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.

翻译：在混合交通中，环岛对自动驾驶构成挑战，因为异质且非确定的人类行为、未知驾驶意图以及高度交互复杂性，导致在驶入冲突区域时无法确定该区域是否会被阻塞或空闲。我们提出ROSA-RL——一种基于强化学习且具有不确定性感知能力的环岛优化速度建议方法。该方法通过概率冲突预测，使自动驾驶车辆和人类驾驶车辆在混合交通中实现安全高效的环岛驶入。基于Transformer的模型可预测五秒时间范围内的冲突区域占用情况，捕捉多智能体交互以预判即将到来的冲突和可利用间隙。预测输出编码了未来运动与意图的不确定性，并增强经典强化学习框架的状态空间，从而实现不确定性感知的速度协调。在实际数据驱动的仿真评估中，ROSA-RL能有效处理不确定性，并优于同类基于模型的基准方法，在提升交通效率和安全性的同时，缩小了与假设完全已知占用情况的理想场景之间的差距。本工作的源代码可在以下地址获取：github.com/urbanAIthi/ROSA-RL。