ROSA: 多模式交通中基于多智能体轨迹预测的环岛优化速度建议
ROSA: Roundabout Optimized Speed Advisory with Multi-Agent Trajectory Prediction in Multimodal Traffic
AI总结：
提出ROSA系统，结合Transformer多智能体轨迹预测与协调速度引导，提升环岛多模式混合交通的效率与安全，预测精度优于前人工作。
摘要：
我们提出ROSA——环岛优化速度建议——一个结合多智能体轨迹预测与协调速度引导的系统，用于环岛处的多模式混合交通。使用基于Transformer的模型，ROSA联合预测环岛处车辆和弱势道路使用者（VRU）的未来轨迹。该模型针对单步预测训练并自回归部署，生成确定性输出，从而实现可操作的速度建议。结合运动动力学，模型在五秒预测范围内实现了高精度（ADE: 1.29m, FDE: 2.99m），超越了先前工作。添加路线意图进一步提升了性能（ADE: 1.10m, FDE: 2.36m），展示了网联车辆数据的价值。基于与VRU和环岛内车辆的预测冲突，ROSA为接近和进入环岛的车辆提供实时、主动的速度建议。尽管存在预测不确定性，ROSA显著提升了车辆效率和安全性，甚至从VRU视角对感知安全性也有积极影响。本工作的源代码可在以下网址获取：this http URL。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
