Towards Infusing Auxiliary Knowledge for Distracted Driver Detection

TL;DR

This paper introduces KiD3, a novel framework that enhances distracted driver detection by integrating auxiliary knowledge like scene graphs and driver pose with visual cues, significantly improving accuracy.

Contribution

The paper presents KiD3, a new method that combines semantic scene relations and driver pose information with visual data for better distracted driver detection.

Findings

KiD3 improves detection accuracy by 13.64% over vision-only models.

Incorporating auxiliary knowledge enhances model robustness and generalization.

Unified framework effectively integrates scene graphs, pose, and visual cues.

Abstract

Distracted driving is a leading cause of road accidents globally. Identification of distracted driving involves reliably detecting and classifying various forms of driver distraction (e.g., texting, eating, or using in-car devices) from in-vehicle camera feeds to enhance road safety. This task is challenging due to the need for robust models that can generalize to a diverse set of driver behaviors without requiring extensive annotated datasets. In this paper, we propose KiD3, a novel method for distracted driver detection (DDD) by infusing auxiliary knowledge about semantic relations between entities in a scene and the structural configuration of the driver's pose. Specifically, we construct a unified framework that integrates the scene graphs, and driver pose information with the visual cues in video frames to create a holistic representation of the driver's actions.Our results indicate that KiD3 achieves a 13.64% accuracy improvement over the vision-only baseline by incorporating such auxiliary knowledge with visual information.