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：本文提出ROSA——环岛优化速度引导系统——该系统将多智能体轨迹预测与协调速度引导相结合，用于处理环岛处的多模态混合交通。ROSA采用基于Transformer的模型，联合预测环岛处车辆与弱势道路使用者（VRU）的未来轨迹。该模型通过单步预测训练并以自回归方式部署，能够生成确定性输出，从而提供可执行的速度引导建议。通过融合运动动力学，模型实现了高精度预测（在五秒预测范围内，ADE：1.29米，FDE：2.99米），超越了先前的研究成果。引入路径意图信息后，性能进一步提升（ADE：1.10米，FDE：2.36米），这证明了网联车辆数据的价值。基于与VRU及环岛内通行车辆的预测冲突，ROSA为接近和进入环岛的车辆提供实时、主动的速度引导建议。尽管存在预测不确定性，ROSA仍能显著提升车辆通行效率与安全性，即使从VRU的视角观察，其对感知安全也具有积极影响。本研究的源代码发布于：github.com/urbanAIthi/ROSA。