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:
- Record URL:
-
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