This project is concerned with the automatic detection and analysis of work zones (construction zones) in naturalistic roadway images. An underlying motivation is to identify locations that may pose challenges to advanced driver-assistance systems or autonomous vehicle navigation systems. We first present an in-depth characterization of work zone scenes from a custom dataset collected from more than a million miles of naturalistic driving data. Then we describe two ML algorithms based on the ResNet and U-Net architectures. The first approach works in an image classification framework that classifies an image as a work zone scene or non-work zone scene. The second algorithm was developed to identify individual components representing evidence of a work zone (signs, barriers, machines, etc.). These systems achieved an F{0.5} score of 0.951 for the classification task and an F1 score of 0.611 for the segmentation task. We further demonstrate the viability of our proposed models through salience map analysis and ablation studies. To our knowledge, this is the first study to consider the detection of work zones in large-scale naturalistic data. The systems demonstrate potential for real-time detection of construction zones using forward-looking cameras mounted on automobiles.
