Research on the optimization of local lane segmentation in BEV based on dynamic serpentine convolution

Yinzhou Dong; Ning Li; Chao Wang

doi:10.1117/12.3026331

9 February 2024 Research on the optimization of local lane segmentation in BEV based on dynamic serpentine convolution

Yinzhou Dong, Ning Li, Chao Wang

Proceedings Volume 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023); 130730F (2024) https://doi.org/10.1117/12.3026331
Event: Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 2023, Changsha, China

Abstract

BEV high-definition maps play a crucial role in autonomous driving and navigation systems, where their segmentation accuracy directly affects system performance and safety. Traditional feature extraction networks, when dealing with complex BEV maps, are often limited by their fixed kernel sizes and shapes, leading to insufficient accuracy in critical tasks such as lane segmentation. This paper improves and proposes an A-HDN framework for high-definition segmentation in BEV, observing the slender and continuous features of linear structures and introducing a Dynamic Serpentine Convolution network (DSConv). This network can flexibly conform to the lane structures in BEV and learn features, while also staying close to the target structure under constraints, thus better learning the features. Additionally, a Ghost module is introduced, which allows the learning network to better preserve features without affecting model performance. Finally, experiments show that this algorithm has increased the map segmentation precision by 1.6 IoU and improved directional detection by 0.8 mAP.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yinzhou Dong, Ning Li, and Chao Wang "Research on the optimization of local lane segmentation in BEV based on dynamic serpentine convolution", Proc. SPIE 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 130730F (9 February 2024); https://doi.org/10.1117/12.3026331

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KEYWORDS

Convolution

Roads

Image segmentation

Performance modeling

Semantics

Autonomous driving

Systems modeling

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