# Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning

arXiv: https://arxiv.org/abs/1810.10469 · HTML: https://mlsanity.com/paper/1810.10469/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2018.

## Built on by (most cited first, 13 of 13)

- [Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous Driving](https://mlsanity.com/paper/2105.10266/) (2021)
- [Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS](https://mlsanity.com/paper/2102.07244/) (2021)
- [Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning](https://mlsanity.com/paper/2107.07316/) (2021)
- [Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic](https://mlsanity.com/paper/2202.09644/) (2022)
- [Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios](https://mlsanity.com/paper/2107.04538/) (2021)
- [Safe Deep Q-Network for Autonomous Vehicles at Unsignalized Intersection](https://mlsanity.com/paper/2106.04561/) (2021)
- [A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios](https://mlsanity.com/paper/2109.10557/) (2021)
- [Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning](https://mlsanity.com/paper/2102.06765/) (2021)
- [Safe, Efficient, Comfort, and Energy-saving Automated Driving through Roundabout Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2306.11465/) (2023)
- [Pedestrian Collision Avoidance for Autonomous Vehicles at Unsignalized Intersection Using Deep Q-Network](https://mlsanity.com/paper/2105.00153/) (2021)
- [Applying Neural Monte Carlo Tree Search to Unsignalized Multi-intersection Scheduling for Autonomous Vehicles](https://mlsanity.com/paper/2410.18786/) (2024)
- [An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios](https://mlsanity.com/paper/2412.08562/) (2024)
- [Goal-conditioned Hierarchical Reinforcement Learning for Sample-efficient and Safe Autonomous Driving at Intersections](https://mlsanity.com/paper/2506.16336/) (2025)

## Abstract (arXiv)

This paper concerns automated vehicles negotiating with other vehicles, typically human driven, in crossings with the goal to find a decision algorithm by learning typical behaviors of other vehicles. The vehicle observes distance and speed of vehicles on the intersecting road and use a policy that adapts its speed along its pre-defined trajectory to pass the crossing efficiently. Deep Q-learning is used on simulated traffic with different predefined driver behaviors and intentions. The results show a policy that is able to cross the intersection avoiding collision with other vehicles 98% of the time, while at the same time not being too passive. Moreover, inferring information over time is important to distinguish between different intentions and is shown by comparing the collision rate between a Deep Recurrent Q-Network at 0.85% and a Deep Q-learning at 1.75%.