Accurate lane detection is essential for effective path planning and lane following in autonomous driving, especially in scenarios with significant occlusion from vehicles and pedestrians. Existing models often struggle under such conditions, leading to unreliable navigation and safety risks. We propose two innovative approaches to enhance lane detection in these challenging environments, each showing notable improvements over current methods. The first approach aug-Segment improves conventional lane detection models by augmenting the training dataset of CULanes with simulated occlusions and training a segmentation model. This method achieves a 12% improvement over a number of SOTA models on the CULanes dataset, demonstrating that enriched training data can better handle occlusions, however, since this model lacked robustness to certain settings, our main contribution is the second approach, LOID Lane Occlusion Inpainting and Detection. LOID introduces an advanced lane detection network that uses an image processing pipeline to identify and mask occlusions. It then employs inpainting models to reconstruct the road environment in the occluded areas. The enhanced image is processed by a lane detection algorithm, resulting in a 20% & 24% improvement over several SOTA models on the BDDK100 and CULanes datasets respectively, highlighting the effectiveness of this novel technique.

翻译：在自动驾驶中，精确的车道线检测对于有效的路径规划和车道保持至关重要，尤其是在存在车辆和行人严重遮挡的场景下。现有模型在此类条件下往往表现不佳，导致导航不可靠并带来安全风险。我们提出了两种创新方法来增强在这些挑战性环境中的车道线检测，每种方法均较现有方法显示出显著改进。第一种方法aug-Segment通过使用模拟遮挡增强CULanes训练数据集并训练一个分割模型，改进了传统的车道线检测模型。该方法在CULanes数据集上相较于多个SOTA模型实现了12%的性能提升，证明了丰富的训练数据能更好地处理遮挡。然而，由于该模型对某些设置缺乏鲁棒性，我们的主要贡献是第二种方法——LOID车道线遮挡修复与检测。LOID引入了一种先进的车道线检测网络，该网络使用图像处理流程来识别和掩蔽遮挡区域，随后采用修复模型重建被遮挡区域的道路环境。增强后的图像由车道线检测算法进行处理，最终在BDDK100和CULanes数据集上分别较多个SOTA模型实现了20%和24%的性能提升，突显了这项新技术的有效性。