HammerDrive: A Task-Aware Driving Visual Attention Model

The authors introduce HammerDrive, a novel architecture for task-aware visual attention prediction in driving. The proposed architecture is learnable from data and can reliably infer the current focus of attention of the driver in real-time, while only requiring limited and easy-to-access telemetry data from the vehicle. They build the proposed architecture on two core concepts: 1) driving can be modeled as a collection of sub-tasks (maneuvers), and 2) each sub-task affects the way a driver allocates visual attention resources, i.e., their eye gaze fixation. HammerDrive comprises two networks: a hierarchical monitoring network of forward-inverse model pairs for sub-task recognition and an ensemble network of task-dependent convolutional neural network modules for visual attention modeling. They assess the ability of HammerDrive to infer driver visual attention on data they collected from 20 experienced drivers in a virtual reality-based driving simulator experiment. The authors evaluate the accuracy of their monitoring network for sub-task recognition and show that it is an effective and light-weight network for reliable real-time tracking of driving maneuvers with above 90% accuracy. Their results show that HammerDrive outperforms a comparable state-of-the-art deep learning model for visual attention prediction on numerous metrics with ~13% improvement for both Kullback-Leibler divergence and similarity, and demonstrate that task-awareness is beneficial for driver visual attention prediction.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2021.3055120
• Record URL:
  https://ieeexplore.ieee.org/document/9351808
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Amadori, Pierluigi Vito
  • Fischer, Tobias
  • Demiris, Yiannis
• Publication Date: 2022-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 5573-5585
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 6
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Advanced driver information systems; Artificial intelligence; Computer architecture; Driving simulators; Neural networks; Predictive models; Visualization
• Subject Areas: Data and Information Technology; Highways;

Filing Info

• Accession Number: 01852104
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 21 2022 11:30AM