Schlamp, Anna-Lena (Technische Hochschule Ingolstadt), Gerner, Jeremias (Techische Hochschule Ingolstadt), Bogenberger, Klaus (Technical University of Munich), Werner Huber, Werner (Technische Hochschule Ingolstadt), Schmidtner, Stefanie (Technische Hochschule Ingolstadt)

ROSA: Roundabout Optimized Speed Advisory with Multi-Agent Trajectory Prediction in Multimodal Traffic

Scheduled for presentation during the Regular Session "S09c-Optimization for Multimodal and On-Demand Urban Mobility Systems" (WE-LA-T9), Wednesday, November 19, 2025, 16:20−16:40, Coolangata 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

Abstract

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.