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. Code will be made available.

通过引入 Focusing Sampling、Partial Field of View Evaluation、Enhanced FPN architecture 和 Directional IoU Loss 等创新方法，针对自动驾驶中准确定位车道的难题，这项研究使用受人类驾驶专注力启发的网络模型。实验证明，与传统方法相比，我们的 Focusing Sampling 策略能够显著提高基准和曲线 / 远距车道识别的准确性，通过增强对驾驶员视野的模拟，FENetV1 取得了最先进的传统度量性能，而 FENetV2 则在提出的 Partial Field 分析方面的可靠性最高。因此，尽管在标准的整幅图像衡量中有一定的损失，我们特别推荐使用 V2 进行实际车道导航。未来的研究方向包括收集道路数据和整合互补的双重框架，以更进一步地突破，以人类感知原理为指导。代码将提供。