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Improving Night Time Driving Safety Using Vision-Based Classification Techniques.

Jong-Chih Chien1, Yong-Sheng Chen2, Jiann-Der Lee3,4,5

  • 1Degree Program of Digital Space and Product Design, Kainan University, Taoyuan City 338, Taiwan. jcchien@knu.edu.tw.

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This summary is machine-generated.

This study introduces two advanced driver-assistance system components to enhance nighttime driving safety. One detects driver drowsiness, while the other identifies large vehicles, improving overall road safety.

Keywords:
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Area of Science:

  • Computer Vision
  • Automotive Safety
  • Artificial Intelligence

Background:

  • Nighttime driving presents elevated risks due to drowsy drivers and the presence of large, fast-moving vehicles.
  • Driver distraction, even momentarily, significantly increases the danger when operating near larger vehicles.
  • Existing Advanced Driver Assistance Systems (ADAS) require enhancements for comprehensive nighttime safety.

Purpose of the Study:

  • To develop and evaluate two novel components for vision-based ADAS to improve driver safety during nighttime driving.
  • To create a driver drowsiness detection system.
  • To develop a system for detecting large vehicles on highways at dusk or night.

Main Methods:

  • Driver drowsiness detection utilizes infrared imaging, Multi-Scale Retinex (MSR), and heuristic analysis of eye movements.
  • Large vehicle detection employs a regular video recorder input with a bi-level classifier system, including an MSR-enhanced KAZE-base Bag-of-Features classifier.
  • An improved Multi-Scale Retinex (MSR) algorithm is proposed for contrast enhancement in both components.

Main Results:

  • The driver drowsiness detection component achieved an average accuracy of 94.26% in detecting closed eyes.
  • The large vehicle detection component, using a novel classifier, is designed to minimize false negatives.
  • Experimental results demonstrate the effectiveness of the MSR algorithm and the proposed classifiers.

Conclusions:

  • The proposed ADAS components effectively address critical risks associated with nighttime driving.
  • The driver drowsiness detection system provides reliable alerts for inattentive drivers.
  • The large vehicle detection system enhances situational awareness, particularly around commercial vehicles on highways.