Wheel Tracking and Vehicle Identification Using Road-Embedded Sensors

IEEE Access, vol. 13, pp. 183330–183348

Abstract

The growing volume of freight traffic and decreasing infrastructure availability due to maintenance emphasize the need for smart road systems such as digital twins to support data-driven road asset management. In this context, we present a framework for tracking individual vehicle wheels and assigning them to their corresponding vehicles using only road-embedded sensors. The approach ultimately enables high-resolution mapping of traffic-induced loads, as required for digital twins. The method relies on timestamps and positions of distributed sensor detections and proceeds in three steps: clustering sensor signals, reconstructing wheel trajectories via binary optimization, and rule-based vehicle identification based on geometric and kinematic consistency. The method targets key characteristics of real-world traffic data, including unknown vehicle geometries and varying speeds. In the evaluated dataset from a research-grade Weigh-in-Motion (WIM) installation on a German highway, the method achieved 100% precision and recall in wheel tracking and near-perfect results in vehicle identification. The method remains robust under dynamic vehicle behavior and up to 5% artificial data loss, demonstrating its potential for infrastructure monitoring and load-informed digital twins of the road.

Authors 3

1. Moritz P. M. Hagmanns Aachen chair Institute of Highway Engineering

   RWTH Aachen University

   Affiliation as printed

   Chair and Institute of Highway Engineering, RWTH Aachen University, Aachen, Germany

   Institute of Highway Engineering, RWTH Aachen University, Aachen, Germany

2. Adrian Fazekas

   Federal Highway and Transport Research Institute

   Affiliation as printed

   Federal Highway Research Institute, Bergisch Gladbach, Germany

3. Markus Oeser

   Federal Highway and Transport Research Institute

   Affiliation as printed

   Federal Highway Research Institute, Bergisch Gladbach, Germany