Investigating a Pressure Sensitive Surface Layer for Vehicle Localization

Roads are one of the most important transportation routes in the world, yet the way we build roads has remained the same for decades. The road system’s structure is virtually unchanged, and there is little use beyond the primary function of load transfer. However, the road could be an essential data source for different applications. This paper presents an algorithm for detecting and tracking vehicles passing over the road surface using real-time load data. We detect individual tires based on local pressure maxima on the surface and track them using a multiple-target tracker. Our algorithm subsequently identifies individual vehicles performing pattern matching with the tracked wheels. We tested the algorithm in the Cyber-Physical Mobility Lab because there is yet to be a system for real-world testing, and cyber-physical labs are more flexible and less expensive than real-world testing. In our test run, we achieved a vehicle detection accuracy and recall of 100% and a localization accuracy of a few centimeters.

I. Introduction

The road is the leading means of transport in the world. In Germany, roads carry 84% of goods and 86% of passenger traffic [1]. Nevertheless, the system structure of roads has remained almost unchanged over the last decades beyond its primary function of load transfer. It is becoming apparent that road durability is declining due to traffic loads. Current research, such as our Transregional Collaborative Research Center 339 in Germany, examines the road system holistically. This research project focuses on the vehicle downwards through the road surface to the lower support structures. Pressure sensors on the road surface will be used to measure the pressure distribution the vehicles apply on the road surface. With this data, we can develop models to understand the road’s load propagation and create a digital shadow of the entire road system. Through this, it will be possible to determine the current stress acting on the roadway based on given load changes and, in the long term, the current damage development in the roadway. In summary, the digital shadow of the roadway makes it possible to understand how vehicles influence the roadway and damage it over time.