2021/04/23 by Akshay Rangesh, Nachiket Deo, Rangesh, Akshay +7 · 2 citations
Engineering · Psychology · #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human-Automation Interaction and Safety #Robotics (cs.RO) #Sleep and Work-Related Fatigue
paper · pdf · doi:10.48550/arxiv.2104.11489
openalex publication_date 2021/04/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With increasing automation in passenger vehicles, the study of safe and\nsmooth occupant-vehicle interaction and control transitions is key. In this\nstudy, we focus on the development of contextual, semantically meaningful\nrepresentations of the driver state, which can then be used to determine the\nappropriate timing and conditions for transfer of control between driver and\nvehicle. To this end, we conduct a large-scale real-world controlled data study\nwhere participants are instructed to take-over control from an autonomous agent\nunder different driving conditions while engaged in a variety of distracting\nactivities. These take-over events are captured using multiple driver-facing\ncameras, which when labelled result in a dataset of control transitions and\ntheir corresponding take-over times (TOTs). We then develop and train TOT\nmodels that operate sequentially on mid to high-level features produced by\ncomputer vision algorithms operating on different driver-facing camera views.\nThe proposed TOT model produces continuous predictions of take-over times\nwithout delay, and shows promising qualitative and quantitative results in\ncomplex real-world scenarios.\n