A Digital Twin-Based Simulation Framework for Safe Curve Speed Estimation Using Unity

TL;DR

This paper introduces a physics-based digital twin simulation framework in Unity to accurately estimate safe vehicle speeds on horizontal curves, considering vehicle and environmental variations for improved safety and adaptability.

Contribution

It presents a novel digital twin framework that integrates real-world data and vehicle modeling in Unity to estimate safe curve speeds more accurately than traditional methods.

Findings

Simulation estimates align with real-world data.

Framework accounts for vehicle and weather variations.

Provides adaptive safe speed recommendations.

Abstract

Horizontal curves are often associated with roadway crashes due to speed misjudgment and loss of control. With the growing adoption of autonomous and connected vehicles, the accurate estimation of safe speed at curves is becoming increasingly important. The widely used AASHTO design method for safe curve speed estimation relies on an analytical equation based on a simplified point mass model, which often uses conservative parameters to account for vehicular and environmental variations. This paper presents a digital twin-based framework for estimating safe speed at curves using a physics-driven virtual environment developed in the Unity engine. In this framework, a real-world horizontal road curve is selected, and vehicle speed data are collected using a radar gun under various weather conditions. A 3D model of the road curve is constructed in a Unity environment using roadway geometric and elevation data. A parameterized vehicle model is integrated, allowing for variations in mass, acceleration, and center of gravity to reflect different vehicle types and loading scenarios. This simulation identifies the maximum safe speed at which a vehicle can traverse the given curve, providing a more vehicle and environment-specific estimate of the safe operating speed. The study validated that the safe curve speed estimates generated by the simulation were consistent with the real-world speed values observed at a curve. This study demonstrates how a physics-based digital twin can estimate a safer and more adaptive operating speed for vehicles traversing horizontal curves.