Eyes on the Road: Detecting Phone Usage by Drivers Using On-Device Cameras
Rushil Khurana, Mayank Goel
Abstract
Using a phone while driving is distracting and dangerous. It increases the accident chances by 400%. Several techniques have been proposed in the past to detect driver distraction due to phone usage. However, such techniques usually require instrumenting the user or the car with custom hardware. While detecting phone usage in the car can be done by using the phone's GPS, it is harder to identify whether the phone is used by the driver or one of the passengers. In this paper, we present a lightweight, software-only solution that uses the phone's camera to observe the car's interior geometry to distinguish phone position and orientation. We then use this information to distinguish between driver and passenger phone use. We collected data in 16 different cars with 33 different users and achieved an overall accuracy of 94% when the phone is held in hand and 92.2% when the phone is docked (≤ 1 sec. delay). With just a software upgrade, this work can enable smartphones to proactively adapt to the user's context in the car and and substantially reduce distracted driving incidents.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 3a54cba1-1500-4e24-8f18-dd4e59f5f0daCited by top-tier papers2
- IMU2Doppler: Cross-Modal Domain Adaptation for Doppler-based Activity Recognition Using IMU DataSejal Bhalla, Mayank Goel, Rushil KhuranaUbiComp 2022 · 42 citations
- Move, Connect, Interact: Introducing a Design Space for Cross-Traffic InteractionAnnika Stampf, Markus Sasalovici, Luca-Maxim Meinhardt, Mark Colley et al.UbiComp 2024 · 5 citations
Related papers
- DriverSonar: Fine-Grained Dangerous Driving Detection Using Active SonarHongbo Jiang, Jingyang Hu, Daibo Liu, Jie Xiong et al.UbiComp 2021 · 31 citations
- Drive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous VehiclesManuel Martin, Alina Roitberg, Monica Haurilet, Matthias Horne et al.ICCV 2019 · 235 citations
- ReflecTouch: Detecting Grasp Posture of Smartphone Using Corneal Reflection ImagesXiang Zhang, Kaori Ikematsu, Kunihiro Kato, Yuta SugiuraCHI 2022 · 15 citations
- Evaluating In-Car Tasks' Distraction Effects with Drive-In LabTuomo Kujala, Abhishek SarkarCHI 2025 · 3 citations
- HandyTrak: Recognizing the Holding Hand on a Commodity Smartphone from Body Silhouette ImagesHyunchul Lim, David Lin, Jessica Tweneboah, Cheng ZhangUIST 2021 · 5 citations
