基于深度学习的驾驶员行为识别与预测  

Driver behavior identification and prediction based on deep learning

作　　者：侯方三 Hou Fangsan(Human Resources and Social Security Bureau of Jimo District,Qingdao city,Qingdao 266200,China)

机构地区：[1]青岛市即墨区人力资源和社会保障局,山东青岛266200

出　　处：《汽车知识》2025年第4期124-126,共3页AUTOMOTIVE KNOWLEDGE

摘　　要：深度学习技术在图像处理、时序分析和多模态数据融合上具有优势,能够提高驾驶员行为识别与预测能力。本文探讨其关键方法:卷积神经网络提取视觉特征、循环神经网络及时序行为建模、多模态数据融合提升全面性。针对数据采集难、模型泛化不足、实时性要求高及隐私保护挑战,提出优化数据采集、轻量化模型、边缘计算、强化隐私保护等对策,为智能驾驶与道路安全管理提供支持。Deep learning technology can improve driver behavior recognition and prediction with its advantages in image processing, time-series analysis and multimodal data fusion. This paper discusses the key methods:convolutional neural network to extract visual features, recurrent neural network to model time-series behavior,and multimodal data fusion to enhance comprehensiveness. Aiming at the difficulty of data collection, insufficient model generalization, high real-time requirements and privacy protection challenges, countermeasures such as optimized data collection, lightweight models, edge computing, and enhanced privacy protection are proposed to provide support for intelligent driving and road safety management.

关 键 词：深度学习 驾驶员 行为识别 预测 

分 类 号：U471.1[机械工程—车辆工程]