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<p>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 <i>aug-Segment</i> 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月24日

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  • Aayush Agrawal,
  • Ashmitha Jaysi Sivakumar,
  • Ibrahim Kaif,
  • Chayan Banerjee

摘要

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.