A new approach for camera supported machine learning algorithms based dynamic headlight model's design

Abstract

Traffic accidents continue to be a significant issue in modern society. Accidents usually happen on dark, mountainous, narrow, steep and curved roadways. One of the primary causes of such accidents is the drivers’ weak sight brought on by the headlights of moving vehicles. In this study, a dynamic headlight model was designed using camera supported machine learning algorithms to improve the drivers’ vision during night drive. In this design, the issues of enabling a lighting field supported by image processing programmed with machine learning, dynamic adjustment of the high beam headlights’ LED cells in response to the vehicle approaching from the opposite direction, traffic-sign recognition system, lane-keeping system, and automatic adjustment of headlight angles were addressed. In this direction, a novel dynamic headlight model that will reduce the risk of accidents caused by lighting was presented, and its analyses were performed.

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

“…For instance, a dynamic headlight model was introduced by Yas xar S xahia¨n and Akar. 62 employing camera-supported machine-learning algorithms to enhance drivers' vision during nighttime driving. This design addresses various issues, including establishing a lighting field supported by image processing programed with machine learning.…”

“…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.…”