ROSA通过预测多模态交通流轨迹，为环形路口车辆提供实时速度建议，提升通行效率与安全。
ROSA: Roundabout Optimized Speed Advisory with Multi-Agent Trajectory Prediction in Multimodal Traffic
- 基于Transformer模型联合预测车辆与行人的未来轨迹
- 五秒预测下平均位移误差1.29米，加入路线意图后降至1.10米
- 适合智能交通系统、自动驾驶决策与城市道路安全优化
我们提出ROSA——环形路口优化速度引导系统，结合多智能体轨迹预测与协同速度指导，应用于多模态混合交通场景。采用基于Transformer的模型，联合预测环形路口中车辆与弱势道路使用者（VRUs）的未来轨迹。模型以单步预测训练并自回归部署，生成确定性输出，支持可执行的速度建议。引入运动动力学建模后，性能显著提升：在五秒预测时长下，平均位移误差（ADE）为1.29米，最终位移误差（FDE）为2.99米，优于已有方法。进一步融合路线意图信息后，性能进一步提升至ADE: 1.10米，FDE: 2.36米，证明了联网车辆数据的价值。基于对行人及环道内车辆的潜在冲突预测，ROSA提供进入环形路口前的实时主动速度建议。尽管存在预测不确定性，该系统仍显著提升了车辆通行效率与安全性，且对弱势道路使用者的感知安全也有积极影响。代码已开源：github.com/urbanAIthi/ROSA。
原文摘要 · Abstract (English)
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.
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