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

2026-02-16

AI Summary

This study addresses the safety and efficiency challenges posed by multimodal mixed traffic—including motorized vehicles and vulnerable road users—in roundabouts. The authors propose a real-time decision-making framework that integrates multi-agent trajectory prediction with coordinated speed guidance. Innovatively combining a Transformer-based autoregressive deterministic trajectory prediction model with path intention information, the approach significantly enhances both prediction accuracy and system practicality. Within a 5-second prediction horizon, the model achieves high precision with an Average Displacement Error (ADE) of 1.10 m and a Final Displacement Error (FDE) of 2.36 m. Experimental results demonstrate notable improvements in vehicular throughput, driving safety for entering vehicles, and perceived safety for vulnerable road users.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• roundabout
• multimodal traffic
• trajectory prediction
• speed advisory
• Vulnerable Road Users

Innovation

• multi-agent trajectory prediction
• Transformer-based model
• speed advisory system
• roundabout optimization
• Vulnerable Road Users (VRUs)