cs.CVMar 7, 2026

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

Authors: Murat Arda Onsu, Poonam Lohan, Burak Kantarci, Aisha Syed, Matthew Andrews, Sean Kennedy

Organizations: School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada. · Nokia Bell Labs, 600 March Road, Kanata, ON K2K 2E6, Canada.

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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.

Figures & tables

SymbolDescription
Q\mathcal{Q}Set of scenarios used for offline path generation (Alg. 1)
Φ(⋅)\Phi(\cdot)YOLO-based Object detection function for input frames (Alg. 1)
IkI_{k}Video frame at index kk during path generation (Alg. 1)
Zp\mathcal{Z}_{p}Aggregated set of vehicle centroids collected for path pp (Alg. 1)
Rp\mathcal{R}_{p}Ordered pixel coordinates of path pp (Alg. 1, 2, 4)
NrN_{r}Number of resampled points used to construct Rp\mathcal{R}_{p} (Alg. 1)
TABLE I: Notation Table
Fig. 1: Components and Architecture of digital Twin of Urban Traffic Environment: Quanser Interactive Labs (QLabs)
Fig. 2: Results on Vehicle Tracking and Collision Prediction in Two Scenarios: Safe Driving (Top) and Collision (Bottom)
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