mlsanityData to 7 Oct 2026

A Two-stage Based Social Preference Recognition in Multi-Agent Autonomous Driving System

arXiv 2310.03303 ↗First posted 5 Oct 2023cs.ROcited by 1

On the map: End-to-end autonomous driving → Cooperative and V2X driving

Abstract (arXiv)

Multi-Agent Reinforcement Learning (MARL) has become a promising solution for constructing a multi-agent autonomous driving system (MADS) in complex and dense scenarios. But most methods consider agents acting selfishly, which leads to conflict behaviors. Some existing works incorporate the concept of social value orientation (SVO) to promote coordination, but they lack the knowledge of other agents' SVOs, resulting in conservative maneuvers. In this paper, we aim to tackle the mentioned problem by enabling the agents to understand other agents' SVOs. To accomplish this, we propose a two-stage system framework. Firstly, we train a policy by allowing the agents to share their ground truth SVOs to establish a coordinated traffic flow. Secondly, we develop a recognition network that estimates agents' SVOs and integrates it with the policy trained in the first stage. Experiments demonstrate that our developed method significantly improves the performance of the driving policy in MADS compared to two state-of-the-art MARL algorithms.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. Builds on 23 of its 36 references that are in the map. First posted on arXiv 5 Oct 2023.

Builds on

23 of 36 references.

Closest ancestors · 2

Same direction, newest first

  1. 2021Altruistic Maneuver Planning for Cooperative Autonomous Vehicles Using Multi-agent Advantage Actor-Critic12 Jul
  2. 2021Social Coordination and Altruism in Autonomous Driving1 Jul

Other references · 21

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020End-to-End Object Detection with Transformerscited by 7,384
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  4. 2017Deep Setscited by 1,394
  5. 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 528
  6. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 301
  7. 2021Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradientscited by 122
  8. 2017Flow: A Modular Learning Framework for Mixed Autonomy Trafficcited by 67
  9. 2019Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learningcited by 53
  10. 2020Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learningcited by 46
  11. 2019Driving in Dense Traffic with Model-Free Reinforcement Learningcited by 33
  12. 2021Learning to Simulate Self-Driven Particles System with Coordinated Policy Optimizationcited by 33
9 more
  1. 2022Interaction-aware Decision-making for Automated Vehicles using Social Value Orientationcited by 20
  2. 2020Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Datacited by 18
  3. 2020LUCIDGames: Online Unscented Inverse Dynamic Games for Adaptive Trajectory Prediction and Planningcited by 17
  4. 2021Cooperative Autonomous Vehicles that Sympathize with Human Driverscited by 17
  5. 2020B-GAP: Behavior-Rich Simulation and Navigation for Autonomous Drivingcited by 15
  6. 2018Courteous Autonomous Carscited by 14
  7. 2022Efficient Game-Theoretic Planning with Prediction Heuristic for Socially-Compliant Autonomous Drivingcited by 13
  8. 2020Online Parameter Estimation for Human Driver Behavior Predictioncited by 6
  9. 2020On Social Interactions of Merging Behaviors at Highway On-Ramps in Congested Trafficcited by 2

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectivescited by 27

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  1. 2022Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic
  2. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey
  3. 2022Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic
  4. 2023Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness
  5. 2025Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach
  6. 2026COIN: Collaborative Interaction-Aware Multi-Agent Reinforcement Learning for Self-Driving Systems
  7. 2022A Cooperative Optimal Control Framework for Connected and Automated Vehicles in Mixed Traffic Using Social Value Orientation
  8. 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic