# Cooperative Autonomous Vehicles that Sympathize with Human Drivers

arXiv: https://arxiv.org/abs/2107.00898 · HTML: https://mlsanity.com/paper/2107.00898/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2021.

## Built on by (most cited first, 18 of 18)

- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic](https://mlsanity.com/paper/2202.00881/) (2022)
- [Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach](https://mlsanity.com/paper/2302.00171/) (2023)
- [Altruistic Maneuver Planning for Cooperative Autonomous Vehicles Using Multi-agent Advantage Actor-Critic](https://mlsanity.com/paper/2107.05664/) (2021)
- [Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach](https://mlsanity.com/paper/2202.07720/) (2022)
- [Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving](https://mlsanity.com/paper/2211.10585/) (2022)
- [Towards Learning Generalizable Driving Policies from Restricted Latent Representations](https://mlsanity.com/paper/2111.03688/) (2021)
- [Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions](https://mlsanity.com/paper/2410.07409/) (2024)
- [Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic](https://mlsanity.com/paper/2211.11963/) (2022)
- [Learning in Cooperative Multiagent Systems Using Cognitive and Machine Models](https://mlsanity.com/paper/2308.09219/) (2023)
- [A Two-stage Based Social Preference Recognition in Multi-Agent Autonomous Driving System](https://mlsanity.com/paper/2310.03303/) (2023)
- [Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test](https://mlsanity.com/paper/2402.17050/) (2024)
- [Learning Probabilistic Responsibility Allocations for Multi-Agent Interactions](https://mlsanity.com/paper/2604.13128/) (2026)
- [Zero-shot Transfer Learning of Driving Policy via Socially Adversarial Traffic Flow](https://mlsanity.com/paper/2304.12821/) (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)
- [RACE-SM: Reinforcement Learning Based Autonomous Control for Social On-Ramp Merging](https://mlsanity.com/paper/2403.03359/) (2024)
- [Towards Developing Socially Compliant Automated Vehicles: Advances, Expert Insights, and A Conceptual Framework](https://mlsanity.com/paper/2501.06089/) (2025)
- [Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow](https://mlsanity.com/paper/2506.14502/) (2025)

## Abstract (arXiv)

Widespread adoption of autonomous vehicles will not become a reality until solutions are developed that enable these intelligent agents to co-exist with humans. This includes safely and efficiently interacting with human-driven vehicles, especially in both conflictive and competitive scenarios. We build up on the prior work on socially-aware navigation and borrow the concept of social value orientation from psychology -- that formalizes how much importance a person allocates to the welfare of others -- in order to induce altruistic behavior in autonomous driving. In contrast with existing works that explicitly model the behavior of human drivers and rely on their expected response to create opportunities for cooperation, our Sympathetic Cooperative Driving (SymCoDrive) paradigm trains altruistic agents that realize safe and smooth traffic flow in competitive driving scenarios only from experiential learning and without any explicit coordination. We demonstrate a significant improvement in both safety and traffic-level metrics as a result of this altruistic behavior and importantly conclude that the level of altruism in agents requires proper tuning as agents that are too altruistic also lead to sub-optimal traffic flow. The code and supplementary material are available at: https://symcodrive.toghi.net/