LOID: Lane Occlusion Inpainting and Detection for Enhanced Autonomous Driving Systems

Engineering notes

This method achieves a 12% improvement over a number of SOTA models on the CULanes dataset.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Accurate lane segmentation 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 conventional methods. The first approach aug-Segment improves conventional lane detection models by augmenting the training dataset (e.g. 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. Additionally, we present a second approach, LOID: Lane Occlusion Inpainting and Detection, designed to address the issue more comprehensively and with improved robustness. LOID introduces an advanced lane segmentation network that uses an image processing pipeline to identify and mask occluded regions. An inpainting model is then applied to reconstruct the road environment in the occluded areas. The enhanced image is then processed by a lane detection algorithm, resulting in a 20% and 24% improvement over several SOTA models on the BDDK100 and CULanes datasets respectively, highlighting the effectiveness of the proposed approach.

Links and sources

• Official / arXiv page
• PDF from original source

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