Yang, LichaoDu, WeixiangZhao, Yifan2023-07-122023-07-122023-07-06Yang L, Du W, Zhao Y. (2023) A lightweight temporal attention-based convolution neural network for driver's activity recognition in edge. Computers and Electrical Engineering, Volume 110, September 2023, Article number 1088610045-7906https://doi.org/10.1016/j.compeleceng.2023.108861https://dspace.lib.cranfield.ac.uk/handle/1826/19964Low inference latency and accurate response to environment changes play a crucial role in the automated driving system, especially in the current Level 3 automated driving. Achieving the rapid and reliable recognition of driver's non-driving related activities (NDRAs) is important for designing an intelligent takeover strategy that ensures a safe and quick control transition. This paper proposes a novel lightweight temporal attention-based convolutional neural network (LTA-CNN) module dedicated to edge computing platforms, specifically for NDRAs recognition. This module effectively learns spatial and temporal representations at a relatively low computational cost. Its superiority has been demonstrated in an NDRA recognition dataset, achieving 81.01% classification accuracy and an 8.37% increase compared to the best result of the efficient network (MobileNet V3) found in the literature. The inference latency has been evaluated to demonstrate its effectiveness in real applications. The latest NVIDIA Jetson AGX Orin could complete one inference in only 63 ms.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Ndra recognitionEfficient CNNAttention mechanismsEdge computingArticle