ELA - an exit lane assistant for adaptive cruise control and navigation systems

Abstract

This paper presents a new application, the Exit Lane Assistant (ELA), based on a novel and robust vision-based classifier of lane boundary types. Using the knowledge that the exit lane is separated by a special lane boundary type from the other lanes, the intention of leaving the motorway can be recognized by classifying the type of the crossed lane boundaries. Therefore, lane markings are detected at predefined vertical coordinates in the image, so-called scanlines. The detection results, lane marking detected or not detected, are saved into a one-dimensional time-series for each scanline and lane boundary. Based on a Fourier analysis of the set of time series, features are extracted and compared with the theoretic values for the different boundary types using a nearest-neighbor classifier. The lane boundary type is determined fusing the classification results of each scanline based on their confidence. The exit lane ist finally recognized by a rule-based fusion with a digital map.