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.

翻译：受人类驾驶注意力机制的启发，本研究首创性地引入了聚焦采样、部分视野评估、增强型FPN架构及方向性IoU损失等创新方法，针对自动驾驶中精确车道检测的障碍提出了解决方案。实验表明，与均匀采样方法不同，我们的聚焦采样策略强调关键远距离细节，显著提升了基准测试及实际场景中弯曲/远距离车道的识别精度，这对行车安全至关重要。FENetV1通过增强感知视角上下文（模拟驾驶员视觉）实现了传统指标上的最优性能，而FENetV2在提出的部分视野分析中展现出最高可靠性。因此，尽管在标准全图指标上略有下降，我们特别推荐V2用于实际车道导航。未来方向包括采集道路数据并整合互补的双框架，以突破人类感知原理引导的进一步研究。相关代码将予以公开。