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Driver attention prediction based on convolution and transformers

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Abstract

In recent years, studying how drivers allocate their attention while driving is critical in achieving human-like cognitive ability for autonomous vehicles. And it has been an active topic in the community of human–machine augmented intelligence for self-driving. However, existing state-of-the-art methods for driver attention prediction are mainly built upon convolutional neural network (CNN) with local receptive field which has a limitation to capture the long-range dependencies. In this work, we propose a novel Attention prediction method based on CNN and Transformer which is termed as ACT-Net. In particular, CNN and Transformer are combined as a block which is further stacked to form the deep model. Through this design, both local and long-range dependencies are captured that both are crucial for driver attention prediction. Exhaustive comparison experiments over other state-of-the-art techniques conducted on widely used dataset of BDD-A and private collected data on BDD-X validate the effectiveness of the proposed ACT-Net.

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Acknowledgements

Project supported by the National Key R&D Program of China (No.2020YFB1600400), National Natural Science Foundation of China (No.61806198), the Key Research and Development Program of Guangzhou (No.202007050002), Shenzhen Science and Technology Program (Grant No.RCBS20200714114920272).

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Authors and Affiliations

  1. Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, 518107, China

    Chao Gou & Yuchen Zhou

  2. School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou, 510275, China

    Chao Gou & Yuchen Zhou

  3. Zhuhai Campus of Sun Yat-sen University, Sun Yat-sen University, Zhuhai, 519082, China

    Dan Li

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  1. Chao Gou
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  2. Yuchen Zhou
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Correspondence to Dan Li.

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Cite this article

Gou, C., Zhou, Y. & Li, D. Driver attention prediction based on convolution and transformers. J Supercomput 78, 8268–8284 (2022). https://doi.org/10.1007/s11227-021-04151-2

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