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Lightweight MobileViT-WT Detection Algorithm for Non-Driving Tasks under L3 Conditions
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Chang Wang1, Jiayu Guo1, Zhao Li1, Wei Zhang1, Yang Zhang2
Automobile Technology | 2026, (10) : 25 - 36
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Automobile Technology | 2026, (10): 25-36
Lightweight MobileViT-WT Detection Algorithm for Non-Driving Tasks under L3 Conditions
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Chang Wang1, Jiayu Guo1, Zhao Li1, Wei Zhang1, Yang Zhang2
Affiliations
  • 1.School of Automobile, Chang'an University, Xi'an 710064
  • 2.Baoji Branch, Shaanxi Transportation Holding Group Co., Ltd., Baoji 721000
Published: 2026-10-24 doi: 10.19620/j.cnki.1000-3703.20250493
Outline
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Under the L3 level automatic driving condition, the driver is in the state of leaving the driving link. Real time detection of the driver's non-driving task is an important mechanism to ensure the safe operation of the L3 level automatic driving system for the automatic driving system to understand the driver's state and evaluate the driver's emergency ability according to the driver's state. For the detection and evaluation of drivers' non-driving tasks, this study built L3 level automatic driving conditions in the real vehicle cockpit driving simulator, carried out real non-driving task collection tests on 40 subjects, carried out 10 kinds of real non-driving tasks experiments such as mobile device operation and turning around to pick up items, and constructed the corresponding real operation data set. By integrating wavelet transform convolution into MobileViT architecture, combined with backbone network optimization, embedding position adjustment and convolution kernel parameter reconstruction, the MobileViT-WT model is established by integrating multi-scale processing and enhanced local global attention mechanism. The test results show that the model achieves 93.85% classification accuracy for non-driving tasks on the self-built data set, and the accuracy of the generalized test set is 82.90%, which is 5.15% higher than the original model, and the number of parameters is 11% lower than the original model. The reasoning delay of the model processing image is only 8.0 ms. The research results can provide optimization for man-machine takeover and safety early warning of L3 automatic driving.

L3 Autonomous Driving  /  Non-Driving Behavior Classification  /  MobileViT Network  /  Wavelet Transform Convolution
王畅, 郭家煜, 李朝, 张韡, 张杨. L3级自动驾驶工况下驾驶人非驾驶任务轻量级检测算法. 汽车技术, 2026 , (10) : 25 -36 . DOI: 10.19620/j.cnki.1000-3703.20250493
Chang Wang, Jiayu Guo, Zhao Li, Wei Zhang, Yang Zhang. Lightweight MobileViT-WT Detection Algorithm for Non-Driving Tasks under L3 Conditions[J]. Automobile Technology, 2026 , (10) : 25 -36 . DOI: 10.19620/j.cnki.1000-3703.20250493
Year 2026 volume Issue 10
59
11
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doi: 10.19620/j.cnki.1000-3703.20250493
  • Online Date:2026-10-29
  • Published:2026-10-24
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  • Revised:2025-09-01
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    1.School of Automobile, Chang'an University, Xi'an 710064
    2.Baoji Branch, Shaanxi Transportation Holding Group Co., Ltd., Baoji 721000
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表12种不同金属材料的力学参数
科
Family
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Number of
genus
种数
Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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