We present ROSA -- Roundabout Optimized Speed Advisory -- a system that combines multi-agent trajectory prediction with coordinated speed guidance for multimodal, mixed traffic at roundabouts. Using a Transformer-based model, ROSA jointly predicts the future trajectories of vehicles and Vulnerable Road Users (VRUs) at roundabouts. Trained for single-step prediction and deployed autoregressively, it generates deterministic outputs, enabling actionable speed advisories. Incorporating motion dynamics, the model achieves high accuracy (ADE: 1.29m, FDE: 2.99m at a five-second prediction horizon), surpassing prior work. Adding route intention further improves performance (ADE: 1.10m, FDE: 2.36m), demonstrating the value of connected vehicle data. Based on predicted conflicts with VRUs and circulating vehicles, ROSA provides real-time, proactive speed advisories for approaching and entering the roundabout. Despite prediction uncertainty, ROSA significantly improves vehicle efficiency and safety, with positive effects even on perceived safety from a VRU perspective. The source code of this work is available under: github.com/urbanAIthi/ROSA.

翻译：我们提出ROSA（Roundabout Optimized Speed Advisory）系统，该系统结合多智能体轨迹预测与协调速度引导，适用于环岛中多模式混合交通场景。采用基于Transformer的模型，ROSA联合预测环岛内车辆与弱势道路使用者（VRU）的未来轨迹。通过单步预测训练和自回归部署，该系统生成确定性输出，从而提供可执行的加速建议。结合运动动力学，该模型在五秒预测范围内实现了高精度（ADE：1.29米，FDE：2.99米），超越了先前研究成果。加入路径意图后进一步提升了性能（ADE：1.10米，FDE：2.36米），证明了网联车辆数据的价值。基于预测的与弱势道路使用者及绕行车辆的冲突风险，ROSA为接近和进入环岛的车辆提供实时主动的速度建议。尽管存在预测不确定性，ROSA仍显著提升了车辆效率与安全性，甚至对弱势道路使用者视角下的感知安全性产生积极影响。本工作源代码发布在：github.com/urbanAIthi/ROSA。