汽车技术

L3级自动驾驶工况下驾驶人非驾驶任务轻量级检测算法

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王畅 ¹ , 郭家煜 ¹ , 李朝 ¹ , 张韡 ¹ , 张杨 ²

汽车技术 | 2026,(10): 25-36

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汽车技术 | 2026 , (10) : 25 -36

L3级自动驾驶工况下驾驶人非驾驶任务轻量级检测算法

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王畅¹, 郭家煜¹, 李朝¹, 张韡¹, 张杨²

作者信息

^1．长安大学汽车学院，西安 710064

^2．陕西交通控股集团有限公司宝鸡分公司，宝鸡 721000

通讯作者:

张韡（1973—），男，博士学位，副教授，主要研究方向为道路交通安全，zhweijx@chd.edu.cn。

Lightweight MobileViT-WT Detection Algorithm for Non-Driving Tasks under L3 Conditions

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

出版时间: 2026-10-24 doi: 10.19620/j.cnki.1000-3703.20250493

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摘要

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针对驾驶人非驾驶任务的检测和评估问题，在实车座舱驾驶模拟器中搭建L3级自动驾驶工况，对40名被试者开展了真实非驾驶任务采集试验，设计了移动设备操作、转身取物品等10种非驾驶任务真实试验，构建了相应的真实操作数据集。通过将小波变换卷积融入MobileViT架构，结合主干网络优化、嵌入位置调整及卷积核参数重构，将多尺度处理与增强局部-全局注意力机制进行融合建立了MobileViT-WT模型。测试结果表明，该模型在自建数据集上对非驾驶任务实现了93.85%的分类准确率，泛化性测试集准确率达82.90%，较原模型提高5.15百分点，参数量较原模型减少11%，模型处理图像的推理延迟仅为8.0 ms。研究结果可为L3级自动驾驶的人机接管与安全预警提供优化。

关键词

L3级自动驾驶 / 非驾驶行为分类 / MobileViT网络 / 小波变换卷积

Abstract

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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.

Key words

L3 Autonomous Driving / Non-Driving Behavior Classification / MobileViT Network / Wavelet Transform Convolution

引用本文

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

基金

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陕西省重点研发计划项目(2023-YBGY-035)
陕西省交通运输厅交通科研项目(23-69K)
国家自然科学基金项目(52362050)

参考文献

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2026年第卷第10期

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文章信息

doi: 10.19620/j.cnki.1000-3703.20250493

首发时间：2026-10-29
出版时间：2026-10-24

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出版历史

修回日期：2025-09-01

基金

陕西省重点研发计划项目(2023-YBGY-035)

陕西省交通运输厅交通科研项目(23-69K)

国家自然科学基金项目(52362050)

作者信息

^1．长安大学汽车学院，西安 710064

^2．陕西交通控股集团有限公司宝鸡分公司，宝鸡 721000

通讯作者:

张韡（1973—），男，博士学位，副教授，主要研究方向为道路交通安全，zhweijx@chd.edu.cn。

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https://castjournals.cast.org.cn/joweb/qcjs/CN/10.19620/j.cnki.1000-3703.20250493

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
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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