Knowledge-infused Distracted Driver Detection

Abstract

(Balappanawar et al., 2024). 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 this http URL results indicate that KiD3 achieves a 13.64% accuracy improvement over the vision-only baseline by incorporating such auxiliary knowledge with visual information.

Related Publications

2024

KIL @ KDD

Towards Infusing Auxiliary Knowledge for Distracted Driver Detection

Ishwar Balappanawar, Ashmit Chamoli, Ruwan Wickramarachchi, Aditya Mishra, and Ponnurangam Kumaraguru

In Fourth Workshop on Knowledge-infused Learning co-located with 30th ACM KDD Conference, Barcelona, Spain, 2024

Cite

@inproceedings{balappanawar2024towards,
  title = {Towards Infusing Auxiliary Knowledge for Distracted Driver Detection},
  author = {Balappanawar, Ishwar and Chamoli, Ashmit and Wickramarachchi, Ruwan and Mishra, Aditya and Kumaraguru, Ponnurangam},
  booktitle = {Fourth Workshop on Knowledge-infused Learning co-located with 30th ACM KDD Conference, Barcelona, Spain},
  year = {2024},
  organization = {ACM},
  url = {https://openreview.net/forum?id=WQdNGavZu3},
}