Social Attention for Autonomous Decision-Making in Dense Traffic

2019/11/27 by Edouard Leurent, Leurent, Edouard, Jean Mercat +1 · 6 citations
Engineering · Psychology · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Human-Automation Interaction and Safety #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Traffic control and management

paper · pdf · doi:10.48550/arxiv.1911.12250

openalex publication_date 2019/11/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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/.

Citations

• Congested traffic states in empirical observations and microscopic simulations
• End to End Learning for Self-Driving Cars
• Recurrent World Models Facilitate Policy Evolution
• A Reduction of Imitation Learning and Structured Prediction to No-Regret\n Online Learning

Cited by

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• An Uncertainty-Weighted Decision Transformer for Navigation in Dense, Complex Driving Scenarios
• Robust Real-Time Coordination of CAVs: A Distributed Optimization Framework under Uncertainty
• IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving
• Goal-conditioned Hierarchical Reinforcement Learning for Sample-efficient and Safe Autonomous Driving at Intersections
• ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving

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