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启发，本研究开创性地构建了融合聚焦采样、局部视野评估、增强型FPN架构及方向性IoU损失的增强网络，旨在解决自动驾驶中车道精准检测的关键障碍。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处细节，显著提升了弯道/远处车道识别精度（这对安全至关重要）。FENetV1通过增强感知场景上下文（模拟驾驶员视觉）实现了传统指标上的最优性能，而FENetV2在提出的局部视野分析中展现出最高可靠性。尽管在标准全图指标上略有下降，我们仍特别推荐V2用于实际车道导航。未来方向包括采集道路数据及整合互补双框架，以基于人类感知原理实现进一步突破。相关代码将开源。