Road surface classification using near infrared spectroscopy

Abstract: Statistics shows that most traffic accidents with fatal outcome can be related to slippery road conditions. The most hazardous road conditions are the ones that are hard for the driver to detect and that appears sudden on the road. A sensor that classify the road condition in front of the vehicle, warning both the driver and the systems in the vehicle that are incorporated to help the driver, like the electronic stability program (ESP), anti-lock brake system (ABS) or the traction control system (TCS), could help to reduce these accidents. There are several prototypes for classification of road conditions available but they are not yet fully functional. In this thesis a method that makes it possible to classify the four distinct road conditions dry asphalt and asphalt covered with water, ice and snow, respectively, with a low probability of wrong classification using three wavelengths is presented. A prototype sensor built on the a technique using two laser diodes and a photo detector is tested in a real environment and compared with laboratory measurements which shows a promising result characterizing dry asphalt and asphalt covered with ice and snow. Both theory and experiments are presented. The most difficult road conditions to classify from each other are water and clear ice for which a method using polarized light is investigated. The investigation shows that using polarized light for illumination and a polarizer as an analyzer for classification of water and ice on asphalt is a more reliable method than using unpolarized light. All three investigations show promising results in developing an actual sensor to reduce fatal accidents in traffic.

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