Lane Detection
Lane detection, a crucial component of autonomous driving systems, aims to accurately identify and track lane markings on roads for safe navigation. Current research emphasizes improving robustness against challenging conditions like occlusions, shadows, and varying weather, often employing deep learning models such as transformers and convolutional neural networks, sometimes enhanced with techniques like knowledge distillation or attention mechanisms. These advancements are vital for enhancing the safety and reliability of autonomous vehicles and contribute significantly to the broader field of computer vision by pushing the boundaries of real-time, accurate object recognition in complex, dynamic environments.
Papers
You Only Look at Once for Real-time and Generic Multi-Task
Jiayuan Wang, Q. M. Jonathan Wu, Ning Zhang
[Re] CLRNet: Cross Layer Refinement Network for Lane Detection
Viswesh N, Kaushal Jadhav, Avi Amalanshu, Bratin Mondal, Sabaris Waran, Om Sadhwani, Apoorv Kumar, Debashish Chakravarty
Elastic Interaction Energy-Informed Real-Time Traffic Scene Perception
Yaxin Feng, Yuan Lan, Luchan Zhang, Guoqing Liu, Yang Xiang