Real-Time Multi-Scale Pedestrian Detection for Driver Assistance Systems

Hemmati, Maryam; Biglari-Abhari, Morteza; Niar, Smaïl; Berber, Stevan

doi:10.1145/3061639.3062308

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…In contrast, nine (9) authors representing 75% utilized the sensor-based headlight beam intensity control approach, 68–73 while three (3) authors representing 25% used the machine-learning-based headlight beam intensity control approach to manage the intelligent headlight beams intensities. 26,74,75 Furthermore, in 2020, the sampled number of authors who conducted a study on intelligent headlight beam intensity control were nine (9), out of which seven (7) authors representing 78% utilized the machine-learning-based headlight beam intensity control approach, 31,62,76–79 , one (1) author representing 11% adopted the sensor-based headlight beam intensity control approach, 42 and the remaining one (1) author representing 11% utilized the fuzzy-logic-based headlight beam intensity control approach. 80 Similarly, in 2021, of the eight (8) sampled authors who conducted a study on intelligent headlight beam intensity control and design of intelligent headlight, five (5) authors representing 62.5% used the sensor-based headlight beam intensity control approach, 5,43,81,82 and the remaining three (3) authors representing 37.5% used the machine-learning-based intensity control approach in the design of the intelligent headlight.…”

Section: Introductionmentioning

confidence: 99%

A review of automotive intelligent and adaptive headlight beams intensity control approaches

Nkrumah,

Cai,

Jafaripournimchahi

2024

Advances in Mechanical Engineering

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The automotive headlight stands out as a critical vehicle component, particularly emphasized during nighttime driving. The high beam, designed for optimal driver visibility on long-distance roads, traditionally relies on manual control by the driver. However, this manual control poses challenges, particularly when the high beam light temporarily blinds oncoming drivers. The resultant dazzle for drivers of opposing vehicles is a significant concern. In response to these issues, there is a growing demand for adaptive and intelligent headlights that can autonomously adjust beam intensity. The intelligent headlight system takes on the responsibility of modifying the beam intensities without requiring explicit input from the drivers. This study aims to systematically review various approaches to controlling intelligent headlight beam intensity. The paper identifies four prominent approaches to intelligent headlight beam intensity control, recognized as widely used techniques. Furthermore, the study uncovers intriguing connections between some of these intensity control approaches. A survey on utilization rates indicates that sensor-based and machine learning (ML)-based intensity control approaches are the most commonly employed methods by automotive headlight designers. The paper concludes by providing insights into the future prospects of intelligent headlight technology, offering guidance for future researchers in this field.

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