HCL Control Strategy for an Adaptive Roadway Lighting Distribution
Source abstract
Abstract
This study aims to develop a human-centric, intelligent lighting control system using adaptive LED lights in roadway lighting, integrated with an imaging luminance meter that uses an IoT sensor driver to detect the brightness of road surfaces. AI image data are collected for luminance and vehicle conditions analyses to adjust the output of the photometric curve. Type-A lenses are designed for R3 dry roads, while Type-B lenses are designed for W1 wet roads, to solve hazards caused by slippery roads, for optimizing safety and for visual clarity for road users. Data are collected for establishing formulae to optimize road lighting. First, the research uses zonal flux analysis to design secondary optical components of LED roadway lighting. Based on the distribution of LED lights and the target photometric curve, the freeform surface calculation model and formula are established, and control points of each curved surface are calculated using an iterative method. The reflection coefficient of a roadway is used to design optical lenses that take into account the illuminance and luminance uniformity to produce photometric curves accordingly. This system monitors roadway luminance in real time, which simulates drivers’ visual experiences and uses the ZigBee protocol to transmit control commands. This optimizes the output of light according to weather and produces quality roadway lighting, providing a safer driving environment.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced optical system design · Advanced Measurement and Detection Methods · Impact of Light on Environment and Health
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical Engineering, National Taiwan University of Science and Technology (NTUST), Taipei City 106335, Taiwan
Department of Civil and Construction Engineering, National Taiwan University of Science and Technology (NTUST), Taipei City 106335, Taiwan
Executive Master of Business Administration, Royal Roads University (RRU), Victoria, BC V9B5Y2, Canada
Department of Civil Engineering, National Central University (NCU), Tao-Yuan 32011, Taiwan
Department of Civil and Environmental Engineering, Hiroshima University, Higashi-Hiroshima City 739-8527, Hiroshima, Japan
References (38)
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