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.

翻译：受人类驾驶注意力机制的启发，本研究首次提出基于聚焦采样、局部视野评估、增强型FPN架构和定向IoU损失函数增强的网络——这些针对性创新旨在解决自动驾驶中精确车道检测的障碍。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景细节，显著提升了安全所需的基准测试及实际场景中弯道/远距车道识别精度。尽管FENetV1通过模拟驾驶员视觉的透视线索感知增强实现了传统指标的先进性能，FENetV2在提出的局部视野分析中展现出最高可靠性。因此，我们特别推荐V2用于实际车道导航，尽管其在标准全图指标上存在轻微下降。未来方向包括采集道路数据并整合互补双框架，以进一步推动基于人类感知原理的突破性进展。代码开源地址：https://github.com/HanyangZhong/FENet。