A Survey on Driver Behavior Analysis From In-Vehicle Cameras

Jiyang Wang, Weiheng Chai, Archana Venkatachalapathy, Kai Liang Tan, Arya Haghighat, Senem Velipasalar, Yaw Adu-Gyamfi, Anuj Sharma

Research output: Contribution to journalReview articlepeer-review

Abstract

Distracted or drowsy driving is unsafe driving behavior responsible for thousands of crashes every year. Studying driver behavior has challenges associated with observing drivers in their natural environment. The naturalistic driving study (NDS) has become the most sought-after approach, since it eliminates the bias of a controlled setup, allowing researchers to understand drivers' behavior in real-world scenarios. Video recordings collected in NDS research are incredibly insightful in identifying driver errors. Computer vision techniques have been used to autonomously analyze video data and classify drivers' behavior. While computer vision scientists focus on image analytics, NDS researchers are interested in the factors impacting driver behavior. This survey paper makes a concerted effort to serve both communities by comprehensively reviewing studies, describing their data collection, computer vision techniques implemented, and performance in classifying driver behavior. The scope is limited to studies employing at least one camera observing the driver inside a vehicle. Based on their objective, papers have been classified as detecting low-level (e.g. head orientation) or high-level (e.g. distraction detection) driver information. Papers have been further classified based on the datasets they employ. In addition to twelve public datasets, many private datasets have also been identified, and their data collection design is discussed to highlight any impact on model performance. Across each task, algorithms employed and their performance are discussed to establish a baseline. A comparison of different frameworks for NDS video data analytics throws light on the existing gaps in the state-of-the-art that can be addressed by future computer vision research.

Original languageEnglish (US)
Pages (from-to)10186-10209
Number of pages24
JournalIEEE Transactions on Intelligent Transportation Systems
Volume23
Issue number8
DOIs
StatePublished - Aug 1 2022

Keywords

  • Driver behavior analysis
  • Driver distraction
  • Drowsiness
  • Face detection
  • Gaze
  • Head orientation
  • Lane change
  • Survey

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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