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Personalized forward collision warning model with learning from human preferences.

Ning Xie1, Rongjie Yu1, Weili Sun2

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

Accident; Analysis and Prevention
|September 29, 2024
PubMed
Summary
This summary is machine-generated.

Personalized Forward Collision Warning (FCW) models improve vehicle safety by adapting to individual driver risk preferences. This enhances warning synchronization and driver trust, reducing crash risks.

Keywords:
Forward collision warningPersonalized modelRear-end crashRisk perception preferences

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Area of Science:

  • Automotive Safety
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Rear-end crashes are the most common vehicle collisions.
  • Existing Forward Collision Warning (FCW) systems suffer from low response rates, limiting their effectiveness.
  • Personalization of FCW systems is needed to enhance driver trust and safety.

Purpose of the Study:

  • To develop personalized FCW models that align with individual driver risk preferences.
  • To improve the response rate and synchronization of FCW systems with driver behavior.
  • To enhance driver trust in automated safety systems.

Main Methods:

  • A warning feedback index was developed to quantify discrepancies between driver risk perception and FCW models.
  • Individual driver risk perception preferences were characterized using reward models.
  • A benchmark FCW model was fine-tuned using the Proximal Policy Optimization (PPO) algorithm guided by personalized reward models.
  • Empirical analysis utilized 95,814 warning fragments from 74 drivers.

Main Results:

  • The precision of pseudo warning results generated by personalized models increased from 53.5% to 78.2%.
  • The average time difference between FCW warnings and driver braking behavior decreased from 2.4 seconds to 1.6 seconds.
  • A higher synchronization level was achieved between personalized FCW models and individual driver risk perception.

Conclusions:

  • Personalized FCW models significantly improve warning accuracy and timeliness.
  • Enhanced synchronization between FCW systems and drivers boosts driver trust and system effectiveness.
  • This approach offers a promising direction for developing more effective and trustworthy vehicle safety systems.