Title :
Recognition of Highway Workzones for Reliable Autonomous Driving
Author :
Young-Woo Seo ; Jongho Lee ; Wende Zhang ; Wettergreen, David
Author_Institution :
Robot. Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
Abstract :
In order to be deployed in real-world driving environments, self-driving cars must be able to recognize and respond to exceptional road conditions, such as highway workzones, because such unusual events can alter previously known traffic rules and road geometry. In this paper, we present a set of computer vision methods that recognize, through identification of workzone signs, the bounds of a highway workzone and temporary changes in highway driving environments. Through testing using video data about highway workzones recorded under various weather conditions, our approach was able to perfectly identify the boundaries of workzones and robustly detect a majority of driving condition changes. In addition to these tests, we evaluated, using a mock workzone setup, the usefulness of our workzone recognition systems´ outputs for safe-guarding a self-driving car.
Keywords :
computer vision; image classification; object recognition; road safety; road traffic; road vehicles; traffic engineering computing; video signal processing; computer vision methods; exceptional road conditions; highway workzone recognition; real-world highway driving environments; reliable autonomous driving; self-driving cars; video data; workzone sign identification; Detectors; Image color analysis; Image recognition; Roads; Shape; Vehicles; Classification confidence propagation; highway workzone recognition; kernel-based sign tracking; learning color models for sign detection; sign classification;
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
DOI :
10.1109/TITS.2014.2335535