mlsanityData to 14 Oct 2026

Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic

arXiv 2211.11963 ↗First posted 22 Nov 2022cs.RO cs.LGcited by 1

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

Abstract (arXiv)

It is expected that autonomous vehicles(AVs) and heterogeneous human-driven vehicles(HVs) will coexist on the same road. The safety and reliability of AVs will depend on their social awareness and their ability to engage in complex social interactions in a socially accepted manner. However, AVs are still inefficient in terms of cooperating with HVs and struggle to understand and adapt to human behavior, which is particularly challenging in mixed autonomy. In a road shared by AVs and HVs, the social preferences or individual traits of HVs are unknown to the AVs and different from AVs, which are expected to follow a policy, HVs are particularly difficult to forecast since they do not necessarily follow a stationary policy. To address these challenges, we frame the mixed-autonomy problem as a multi-agent reinforcement learning (MARL) problem and propose an approach that allows AVs to learn the decision-making of HVs implicitly from experience, account for all vehicles' interests, and safely adapt to other traffic situations. In contrast with existing works, we quantify AVs' social preferences and propose a distributed reward structure that introduces altruism into their decision-making process, allowing the altruistic AVs to learn to establish coalitions and influence the behavior of HVs.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. Builds on 36 of its 66 references that are in the map. First posted on arXiv 22 Nov 2022.

Builds on

36 of 66 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 · 34

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
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22 more
  1. 2019Autonomous Highway Driving using Deep Reinforcement Learningcited by 22
  2. 2019Cooperation-Aware Reinforcement Learning for Merging in Dense Trafficcited by 20
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  18. 2021Impact of Communication Loss on MPC based Cooperative Adaptive Cruise Control and Platooningcited by 4
  19. 2021Cooperation for Scalable Supervision of Autonomy in Mixed Trafficcited by 4
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Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Trust-MARL: Trust-Based Multi-Agent Reinforcement Learning Framework for Cooperative On-Ramp Merging Control in Heterogeneous Traffic Flowcited by 3

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  1. 2022Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic
  2. 2023Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness
  3. 2022Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving
  4. 2026Collective Autonomous Vehicle Routing Needs Social Awareness for Coexistence with Human Drivers
  5. 2025Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach
  6. 2023A Two-stage Based Social Preference Recognition in Multi-Agent Autonomous Driving System
  7. 2025Socially-Aware Autonomous Driving: Inferring Yielding Intentions for Safer Interactions
  8. 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic