Improving Lane Detection Performance for Autonomous Vehicle Integrating Camera with Dual Light Sensors

Abstract

Automotive companies have studied the development of lane support systems in order to secure the Euro New Car Assessment Program (NCAP)’s high score. A front camera module is applied with safety assistance systems in an intelligent vehicle. However, the front camera module has limitations in terms of backlight conditions, entering or exiting tunnels, and night driving because of lower image quality. In this paper, we propose an integrated camera with dual light sensor for improving lane detection performance under the worst conditions. We include a new algorithm to enhance image data quality and improve edge detection and lane tracking using illumination information. We evaluate the tests under various conditions on a real road. These tests are performed on 728 km of road (under various external situations and lane types) for false alarm rates. The experimental results show that the system is promising in terms of reliability, enhancement, and improvements.

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

“…Other methods explore techniques such as ridge detectors [ 120 ], LSTM networks [ 121 ], and multitask attention networks [ 122 ] to enhance lane detection accuracy in various challenging scenarios. Additionally, some papers integrate multiple sensor data [ 123 , 124 , 125 , 126 ] or use specific sensors like radar [ 127 ] and light photometry systems [ 128 ] to achieve more robust and accurate lane detection for autonomous vehicles. These research contributions provide valuable insights into the development of advanced lane detection systems for safer and more reliable autonomous driving applications.…”

“…Chen and Xiang [ 125 ] incorporated pre-aligned spatial–temporal attention for lane mark detection. Nie et al [ 126 ] integrated a camera with dual light sensors to improve lane-detection performance in autonomous vehicles. These studies collectively present diverse and effective methodologies, contributing to the advancement of lane-detection systems in autonomous driving and intelligent vehicle applications.…”