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

Anna-Lena Schlamp, Jeremias Gerner, Klaus Bogenberger, Werner Huber, Stefanie Schmidtner

TL;DR

A system that combines multi-agent trajectory prediction with coordinated speed guidance for multimodal, mixed traffic at roundabouts, which significantly improves vehicle efficiency and safety, with positive effects even on perceived safety from a VRU perspective.

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.

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

TL;DR

A system that combines multi-agent trajectory prediction with coordinated speed guidance for multimodal, mixed traffic at roundabouts, which significantly improves vehicle efficiency and safety, with positive effects even on perceived safety from a VRU perspective.

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.
Paper Structure (15 sections, 2 equations, 1 figure, 4 tables, 1 algorithm)

This paper contains 15 sections, 2 equations, 1 figure, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Coordination at Roundabouts
  3. Trajectory Prediction at Roundabouts
  4. Contributions
  5. Interaction-Aware Multi-Agent Trajectory Prediction
  6. Dataset and Preprocessing
  7. Model Architecture
  8. Autoregressive Prediction
  9. Evaluation Setup
  10. Results
  11. Roundabout Optimized Speed Advisory
  12. Algorithm Description
  13. Evaluation Setup
  14. Results
  15. Discussion and Conclusion

Figures (1)

  • Figure 1: Visualization of the urban roundabout rdb1 from the openDD dataset Breuer2020openDDAL with prioritized VRU crossings. The sample scenario illustrates interaction-aware trajectory prediction by ROSA jointly for all agents, including a cyclist (magenta). Shaded areas highlight a representative crosswalk and entry conflict zone for occupancy evaluation.