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Curve-based lane estimation model with lightweight attention mechanism

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Abstract

One of the primary responsibilities of an autonomous driving system is lane detecting. We propose modeling lane markings using Catmull-Rom curves, as opposed to segmentation-based approaches and point detection-based methods, which input images captured by monocular cameras and output lane markings represented by Catmull-Rom segments. The Catmull-Rom segment fits the lane lines more closely than the cubic polynomial and is stable and simple to construct (tested on the TuSimple dataset). We also suggest a spatial attention module to take advantage of lane lines’ nearly vertical distribution. Our suggested approach strikes a balance between accuracy and real-time. In the Tusimple dataset, our model’s accuracy is 0.45% more accurate than the cubic polynomial model (PolyLaneNet). These results demonstrated the suitability of our approach for the lane detecting task.

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This article uses two publicly available datasets: TuSimple dataset and CULane dataset.

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Funding

(1) National Key Research and Development Program of China(2017YFB0102500). (2) National Natural Science Foundation of China (62172186)

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Author notes

  1. Jindong Zhang and Haoting Zhong have contributed equally to this work.

Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Qianjin Street, Changchun, 130012, Jilin, China

    Jindong Zhang & Haoting Zhong

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  1. Jindong Zhang
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Contributions

(1) Jindong Zhang is responsible for writing the paper and providing the experimental platform. (2) Haoting Zhong was responsible for refining the method and designing the experiments.

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Correspondence to Jindong Zhang.

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Zhang, J., Zhong, H. Curve-based lane estimation model with lightweight attention mechanism. SIViP 17, 2637–2643 (2023). https://doi.org/10.1007/s11760-022-02480-0

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