsanity.com/paper/2109.06514/) (2021)
- [Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectives](https://mlsanity.com/paper/2505.09074/) (2025)
- [A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios](https://mlsanity.com/paper/2109.10557/) (2021)
- [A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges](https://mlsanity.com/paper/2211.06665/) (2022)
- [Don't Watch Me: A Spatio-Temporal Trojan Attack on Deep-Reinforcement-Learning-Augment Autonomous Driving](https://mlsanity.com/paper/2211.14440/) (2022)
- [Embodied Navigation with Auxiliary Task of Action Description Prediction](https://mlsanity.com/paper/2510.21809/) (2025)
- [XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques](https://mlsanity.com/paper/2402.12685/) (2024)
- [A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective](https://mlsanity.com/paper/2503.23650/) (2025)
- [Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning](https://mlsanity.com/paper/2109.10490/) (2021)
- [Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative Driving](https://mlsanity.com/paper/2403.11914/) (2024)
- [Spatially and Seamlessly Hierarchical Reinforcement Learning for State Space and Policy space in Autonomous Driving](https://mlsanity.com/paper/2111.05479/) (2021)
- [Self-Awareness Safety of Deep Reinforcement Learning in Road Traffic Junction Driving](https://mlsanity.com/paper/2201.08116/) (2022)
- [Scalable Multi-modal Model Predictive Control via Duality-based Interaction Predictions](https://mlsanity.com/paper/2402.01116/) (2024)

## Abstract (arXiv)

We study the design of learning architectures for behavioural planning in a dense traffic setting. Such architectures should deal with a varying number of nearby vehicles, be invariant to the ordering chosen to describe them, while staying accurate and compact. We observe that the two most popular representations in the literature do not fit these criteria, and perform badly on an complex negotiation task. We propose an attention-based architecture that satisfies all these properties and explicitly accounts for the existing interactions between the traffic participants. We show that this architecture leads to significant performance gains, and is able to capture interactions patterns that can be visualised and qualitatively interpreted. Videos and code are available at https://eleurent.github.io/social-attention/.