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Driver Take-Over Behaviour Study Based on Gaze Focalization and Vehicle Data in CARLA Simulator.

Javier Araluce1, Luis M Bergasa1, Manuel Ocaña1

  • 1Electronics Department, University of Alcalá, 28805 Alcalá de Henares, Spain.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

This study analyzed driver behavior during autonomous to manual driving transitions using a CARLA simulator. Findings reveal driver attention patterns and reaction times, crucial for safe human-vehicle interaction in mixed autonomy.

Keywords:
CARLA simulatordriver situation awarenessdriving behaviour studygaze focalizationnon-driving-related tasks (NDRTs)take-over qualitytake-over time

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Area of Science:

  • Human-Computer Interaction
  • Automotive Engineering
  • Cognitive Psychology

Background:

  • The automotive industry is rapidly advancing towards autonomous vehicles, necessitating a study of the human-machine interaction during the transition phases.
  • Current limitations in autonomous technology and legal frameworks require drivers to retake control, making the handover process critical for safety.

Purpose of the Study:

  • To investigate driver behavior during the transition between autonomous and manual driving modes.
  • To analyze driver gaze focalization and reaction times in response to takeover requests within a simulated environment.

Main Methods:

  • Utilized the CARLA simulator for a novel take-over study, tracking driver gaze using a camera-based, non-intrusive method (OpenFace 2.0 toolkit and NARMAX calibration).
  • Fused gaze data with road semantic segmentation to pinpoint driver attention.
  • Employed a dual-computer system with the Robot Operating System (ROS) framework for simulator portability and flexibility.

Main Results:

  • Presented transition analysis results for 20 users across two scenarios using established metrics.
  • Introduced a novel metric to assess driver situation awareness during mode transitions.
  • Quantified driver attention and reaction times during critical takeover events.

Conclusions:

  • The study provides valuable insights into driver behavior during autonomous to manual transitions, essential for designing safer autonomous systems.
  • The developed gaze tracking method offers a cost-effective and non-intrusive approach for driver monitoring in simulators.
  • Findings contribute to understanding human factors in mixed-autonomy driving environments.