# An Enhanced Graph Representation for Machine Learning Based Automatic Intersection Management

arXiv: https://arxiv.org/abs/2207.08655 · HTML: https://mlsanity.com/paper/2207.08655/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 18 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [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)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [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)
- [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)

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

- [Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2301.12717/) (2023)
- [Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving](https://mlsanity.com/paper/2304.08280/) (2023)
- [A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2405.08655/) (2024)
- [A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management](https://mlsanity.com/paper/2507.09311/) (2025)

## Abstract (arXiv)

The improvement of traffic efficiency at urban intersections receives strong research interest in the field of automated intersection management. So far, mostly non-learning algorithms like reservation or optimization-based ones were proposed to solve the underlying multi-agent planning problem. At the same time, automated driving functions for a single ego vehicle are increasingly implemented using machine learning methods. In this work, we build upon a previously presented graph-based scene representation and graph neural network to approach the problem using reinforcement learning. The scene representation is improved in key aspects by using edge features in addition to the existing node features for the vehicles. This leads to an increased representation quality that is leveraged by an updated network architecture. The paper provides an in-depth evaluation of the proposed method against baselines that are commonly used in automatic intersection management. Compared to a traditional signalized intersection and an enhanced first-in-first-out scheme, a significant reduction of induced delay is observed at varying traffic densities. Finally, the generalization capability of the graph-based representation is evaluated by testing the policy on intersection layouts not seen during training. The model generalizes virtually without restrictions to smaller intersection layouts and within certain limits to larger ones.