# End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2104.13617 · HTML: https://mlsanity.com/paper/2104.13617/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 23 of 23)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://mlsanity.com/paper/1610.00633/) (2016)
- [Learning to Drive in a Day](https://mlsanity.com/paper/1807.00412/) (2018)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Playing for Benchmarks](https://mlsanity.com/paper/1709.07322/) (2017)
- [Deep Reinforcement Learning in Large Discrete Action Spaces](https://mlsanity.com/paper/1512.07679/) (2015)
- [Learning to Walk via Deep Reinforcement Learning](https://mlsanity.com/paper/1812.11103/) (2018)
- [Variational Autoencoder for Deep Learning of Images, Labels and Captions](https://mlsanity.com/paper/1609.08976/) (2016)
- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [Learning to Drive from Simulation without Real World Labels](https://mlsanity.com/paper/1812.03823/) (2018)
- [Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning](https://mlsanity.com/paper/1803.10056/) (2018)
- [End-to-End Deep Reinforcement Learning for Lane Keeping Assist](https://mlsanity.com/paper/1612.04340/) (2016)
- [Beyond Grand Theft Auto V for Training, Testing and Enhancing Deep Learning in Self Driving Cars](https://mlsanity.com/paper/1712.01397/) (2017)
- [Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning](https://mlsanity.com/paper/1705.01196/) (2017)
- [Emergent Road Rules In Multi-Agent Driving Environments](https://mlsanity.com/paper/2011.10753/) (2020)
- [Intelligent Roundabout Insertion using Deep Reinforcement Learning](https://mlsanity.com/paper/2001.00786/) (2020)
- [From Simulation to Real World Maneuver Execution using Deep Reinforcement Learning](https://mlsanity.com/paper/2005.07023/) (2020)
- [Microscopic Traffic Simulation by Cooperative Multi-agent Deep Reinforcement Learning](https://mlsanity.com/paper/1903.01365/) (2019)

## Built on by (most cited first, 6 of 6)

- [Cooperative Behavior Planning for Automated Driving using Graph Neural Networks](https://mlsanity.com/paper/2202.11376/) (2022)
- [Tackling Real-World Autonomous Driving using Deep Reinforcement Learning](https://mlsanity.com/paper/2207.02162/) (2022)
- [An Enhanced Graph Representation for Machine Learning Based Automatic Intersection Management](https://mlsanity.com/paper/2207.08655/) (2022)
- [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)
- [Introducing Risk Shadowing For Decisive and Comfortable Behavior Planning](https://mlsanity.com/paper/2307.10714/) (2023)

## Abstract (arXiv)

Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. However, for the majority of intersections regulated by traffic lights, the problem could be solved by a simple rule-based method in which the autonomous vehicle behavior is closely related to the traffic light states. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step. We demonstrate that agents learn both the basic rules needed to handle intersections by understanding the priorities of other learners inside the environment, and to drive safely along their paths. Moreover, a comparison between our system and a rule-based method proves that our model achieves better results especially with dense traffic conditions. Finally, we test our system on real world scenarios using real recorded traffic data, proving that our module is able to generalize both to unseen environments and to different traffic conditions.