# Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized Intersections

arXiv: https://arxiv.org/abs/2301.05294 · HTML: https://mlsanity.com/paper/2301.05294/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 76 references that are in the map.
- First posted on arXiv 12 Jan 2023.

## Builds on (most cited first, 24 of 24)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer](https://mlsanity.com/paper/2207.14024/) (2022)
- [A Review of Safe Reinforcement Learning: Methods, Theory and Applications](https://mlsanity.com/paper/2205.10330/) (2022)
- [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://mlsanity.com/paper/2209.05324/) (2022)
- [A Survey on Traffic Signal Control Methods](https://mlsanity.com/paper/1904.08117/) (2019)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic](https://mlsanity.com/paper/1911.04074/) (2019)
- [Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous Vehicles](https://mlsanity.com/paper/1812.06120/) (2018)
- [Reinforcement Learning for Mixed Autonomy Intersections](https://mlsanity.com/paper/2111.04686/) (2021)
- [Unified Automatic Control of Vehicular Systems with Reinforcement Learning](https://mlsanity.com/paper/2208.00268/) (2022)
- [A Decentralized Energy-Optimal Control Framework for Connected Automated Vehicles at Signal-Free Intersections](https://mlsanity.com/paper/1602.03786/) (2016)
- [Scalable Multiagent Driving Policies For Reducing Traffic Congestion](https://mlsanity.com/paper/2103.00058/) (2021)
- [Using Reinforcement Learning with Partial Vehicle Detection for Intelligent Traffic Signal Control](https://mlsanity.com/paper/1807.01628/) (2018)
- [Polling-systems-based Autonomous Vehicle Coordination in Traffic Intersections with No Traffic Signals](https://mlsanity.com/paper/1607.07896/) (2016)
- [Towards a Systematic Computational Framework for Modeling Multi-Agent Decision-Making at Micro Level for Smart Vehicles in a Smart World](https://mlsanity.com/paper/2009.12213/) (2020)
- [Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning](https://mlsanity.com/paper/2106.06369/) (2021)
- [Optimal Control of Connected and Automated Vehicles at Roundabouts: An Investigation in a Mixed-Traffic Environment](https://mlsanity.com/paper/1710.11295/) (2017)
- [COOR-PLT: A hierarchical control model for coordinating adaptive platoons of connected and autonomous vehicles at signal-free intersections based on deep reinforcement learning](https://mlsanity.com/paper/2207.07195/) (2022)
- [Learning Autonomous Vehicle Safety Concepts from Demonstrations](https://mlsanity.com/paper/2210.02761/) (2022)

## Built on by (most cited first, 12 of 12)

- [Mixture-of-Personas Language Models for Population Simulation](https://mlsanity.com/paper/2504.05019/) (2025)
- [Mixed Traffic Control and Coordination from Pixels](https://mlsanity.com/paper/2302.09167/) (2023)
- [EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning](https://mlsanity.com/paper/2311.12261/) (2023)
- [CARL: Congestion-Aware Reinforcement Learning for Imitation-based Perturbations in Mixed Traffic Control](https://mlsanity.com/paper/2404.00796/) (2024)
- [Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2504.04691/) (2025)
- [Learning to Change: Choreographing Mixed Traffic Through Lateral Control and Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2403.14879/) (2024)
- [Exploring Transformer-Augmented LSTM for Temporal and Spatial Feature Learning in Trajectory Prediction](https://mlsanity.com/paper/2412.13419/) (2024)
- [Neighbor-Aware Reinforcement Learning for Mixed Traffic Optimization in Large-scale Networks](https://mlsanity.com/paper/2412.12622/) (2024)
- [Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmark](https://mlsanity.com/paper/2501.16728/) (2025)
- [LDGNet: A Lightweight Difference Guiding Network for Remote Sensing Change Detection](https://mlsanity.com/paper/2504.05062/) (2025)
- [Multi-Objective Reinforcement Learning for Large-Scale Mixed Traffic Control](https://mlsanity.com/paper/2512.11247/) (2025)
- [Learning to Design City-scale Transit Routes](https://mlsanity.com/paper/2512.19767/) (2025)

## Abstract (arXiv)

Intersections are essential road infrastructures for traffic in modern metropolises. However, they can also be the bottleneck of traffic flows as a result of traffic incidents or the absence of traffic coordination mechanisms such as traffic lights. Recently, various control and coordination mechanisms that are beyond traditional control methods have been proposed to improve the efficiency of intersection traffic by leveraging the ability of autonomous vehicles. Amongst these methods, the control of foreseeable mixed traffic that consists of human-driven vehicles (HVs) and robot vehicles (RVs) has emerged. We propose a decentralized multi-agent reinforcement learning approach for the control and coordination of mixed traffic by RVs at real-world, complex intersections -- an open challenge to date. We design comprehensive experiments to evaluate the effectiveness, robustness, generalizablility, and adaptability of our approach. In particular, our method can prevent congestion formation via merely 5% RVs under a real-world traffic demand of 700 vehicles per hour. In contrast, without RVs, congestion will form when the traffic demand reaches as low as 200 vehicles per hour. Moreover, when the RV penetration rate exceeds 60%, our method starts to outperform traffic signal control in terms of the average waiting time of all vehicles. Our method is not only robust against blackout events, sudden RV percentage drops, and V2V communication error, but also enjoys excellent generalizablility, evidenced by its successful deployment in five unseen intersections. Lastly, our method performs well under various traffic rules, demonstrating its adaptability to diverse scenarios. Videos and code of our work are available at https://sites.google.com/view/mixedtrafficcontrol