# State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersections

arXiv: https://arxiv.org/abs/2207.04361 · HTML: https://mlsanity.com/paper/2207.04361/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 10 Jul 2022.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1805.01956/) (2018)
- [EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning](https://mlsanity.com/paper/1807.08364/) (2018)
- [Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation](https://mlsanity.com/paper/1703.00420/) (2017)
- [Safe Reinforcement Learning on Autonomous Vehicles](https://mlsanity.com/paper/1910.00399/) (2019)
- [Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning](https://mlsanity.com/paper/2103.14666/) (2021)
- [Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning](https://mlsanity.com/paper/1705.01196/) (2017)
- [Reinforcement Learning with Uncertainty Estimation for Tactical Decision-Making in Intersections](https://mlsanity.com/paper/2006.09786/) (2020)
- [Learning When to Drive in Intersections by Combining Reinforcement Learning and Model Predictive Control](https://mlsanity.com/paper/1908.00177/) (2019)
- [Safe Planning for Self-Driving Via Adaptive Constrained ILQR](https://mlsanity.com/paper/2003.02757/) (2020)

## Built on by (most cited first, 16 of 16)

- [CurricuVLM: Towards Safe Autonomous Driving via Personalized Safety-Critical Curriculum Learning with Vision-Language Models](https://mlsanity.com/paper/2502.15119/) (2025)
- [Reward-Driven Automated Curriculum Learning for Interaction-Aware Self-Driving at Unsignalized Intersections](https://mlsanity.com/paper/2403.13674/) (2024)
- [Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions](https://mlsanity.com/paper/2409.13144/) (2024)
- [Continual Driving Policy Optimization with Closed-Loop Individualized Curricula](https://mlsanity.com/paper/2309.14209/) (2023)
- [Dynamic High-Order Control Barrier Functions with Diffuser for Safety-Critical Trajectory Planning at Signal-Free Intersections](https://mlsanity.com/paper/2412.00162/) (2024)
- [LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models](https://mlsanity.com/paper/2501.05057/) (2025)
- [Bilevel Multi-Armed Bandit-Based Hierarchical Reinforcement Learning for Interaction-Aware Self-Driving at Unsignalized Intersections](https://mlsanity.com/paper/2502.03960/) (2025)
- [Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review](https://mlsanity.com/paper/2411.10268/) (2024)
- [Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning](https://mlsanity.com/paper/2505.08264/) (2025)
- [V2X-Lead: LiDAR-based End-to-End Autonomous Driving with Vehicle-to-Everything Communication Integration](https://mlsanity.com/paper/2309.15252/) (2023)
- [Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2504.04691/) (2025)
- [Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving](https://mlsanity.com/paper/2505.06737/) (2025)
- [Model Checking for Reinforcement Learning in Autonomous Driving: One Can Do More Than You Think!](https://mlsanity.com/paper/2411.14375/) (2024)
- [Diverse and Adaptive Behavior Curriculum for Autonomous Driving: A Student-Teacher Framework with Multi-Agent RL](https://mlsanity.com/paper/2507.19146/) (2025)
- [Orchestrate, Generate, Reflect: A VLM-Based Multi-Agent Collaboration Framework for Automated Driving Policy Learning](https://mlsanity.com/paper/2509.17042/) (2025)
- [Lamarck's Driving School: Discovering Autonomous Driving Training Strategies through Evolutionary Competition](https://mlsanity.com/paper/2610.11662/) (2026)

## Abstract (arXiv)

Traversing intersections is a challenging problem for autonomous vehicles, especially when the intersections do not have traffic control. Recently deep reinforcement learning has received massive attention due to its success in dealing with autonomous driving tasks. In this work, we address the problem of traversing unsignalized intersections using a novel curriculum for deep reinforcement learning. The proposed curriculum leads to: 1) A faster training process for the reinforcement learning agent, and 2) Better performance compared to an agent trained without curriculum. Our main contribution is two-fold: 1) Presenting a unique curriculum for training deep reinforcement learning agents, and 2) showing the application of the proposed curriculum for the unsignalized intersection traversal task. The framework expects processed observations of the surroundings from the perception system of the autonomous vehicle. We test our method in the CommonRoad motion planning simulator on T-intersections and four-way intersections.