A Lightweight Digital-Twin-Based Framework for Edge-Assisted Vehicle Tracking and Collision Prediction

2026-03-07

AI Summary

This work addresses the challenge of deploying computationally intensive trajectory prediction models on resource-constrained edge devices, which hinders real-time vehicle tracking and collision warning in intelligent transportation systems. To overcome this limitation, the authors propose a lightweight digital twin framework that eschews complex trajectory prediction networks and instead leverages YOLO-based object detection, combined with an offline road map and a K-D tree index, to efficiently associate vehicles with road segments for motion state estimation and future position prediction. The approach substantially reduces computational overhead, enabling highly responsive collision warnings at the edge. Evaluated in diverse urban simulation scenarios, the method successfully predicts approximately 88% of collision events in advance while providing a reproducible and controllable validation environment.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Computer Vision: Motion & Tracking

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Vehicle tracking, motion estimation, and collision prediction are fundamental components of traffic safety and management in Intelligent Transportation Systems (ITS). Many recent approaches rely on computationally intensive prediction models, which limits their practical deployment on resource-constrained edge devices. This paper presents a lightweight digital-twin-based framework for vehicle tracking and spatiotemporal collision prediction that relies solely on object detection, without requiring complex trajectory prediction networks. The framework is implemented and evaluated in Quanser Interactive Labs (QLabs), a high-fidelity digital twin of an urban traffic environment that enables controlled and repeatable scenario generation. A YOLO-based detector is deployed on simulated edge cameras to localize vehicles and extract frame-level centroid trajectories. Offline path maps are constructed from multiple traversals and indexed using K-D trees to support efficient online association between detected vehicles and road segments. During runtime, consistent vehicle identifiers are maintained, vehicle speed and direction are estimated from the temporal evolution of path indices, and future positions are predicted accordingly. Potential collisions are identified by analyzing both spatial proximity and temporal overlap of predicted future trajectories. Our experimental results across diverse simulated urban scenarios show that the proposed framework predicts approximately 88% of collision events prior to occurrence while maintaining low computational overhead suitable for edge deployment. Rather than introducing a computationally intensive prediction model, this work introduces a lightweight digital-twin-based solution for vehicle tracking and collision prediction, tailored for real-time edge deployment in ITS.

Problem

• vehicle tracking
• collision prediction
• edge computing
• digital twin
• intelligent transportation systems

Innovation

• lightweight digital twin
• edge-assisted vehicle tracking
• collision prediction
• YOLO-based detection
• K-D tree path indexing