Sequential Neural Network Model with Spatial-Temporal Attention Mechanism for Robust Lane Detection Using Multi Continuous Image Frames

Lane detection serves as a fundamental task for automated vehicles and Advanced Driver Assistance Systems. However, current lane detection methods can not deliver the versatility of accurate, robust, and realtime compatible lane detection in real-world scenarios especially under challenging driving scenes. Available vision-based methods in the...

A hybrid spatial–temporal deep learning architecture for lane detection

Accurate and reliable lane detection is vital for the safe performance of lane-keeping assistance and lane departure warning systems. However, under certain challenging circumstances, it is difficult to get satisfactory performance in accurately detecting the lanes from one single image as mostly done in current literature. Since lane...

A Hybrid Spatial-temporal Sequence-to-one Neural Network Model for Lane Detection

Reliable and accurate lane detection is of vital importance for the safe performance of Lane Keeping Assistance and Lane Departure Warning systems. However, under certain challenging peculiar circumstances (e.g., marking degradation, serious vehicle occlusion), it is quite difficult to get satisfactory performance in accurately detecting the...

Lane Detection using Spatio-Temporal Attention

Lane detection represents a fundamental task for automated/autonomous vehicles. Current lane detection methods do not provide the versatility of real-time performance, robustness,and accuracy required for real-world scenarios. The reasons include lack of computing power while being portable and inability to observe the continuity and structure...