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Related Concept Videos

PD Controller: Design01:26

PD Controller: Design

In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
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Related Experiment Video

Updated: Jul 10, 2026

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
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PILOT-DSM: Risk perception-aligned takeover prompting framework via coverage-gap assessment in conditionally

Yexin Huang1, Lishengsa Yue1, Yongbin Lin1

  • 1College of Transportation, Tongji University, Key Laboratory of Road and Traffic Engineering, Ministry of Education, 4800 Cao'an Road, Jiading District, Shanghai 201804, China.

Accident; Analysis and Prevention
|July 8, 2026
PubMed
Summary

New takeover prompting systems improve driver safety in automated driving by focusing on visual coverage gaps. Programmatic Imitation Learning for Ocular Tracking and Dynamic ScanMatch (PILOT-DSM) enhances driver attention to critical hazards.

Keywords:
AR-HUDDriver gaze modelingProgrammatic imitation learningRisk perceptionScanpath matchingTakeover prompting

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Area of Science:

  • Human-Computer Interaction
  • Automotive Safety
  • Cognitive Psychology

Background:

  • Driver performance in conditionally automated driving degrades due to missed hazards post-takeover request (TOR).
  • Existing prompting strategies are environment-driven, failing to assess driver visual coverage and potentially misdirecting attention.

Purpose of the Study:

  • To introduce Programmatic Imitation Learning for Ocular Tracking and Dynamic ScanMatch (PILOT-DSM), a closed-loop takeover prompting framework.
  • To assess driver hazard coverage and address unattended risks using an augmented reality head-up display (AR-HUD).

Main Methods:

  • PILOT learns expert gaze-transition policies from visual scanning data.
  • Dynamic ScanMatch (DSM) compares driver gaze with expert strategies to identify coverage gaps.
  • An AR-HUD intervenes by highlighting unattended hazards.

Main Results:

  • PILOT-DSM-HUD significantly reduced gaze latency and increased takeover success rates compared to No-HUD.
  • PILOT-DSM-HUD outperformed Static-HUD in planned contrasts, showing descriptive improvements in gaze latency and takeover success.
  • Safety benefits were observed, particularly for secondary and rear-approaching risks.

Conclusions:

  • Coverage-gap-driven prompting enhances takeover safety in automated driving.
  • PILOT-DSM framework effectively directs driver attention to critical, previously unattended hazards.
  • This approach avoids unnecessary cognitive burden while improving driver response to takeover requests.