  Inspired by human driving focus, this research pioneers networks augmented
with Focusing Sampling, Partial Field of View Evaluation, Enhanced FPN
architecture and Directional IoU Loss - targeted innovations addressing
obstacles to precise lane detection for autonomous driving. Experiments
demonstrate our Focusing Sampling strategy, emphasizing vital distant details
unlike uniform approaches, significantly boosts both benchmark and practical
curved/distant lane recognition accuracy essential for safety. While FENetV1
achieves state-of-the-art conventional metric performance via enhancements
isolating perspective-aware contexts mimicking driver vision, FENetV2 proves
most reliable on the proposed Partial Field analysis. Hence we specifically
recommend V2 for practical lane navigation despite fractional degradation on
standard entire-image measures. Future directions include collecting on-road
data and integrating complementary dual frameworks to further breakthroughs
guided by human perception principles. The Code is available at
https://github.com/HanyangZhong/FENet.
