综述多模态与机器学习在分心驾驶检测中的应用效果
A Review Paper of the Effects of Distinct Modalities and ML Techniques to Distracted Driving Detection
- 系统梳理视觉、听觉、传感等多模态数据的应用方法
- 指出多模态融合比单一模态更有效识别复杂分心行为
- 适合交通安全研究者与智能驾驶系统开发者参考
分心驾驶仍是全球性重大挑战，带来严重的人身与经济损失，亟需更有效的检测与干预策略。尽管以往研究广泛探索了单模态方法，但近期研究表明这些系统在识别复杂分心模式（尤其是认知分心）时表现有限。本综述通过系统分析应用于视觉、感官、听觉及多模态数据的机器学习（ML）与深度学习（DL）技术，按模态、数据可获取性与方法学分类评估相关研究，明确各类方法在准确率上的表现差异，为特定检测目标选择最优方案提供依据。研究揭示了多模态系统相较于单模态的优势，总结了该领域的最新进展。最终，本综述为构建稳健的分心驾驶检测框架提供了重要指导，助力提升道路安全与防控策略。
原文摘要 · Abstract (English)
Distracted driving remains a significant global challenge with severe human and economic repercussions, demanding improved detection and intervention strategies. While previous studies have extensively explored single-modality approaches, recent research indicates that these systems often fall short in identifying complex distraction patterns, particularly cognitive distractions. This systematic review addresses critical gaps by providing a comprehensive analysis of machine learning (ML) and deep learning (DL) techniques applied across various data modalities - visual,, sensory, auditory, and multimodal. By categorizing and evaluating studies based on modality, data accessibility, and methodology, this review clarifies which approaches yield the highest accuracy and are best suited for specific distracted driving detection goals. The findings offer clear guidance on the advantages of multimodal versus single-modal systems and capture the latest advancements in the field. Ultimately, this review contributes valuable insights for developing robust distracted driving detection frameworks, supporting enhanced road safety and mitigation strategies.
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