VTD: Visual and Tactile Database for Driver State and Behavior Perception

TL;DR

This paper introduces a multi-modal visual-tactile dataset collected via driving simulation, aimed at improving driver state and behavior perception for autonomous vehicle co-pilot systems, especially under fatigue and distraction conditions.

Contribution

The creation of a comprehensive, synchronized visual-tactile dataset under various driver states for advancing perception algorithms in autonomous driving.

Findings

600 minutes of fatigue detection data from 15 subjects

102 takeover experiments with 17 drivers

Dataset supports development of cross-modal driver behavior perception algorithms

Abstract

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.