Recognition of Highway Workzones for Reliable Autonomous Driving

IEEE Transactions on Intelligent Transportation Systems(2015)

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摘要
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.
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关键词
video signal processing,highway workzone recognition,self-driving cars,road vehicles,road safety,traffic engineering computing,exceptional road conditions,sign classification,learning color models for sign detection,workzone sign identification,reliable autonomous driving,classification confidence propagation,image classification,video data,kernel-based sign tracking,object recognition,computer vision,road traffic,real-world highway driving environments,computer vision methods,shape,classification,detectors,machine vision,image recognition
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