Autonomous driving is the future of the automotive industry across the globe. Many
challenges must be resolved in designing and developing successful Autonomous Driving …

Inspired by the UNet architecture of semantic image segmentation, we propose a lightweight
UNet using depthwise separable convolutions (DSUNet) for end-to-end learning of lane …

Lane detection plays a vital role in making the idea of the autonomous car a reality.
Traditional lane detection methods need extensive hand-crafted features and post …

Locomotion is basic to all human needs. Modern-day transport has come a long way but still
far away from perfection and all-around safety. Lane Detection is a concept of demarcating …

Automated driving provides a valuable real-time solution in controlling critical risks involved
in driving, attracting researchers in the automation domain. Self-driving vehicles rely …

Lane detection plays a pivotal role in the field of autonomous vehicles and advanced driving
assistant systems (ADAS). Despite advances from image processing to deep learning-based …

As the research and development of autonomous vehicles has become more active, lane
detection technologies for providing road information have become key elements. There are …

For self‐driving vehicles, detecting lane lines in changeable scenarios is a fundamental yet
challenging task. The rise of deep learning in recent years has contributed to the thriving of …

Hundreds of people are injured or killed in road accidents. These accidents are caused by
several intrinsic and extrinsic factors, including the attentiveness of the driver towards the …

Lane detection is necessary for developing intelligent Autonomous Vehicles (AVs). Using
vision-based lane detection is more cost-effective, requiring less operational power. Images …