# Cooperative Behavior Planning for Automated Driving using Graph Neural Networks

arXiv: https://arxiv.org/abs/2202.11376 · HTML: https://mlsanity.com/paper/2202.11376/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving.
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)

## Builds on (most cited first, 14 of 14)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [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)
- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (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)
- [Dynamic Input for Deep Reinforcement Learning in Autonomous Driving](https://mlsanity.com/paper/1907.10994/) (2019)
- [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, 10 of 10)

- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [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)
- [MEGA: A Memory-Efficient GNN Accelerator Exploiting Degree-Aware Mixed-Precision Quantization](https://mlsanity.com/paper/2311.09775/) (2023)
- [CoDriveVLM: VLM-Enhanced Urban Cooperative Dispatching and Motion Planning for Future Autonomous Mobility on Demand Systems](https://mlsanity.com/paper/2501.06132/) (2025)
- [Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications](https://mlsanity.com/paper/2305.16196/) (2023)
- [FastGL: A GPU-Efficient Framework for Accelerating Sampling-Based GNN Training at Large Scale](https://mlsanity.com/paper/2409.14939/) (2024)
- [A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2405.08655/) (2024)
- [Explainable Scene Understanding with Qualitative Representations and Graph Neural Networks](https://mlsanity.com/paper/2504.12817/) (2025)
- [A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management](https://mlsanity.com/paper/2507.09311/) (2025)

## Abstract (arXiv)

Urban intersections are prone to delays and inefficiencies due to static precedence rules and occlusions limiting the view on prioritized traffic. Existing approaches to improve traffic flow, widely known as automatic intersection management systems, are mostly based on non-learning reservation schemes or optimization algorithms. Machine learning-based techniques show promising results in planning for a single ego vehicle. This work proposes to leverage machine learning algorithms to optimize traffic flow at urban intersections by jointly planning for multiple vehicles. Learning-based behavior planning poses several challenges, demanding for a suited input and output representation as well as large amounts of ground-truth data. We address the former issue by using a flexible graph-based input representation accompanied by a graph neural network. This allows to efficiently encode the scene and inherently provide individual outputs for all involved vehicles. To learn a sensible policy, without relying on the imitation of expert demonstrations, the cooperative planning task is considered as a reinforcement learning problem. We train and evaluate the proposed method in an open-source simulation environment for decision making in automated driving. Compared to a first-in-first-out scheme and traffic governed by static priority rules, the learned planner shows a significant gain in flow rate, while reducing the number of induced stops. In addition to synthetic simulations, the approach is also evaluated based on real-world traffic data taken from the publicly available inD dataset.