Demo: Driver Gaze-Aware Adaptive LiDAR Sensing for Advanced Driver Assistance Systems

Federico Scari; Arkady Zgonnikov; Chen Quan; Nitin Jonathan Myers

doi:10.1109/VTC2025-Spring65109.2025.11174490

Demo: Driver Gaze-Aware Adaptive LiDAR Sensing for Advanced Driver Assistance Systems

Federico Scari, Arkady Zgonnikov, Chen Quan, Nitin Jonathan Myers

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

Abstract

Light detection and ranging (LiDAR) plays a crucial role in machine perception for advanced driver assistance systems. Existing LiDARs, however, do not adapt their sensing strategy to complement driver's perception. We demonstrate a novel LiDAR prototype that dynamically adapts its range and resolution over the field of view, according to real-time driver gaze. Our gaze-aware LiDAR emphasizes scanning peripheral zones the driver may overlook, i.e., critical areas during driving. Our demonstration showcases enhanced perception, highlighting the potential of hybrid human-machine sensing for safer driving.

Original language	English
Title of host publication	Proceedings of the IEEE 101st Vehicular Technology Conference, VTC 2025-Spring 2025
Publisher	IEEE
Number of pages	2
ISBN (Electronic)	979-8-3315-3147-8
DOIs	https://doi.org/10.1109/VTC2025-Spring65109.2025.11174490
Publication status	Published - 2025
Event	101st IEEE Vehicular Technology Conference, VTC 2025-Spring 2025 - Oslo, Norway Duration: 17 Jun 2025 → 20 Jun 2025

Publication series

Name	IEEE Vehicular Technology Conference
ISSN (Print)	1550-2252

Conference

Conference	101st IEEE Vehicular Technology Conference, VTC 2025-Spring 2025
Country/Territory	Norway
City	Oslo
Period	17/06/25 → 20/06/25

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/publishing/publisher-deals
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

ADAS
Eye tracking
Human-Machine Systems
LiDAR

Access to Document

10.1109/VTC2025-Spring65109.2025.11174490

Cite this

@inproceedings{de7f1880f80c4612aa559055ac48be10,

title = "Demo: Driver Gaze-Aware Adaptive LiDAR Sensing for Advanced Driver Assistance Systems",

abstract = "Light detection and ranging (LiDAR) plays a crucial role in machine perception for advanced driver assistance systems. Existing LiDARs, however, do not adapt their sensing strategy to complement driver's perception. We demonstrate a novel LiDAR prototype that dynamically adapts its range and resolution over the field of view, according to real-time driver gaze. Our gaze-aware LiDAR emphasizes scanning peripheral zones the driver may overlook, i.e., critical areas during driving. Our demonstration showcases enhanced perception, highlighting the potential of hybrid human-machine sensing for safer driving.",

keywords = "ADAS, Eye tracking, Human-Machine Systems, LiDAR",

author = "Federico Scari and Arkady Zgonnikov and Chen Quan and Myers, {Nitin Jonathan}",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/publishing/publisher-deals Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 101st IEEE Vehicular Technology Conference, VTC 2025-Spring 2025 ; Conference date: 17-06-2025 Through 20-06-2025",

year = "2025",

doi = "10.1109/VTC2025-Spring65109.2025.11174490",

language = "English",

series = "IEEE Vehicular Technology Conference",

publisher = "IEEE",

booktitle = "Proceedings of the IEEE 101st Vehicular Technology Conference, VTC 2025-Spring 2025",

address = "United States",

}

Scari, F , Zgonnikov, A , Quan, C & Myers, NJ 2025, Demo: Driver Gaze-Aware Adaptive LiDAR Sensing for Advanced Driver Assistance Systems. in Proceedings of the IEEE 101st Vehicular Technology Conference, VTC 2025-Spring 2025 . IEEE Vehicular Technology Conference, IEEE, 101st IEEE Vehicular Technology Conference, VTC 2025-Spring 2025, Oslo, Norway, 17/06/25. https://doi.org/10.1109/VTC2025-Spring65109.2025.11174490

Demo: Driver Gaze-Aware Adaptive LiDAR Sensing for Advanced Driver Assistance Systems. / Scari, Federico ; Zgonnikov, Arkady ; Quan, Chen et al.
Proceedings of the IEEE 101st Vehicular Technology Conference, VTC 2025-Spring 2025 . IEEE, 2025. (IEEE Vehicular Technology Conference).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Demo

T2 - 101st IEEE Vehicular Technology Conference, VTC 2025-Spring 2025

AU - Scari, Federico

AU - Zgonnikov, Arkady

AU - Quan, Chen

AU - Myers, Nitin Jonathan

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/publishing/publisher-deals Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2025

Y1 - 2025

N2 - Light detection and ranging (LiDAR) plays a crucial role in machine perception for advanced driver assistance systems. Existing LiDARs, however, do not adapt their sensing strategy to complement driver's perception. We demonstrate a novel LiDAR prototype that dynamically adapts its range and resolution over the field of view, according to real-time driver gaze. Our gaze-aware LiDAR emphasizes scanning peripheral zones the driver may overlook, i.e., critical areas during driving. Our demonstration showcases enhanced perception, highlighting the potential of hybrid human-machine sensing for safer driving.

AB - Light detection and ranging (LiDAR) plays a crucial role in machine perception for advanced driver assistance systems. Existing LiDARs, however, do not adapt their sensing strategy to complement driver's perception. We demonstrate a novel LiDAR prototype that dynamically adapts its range and resolution over the field of view, according to real-time driver gaze. Our gaze-aware LiDAR emphasizes scanning peripheral zones the driver may overlook, i.e., critical areas during driving. Our demonstration showcases enhanced perception, highlighting the potential of hybrid human-machine sensing for safer driving.

KW - ADAS

KW - Eye tracking

KW - Human-Machine Systems

KW - LiDAR

UR - http://www.scopus.com/inward/record.url?scp=105019047155&partnerID=8YFLogxK

U2 - 10.1109/VTC2025-Spring65109.2025.11174490

DO - 10.1109/VTC2025-Spring65109.2025.11174490

M3 - Conference contribution

AN - SCOPUS:105019047155

T3 - IEEE Vehicular Technology Conference

BT - Proceedings of the IEEE 101st Vehicular Technology Conference, VTC 2025-Spring 2025

PB - IEEE

Y2 - 17 June 2025 through 20 June 2025

ER -

Demo: Driver Gaze-Aware Adaptive LiDAR Sensing for Advanced Driver Assistance Systems

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Embargoed Document

Fingerprint

Cite this