Roundabout Dilemma Zone Data Mining and Forecasting with Trajectory Prediction and Graph Neural Networks

TL;DR

This paper introduces a graph neural network-based system for predicting dilemma zone events at traffic roundabouts, aiming to improve safety for autonomous and manual vehicles through trajectory forecasting.

Contribution

It presents a novel modular, graph-structured recurrent model that forecasts agent trajectories and predicts dilemma zone events using heterogeneous data, including semantic maps.

Findings

High precision in dilemma zone forecasting (false positive rate of 0.1)

Effective trajectory prediction for diverse agents at roundabouts

Advances in safety assurance for autonomous vehicle navigation

Abstract

Traffic roundabouts, as complex and critical road scenarios, pose significant safety challenges for autonomous vehicles. In particular, the encounter of a vehicle with a dilemma zone (DZ) at a roundabout intersection is a pivotal concern. This paper presents an automated system that leverages trajectory forecasting to predict DZ events, specifically at traffic roundabouts. Our system aims to enhance safety standards in both autonomous and manual transportation. The core of our approach is a modular, graph-structured recurrent model that forecasts the trajectories of diverse agents, taking into account agent dynamics and integrating heterogeneous data, such as semantic maps. This model, based on graph neural networks, aids in predicting DZ events and enhances traffic management decision-making. We evaluated our system using a real-world dataset of traffic roundabout intersections. Our experimental results demonstrate that our dilemma forecasting system achieves a high precision with a low false positive rate of 0.1. This research represents an advancement in roundabout DZ data mining and forecasting, contributing to the assurance of intersection safety in the era of autonomous vehicles.