# Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks

arXiv: https://arxiv.org/abs/2301.12717 · HTML: https://mlsanity.com/paper/2301.12717/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 30 Jan 2023.

## Builds on (most cited first, 17 of 17)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018)
- [COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles](https://mlsanity.com/paper/2205.02222/) (2022)
- [Relational Graph Attention Networks](https://mlsanity.com/paper/1904.05811/) (2019)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety](https://mlsanity.com/paper/1903.00640/) (2019)
- [Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving](https://mlsanity.com/paper/1909.13582/) (2019)
- [Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments](https://mlsanity.com/paper/2006.12576/) (2020)
- [A Decentralized Energy-Optimal Control Framework for Connected Automated Vehicles at Signal-Free Intersections](https://mlsanity.com/paper/1602.03786/) (2016)
- [Cooperative Behavior Planning for Automated Driving using Graph Neural Networks](https://mlsanity.com/paper/2202.11376/) (2022)
- [Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search](https://mlsanity.com/paper/1807.09530/) (2018)
- [Autonomous and Semi-Autonomous Intersection Management: A Survey](https://mlsanity.com/paper/2006.13133/) (2020)
- [End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2104.13617/) (2021)
- [An Enhanced Graph Representation for Machine Learning Based Automatic Intersection Management](https://mlsanity.com/paper/2207.08655/) (2022)

## Built on by (most cited first, 4 of 4)

- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving](https://mlsanity.com/paper/2304.08280/) (2023)
- [Camera Perspective Transformation to Bird's Eye View via Spatial Transformer Model for Road Intersection Monitoring](https://mlsanity.com/paper/2408.05577/) (2024)
- [A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management](https://mlsanity.com/paper/2507.09311/) (2025)

## Abstract (arXiv)

Connected automated driving has the potential to significantly improve urban traffic efficiency, e.g., by alleviating issues due to occlusion. Cooperative behavior planning can be employed to jointly optimize the motion of multiple vehicles. Most existing approaches to automatic intersection management, however, only consider fully automated traffic. In practice, mixed traffic, i.e., the simultaneous road usage by automated and human-driven vehicles, will be prevalent. The present work proposes to leverage reinforcement learning and a graph-based scene representation for cooperative multi-agent planning. We build upon our previous works that showed the applicability of such machine learning methods to fully automated traffic. The scene representation is extended for mixed traffic and considers uncertainty in the human drivers' intentions. In the simulation-based evaluation, we model measurement uncertainties through noise processes that are tuned using real-world data. The paper evaluates the proposed method against an enhanced first in - first out scheme, our baseline for mixed traffic management. With increasing share of automated vehicles, the learned planner significantly increases the vehicle throughput and reduces the delay due to interaction. Non-automated vehicles benefit virtually alike.