# ROSA-RL: Uncertainty-Aware Roundabout Optimized Speed Advisory with Reinforcement Learning

arXiv: https://arxiv.org/abs/2606.16558 · HTML: https://mlsanity.com/paper/2606.16558/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 15 Jun 2026.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Multimodal Motion Prediction with Stacked Transformers](https://mlsanity.com/paper/2103.11624/) (2021)
- [Uncertainty-Aware Reinforcement Learning for Collision Avoidance](https://mlsanity.com/paper/1702.01182/) (2017)
- [Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation](https://mlsanity.com/paper/2104.11212/) (2021)
- [openDD: A Large-Scale Roundabout Drone Dataset](https://mlsanity.com/paper/2007.08463/) (2020)
- [Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networks](https://mlsanity.com/paper/1812.09395/) (2018)
- [MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs](https://mlsanity.com/paper/2302.00735/) (2023)
- [Exploring Dynamic Context for Multi-path Trajectory Prediction](https://mlsanity.com/paper/2010.16267/) (2020)
- [Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning](https://mlsanity.com/paper/2205.03212/) (2022)
- [Motion Estimation in Occupancy Grid Maps in Stationary Settings Using Recurrent Neural Networks](https://mlsanity.com/paper/1909.11387/) (2019)
- [Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/2409.16824/) (2024)
- [Learning-enabled multi-modal motion prediction in urban environments](https://mlsanity.com/paper/2304.11764/) (2023)
- [Floating Car Observers in Intelligent Transportation Systems: Detection Modeling and Temporal Insights](https://mlsanity.com/paper/2505.02845/) (2025)
- [Predicted-occupancy grids for vehicle safety applications based on autoencoders and the Random Forest algorithm](https://mlsanity.com/paper/2512.12901/) (2025)
- [ROSA: Roundabout Optimized Speed Advisory with Multi-Agent Trajectory Prediction in Multimodal Traffic](https://mlsanity.com/paper/2602.14780/) (2026)

## Abstract (arXiv)

Roundabouts challenge automated driving in mixed traffic, as heterogeneous and non-deterministic human behavior, unknown driving intentions, and high interaction complexity create uncertainty about whether the conflict zone will be blocked or available at the moment of entry. We present ROSA-RL -- uncertainty-aware Roundabout Optimized Speed Advisory with Reinforcement Learning. It enables safe and efficient roundabout entry for automated and human-driven vehicles in mixed traffic through probabilistic conflict forecasting. A Transformer-based model predicts conflict zone occupancy over a five-second horizon, capturing multi-agent interactions to anticipate upcoming conflicts and available gaps. The prediction outputs encode uncertainty in future motion and intent, and augment the state of a classical RL framework, enabling uncertainty-aware speed coordination. Evaluated in simulations grounded in real-world data, ROSA-RL can effectively handle uncertainty and outperform a comparable model-based baseline, closing the gap to an ideal setting assuming fully known occupancy while improving traffic efficiency and safety. The source code of this work is available under: github.com/urbanAIthi/ROSA-RL.