# Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1705.01196 · HTML: https://mlsanity.com/paper/1705.01196/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2017.

## Built on by (most cited first, 21 of 21)

- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous Driving](https://mlsanity.com/paper/2105.10266/) (2021)
- [State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersections](https://mlsanity.com/paper/2207.04361/) (2022)
- [Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning](https://mlsanity.com/paper/2102.03119/) (2021)
- [OIL: Observational Imitation Learning](https://mlsanity.com/paper/1803.01129/) (2018)
- [Learning to Navigate Intersections with Unsupervised Driver Trait Inference](https://mlsanity.com/paper/2109.06783/) (2021)
- [End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2104.13617/) (2021)
- [Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning](https://mlsanity.com/paper/2102.03127/) (2021)
- [Safe Deep Q-Network for Autonomous Vehicles at Unsignalized Intersection](https://mlsanity.com/paper/2106.04561/) (2021)
- [Formulation and validation of a car-following model based on deep reinforcement learning](https://mlsanity.com/paper/2109.14268/) (2021)
- [Deep Q-Network Based Decision Making for Autonomous Driving](https://mlsanity.com/paper/2303.11634/) (2023)
- [Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning](https://mlsanity.com/paper/2102.06765/) (2021)
- [Safe Inverse Reinforcement Learning via Control Barrier Function](https://mlsanity.com/paper/2212.02753/) (2022)
- [Pedestrian Collision Avoidance for Autonomous Vehicles at Unsignalized Intersection Using Deep Q-Network](https://mlsanity.com/paper/2105.00153/) (2021)
- [Improve Generalization of Driving Policy at Signalized Intersections with Adversarial Learning](https://mlsanity.com/paper/2204.04403/) (2022)
- [High-level Decisions from a Safe Maneuver Catalog with Reinforcement Learning for Safe and Cooperative Automated Merging](https://mlsanity.com/paper/2107.07413/) (2021)
- [Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2205.05248/) (2022)
- [Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery](https://mlsanity.com/paper/2207.11724/) (2022)
- [Review of Deep Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2302.06370/) (2023)
- [Intersection Warning System for Occlusion Risks using Relational Local Dynamic Maps](https://mlsanity.com/paper/2303.07227/) (2023)
- [Communication-Efficient Zeroth-Order Distributed Online Optimization: Algorithm, Theory, and Applications](https://mlsanity.com/paper/2306.05655/) (2023)

## Abstract (arXiv)

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.