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1. ROBUST VEHICLE DETECTION AND DISTANCE ESTIMATION UNDER
CHALLENGING LIGHTING CONDITIONS
ABSTRACT
Avoiding high computational costs and calibrationissues involved in stereo-vision-based
algorithms, this paper proposesreal-time monocular-vision-based techniques for simultaneous
vehicle detection and inter-vehicle distance estimation, inwhich the performance and robustness
of the system remaincompetitive, even for highly challenging benchmark datasets. Thispaper
develops a collision warning system by detecting vehiclesahead and, by identifying safety
distances to assist a distracteddriver, prior to occurrence of an imminent crash. We introduce
adaptive global Haar-like features for vehicle detection, tail-lightsegmentation, virtual symmetry
detection, intervehicle distance estimation, as well as an efficient single-sensor multifeature
fusiontechnique to enhance the accuracy and robustness of our algorithm.The proposed
algorithm is able to detect vehicles ahead atboth day or night and also for short- and long-range
distances. Experimentalresults under various weather and lighting conditions(including sunny,
rainy, foggy, or snowy) show that the proposedalgorithm outperforms state-of-the-art algorithms.