Title page for ETD etd-06062011-093649

Type of Document Master's Thesis
Author Terry, Travis
Author's Email Address trav17@vt.edu
URN etd-06062011-093649
Title Comparing LED Lighting Systems in the Detection and Color Recognition of Roadway Objects
Degree Master of Engineering
Department Industrial and Systems Engineering
Advisory Committee
Advisor Name Title
Gibbons, Ronald B. Committee Co-Chair
Lockhart, Thurmon E. Committee Co-Chair
Smith-Jackson, Tonya L. Committee Member
  • LED
  • roadway safety
  • night driving
  • lighting
  • color contrast
  • luminance contrast
  • small target visibility
  • color recognition
Date of Defense 2011-05-11
Availability restricted

This study compared two LED luminaires and their abilities to provide detection distance and color recognition distance of potential roadway hazard. Detection distance is regarded as a metric of visibility. Color recognition distance is a metric for comparing the impact of the (Correlated Color Temperature) CCT of each luminaire and their color contrast impact. Mesopic vision, the mode of vision most commonly used for night driving, was considered in this study. Off-axis objects were presented to participants to assess the peripheral abilities of the luminaires. The impacts of luminance and color contrast were addressed in this study. The experiment was performed on the Virginia Smart Road where standard objects of different colors and pedestrians wearing different colors were detected by drivers of a moving vehicle in a controlled environment. The key difference between the two luminaires was their color temperatures (3500K versus 6000K). The results indicated that neither light source provided a significant benefit over the other although significant interactions were found among object color, age, and lighting level. The results indicate that the luminaires provide similar luminance contrast but their color contrasts depend heavily on the color temperature, the object, and the observer. This study followed the protocol developed by the Mesopic Optimisation of Visual Efficiency (MOVE) consortium developed by the CIE for modeling mesopic visual behavior.

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