One of the main factors that contributed to the large advances in autonomous driving is the
advent of deep learning. For safer self-driving vehicles, one of the problems that has yet to …

Lane detection is a fundamental aspect of most current advanced driver assistance systems
(ADASs). A large number of existing results focus on the study of vision-based lane …

Modern road vehicles are employing features of driver assistance systems (DAS) to improve
drivability performance, comfort, and safety. In the future perspective, the advances in this …

Lane detection on the road is the most essential phase of existing advanced driver
assistance systems (ADAS). In the current road scheme, the lanes are parallel tracks which …

Naturalistic driving studies (NDSs) capture large volumes of drive data from multiple sensor
modalities, which are analyzed for critical information about driver behavior and driving …

Decreasing costs of vision sensors and advances in embedded hardware boosted lane
related research–detection, estimation, tracking, etc.–in the past two decades. The interest in …

State-of-the-art lane detection methods use a variety of deep learning techniques for lane
feature extraction and prediction, demonstrating better performance than conventional lane …

Lane detection techniques have been widely studied in the last two decades and applied in
many advance driver assistance systems. However, the development of a robust lane …

Machine learning (ML) algorithms generate a continuous stream of success stories from
various domains and enable many novel applications in safety-critical systems. With the …

Vision-based methods for detecting the dynamic clearance intrusion of trains in
underground tunnels encounter challenges in extracting rails from low-contrast images …