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Quantitative Identification of Driver Distraction: A Weakly Supervised Contrastive Learning Approach

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS(2024)

Nanyang Technol Univ | Polytech Hauts Defrance

Cited 7|Views34
Abstract
Accurate recognition of driver distraction is significant for the design of human-machine cooperation driving systems. Existing studies mainly focus on classifying varied distracted driving behaviors, which depend heavily on the scale and quality of datasets and only detect the discrete distraction categories. Therefore, most data-driven approaches have limited capability of recognizing unseen driving activities and cannot provide a reasonable solution for downstream applications. To address these challenges, this paper develops a vision Transformer-enabled weakly supervised contrastive (W-SupCon) learning framework, in which distracted behaviors are quantified by calculating their distances from the normal driving representation set. The Gaussian mixed model (GMM) is employed for the representation clustering, which centralizes the distribution of the normal driving representation set to better identify distracted behaviors. A novel driver behavior dataset and the other three ones are employed for the evaluation, experimental results demonstrate that our proposed approach has more accurate and robust performance than existing methods in the recognition of unknown driver activities. Furthermore, the rationality of distraction levels for different driving behaviors is evaluated through driver skeleton poses. The constructed dataset and demo videos are available at https://yanghh.io/Driver-Distraction-Quantification .
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Behavioral sciences,Vehicles,Feature extraction,Transformers,Training,Decoding,Support vector machines,Driver distraction quantification,weakly supervised contrastive learning,representation clustering
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要点】:本文提出了一种基于视觉Transformer的弱监督对比学习框架,用于量化驾驶员的分心行为,提高了对未见驾驶活动的识别能力。

方法】:通过计算分心行为与正常驾驶表现集的距离来量化分心行为,并使用高斯混合模型(GMM)进行表现集聚类。

实验】:作者使用了一个新构建的驾驶员行为数据集和另外三个数据集进行评估,实验结果表明,所提方法在识别未知驾驶活动方面比现有方法更准确、更稳健。数据集和示例视频可在 https://yanghh.io/Driver-Distraction-Quantification 获取。