| Paper Abstract and Keywords |
| Presentation |
2026-02-17 10:20
A driver's gaze prediction model employing deep learning mechanisms
Risa Yoshie, Yuhei Ohsawa (Kindai Univ.), Fumika Nakanishi, Minori Yamataka (ARIC, DENSO CORP.), Takeshi Kohama (Kindai Univ.) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
Although driver assistance systems have made remarkable progress in recent years, it has been pointed out that system interventions can potentially destabilize driving behavior. Consequently, designing systems that account for human-machine collaboration is essential for enhancing safety performance. Conventional gaze prediction models are typically trained under limited conditions, such as accident scenarios, which pose challenges for generalization across a broad range of scenes. In this study, we developed a deep-learning-based gaze prediction model for routine driving scenes that incorporates physiological insights. This model considers both bottom-up factors based on image features and top-down factors such as contextual information. We evaluated the correlation between the probability distribution maps generated from the measured fixation data and the gaze distributions predicted by the model. The results demonstrated a positive correlation, yielding higher values than those of the previous models. These findings suggest that models with structures similar to those of the proposed model enable effective gaze prediction in routine driving scenes. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Deep learning model / Gaze prediction / Driving assistance / Driver / / / / |
| Reference Info. |
ITE Tech. Rep., vol. 50, no. 4, HI2026-3, pp. 12-17, Feb. 2026. |
| Paper # |
HI2026-3 |
| Date of Issue |
2026-02-10 (HI) |
| ISSN |
Online edition: ISSN 2424-1970 |
| Download PDF |
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| Conference Information |
| Committee |
IEICE-HIP HI VRPSY ASJ-H |
| Conference Date |
2026-02-17 - 2026-02-18 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
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| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
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| Paper Information |
| Registration To |
HI |
| Conference Code |
2026-02-HIP-HI-VRPSY-H |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
A driver's gaze prediction model employing deep learning mechanisms |
| Sub Title (in English) |
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| Keyword(1) |
Deep learning model |
| Keyword(2) |
Gaze prediction |
| Keyword(3) |
Driving assistance |
| Keyword(4) |
Driver |
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| 1st Author's Name |
Risa Yoshie |
| 1st Author's Affiliation |
Kindai University (Kindai Univ.) |
| 2nd Author's Name |
Yuhei Ohsawa |
| 2nd Author's Affiliation |
Kindai University (Kindai Univ.) |
| 3rd Author's Name |
Fumika Nakanishi |
| 3rd Author's Affiliation |
Advanced Research and Innovation Center, DENSO CORPORATION (ARIC, DENSO CORP.) |
| 4th Author's Name |
Minori Yamataka |
| 4th Author's Affiliation |
Advanced Research and Innovation Center, DENSO CORPORATION (ARIC, DENSO CORP.) |
| 5th Author's Name |
Takeshi Kohama |
| 5th Author's Affiliation |
Kindai University (Kindai Univ.) |
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| Speaker |
Author-1 |
| Date Time |
2026-02-17 10:20:00 |
| Presentation Time |
20 minutes |
| Registration for |
HI |
| Paper # |
HI2026-3 |
| Volume (vol) |
vol.50 |
| Number (no) |
no.4 |
| Page |
pp.12-17 |
| #Pages |
6 |
| Date of Issue |
2026-02-10 (HI) |