构建多模态驾驶状态数据集，助力自动驾驶人机共驾安全感知
VTD: Visual and Tactile Database for Driver State and Behavior Perception
- 结合驾驶仿真与信号采集，融合视觉与触觉数据
- 采集15人600分钟疲劳数据，17人102次接管实验
- 适合研究人机交互、驾驶状态识别的团队使用
在自动驾驶领域，人车协同系统受到广泛关注。为解决驾驶员状态与交互行为的主观不确定性这一影响人机共驾安全的关键问题，本文提出一种新型视觉-触觉感知方法。基于驾驶仿真平台，构建了包含多模态数据的综合性数据集，涵盖疲劳与分心场景。实验通过驾驶仿真与信号采集相结合，获取15名受试者共计600分钟的疲劳检测数据，以及17名驾驶员完成的102次接管实验数据。该数据集实现多模态同步，可为推进跨模态驾驶行为感知算法提供可靠资源。
原文摘要 · Abstract (English)
In the domain of autonomous vehicles, the human-vehicle co-pilot system has garnered significant research attention. To address the subjective uncertainties in driver state and interaction behaviors, which are pivotal to the safety of Human-in-the-loop co-driving systems, we introduce a novel visual-tactile perception method. Utilizing a driving simulation platform, a comprehensive dataset has been developed that encompasses multi-modal data under fatigue and distraction conditions. The experimental setup integrates driving simulation with signal acquisition, yielding 600 minutes of fatigue detection data from 15 subjects and 102 takeover experiments with 17 drivers. The dataset, synchronized across modalities, serves as a robust resource for advancing cross-modal driver behavior perception algorithms.
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