Work Zone Safety III: Calibration of Safety Notifications through Reinforcement Learning and Eye Tracking

Despite increased regulations, restrictive measures, and devices used for warnings, work zone injuries and fatalities are still observed at highway construction projects with alarms/notifications being ignored. With a vision to reduce the number of injuries and fatalities, Phase 3 of the research team's worker safety project extends the original scope and adds two new main components, including the addition of eye-tracking for identifying worker attention under dangerous situations and a reinforcement learning model used to optimally send alarms to workers to maximize their attentions along with wide deployment and demonstration of the team's previous C2Smart research effort. This phase of the project aims to make sense of the biometric sensor data (i.e., heart rate and pupil movements while workers omit or accept safety notifications) through state-of-the-art reinforcement learning approaches. The outcomes of this research will bring an understanding to the unknowns of worker behaviors on why they decide to ignore/accept notifications for calibration of when and at what frequency to send notifications to workers for a better acceptance rate. Key questions this research answers are, at what conditions workers ignore/response to warnings at work zones? How we can calibrate notification systems for getting responsive actions from workers? What are the modalities, frequencies, and timings of pushing notifications in these calibrated systems? Through wearable sensors, hardware integrated realistic representations of work zones in virtual reality and eye tracking, in this phase of the project the team will widely have pilot demonstrations of the integrated platform to collect worker behavioral and biometric (heart rate, eye-tracking) responses to alarms/warnings/notifications issued under realistic scenarios and modalities of warning mechanisms (e.g., sensory, visual, audial) that were developed in earlier phases of this project, and mine these captured data towards understanding human behaviors in response to modalities of notifications.

Language

  • English

Project

Subject/Index Terms

Filing Info

  • Accession Number: 01768989
  • Record Type: Research project
  • Source Agency: Connected Cities for Smart Mobility towards Accessible and Resilient Transportation Center (C2SMART)
  • Contract Numbers: USDOT 69A3551747124
  • Files: UTC, RIP
  • Created Date: Apr 1 2021 12:36PM