Zero-Configuration Alarms: Towards Reducing Distracting Smartphone Interactions while Driving
Authors: 
Sugandh Pargal, Neha Dalmia, Harsh Borse, Bivas Mitra, Sandip ChakrabortyAuthors Info & Claims
Sugandh Pargal, Neha Dalmia, Harsh Borse, Bivas Mitra, Sandip ChakrabortyAuthors Info & ClaimsArticle No.: 29, Pages 1 - 30
Abstract
The rising ubiquity of smartphones for navigation and driver mode, among others, has increased their use significantly among drivers; however, there are growing numbers of road fatalities being reported due to distractions from the phone while driving. In contrast to the existing solutions that use a camera or other communication media on the car or need external setups, this article proposes a solution called ZeCA, where the smartphone itself can identify in real time with zero pre-configurations whether its user is driving while engaging in a high-distraction interaction with the phone. ZeCA runs as a smartphone background service and generates audio-visual alerts when the phone can distract the driver. A thorough evaluation and usability study of ZeCA with 50 different models of vehicles driven by 70 drivers over five countries indicates that the proposed solution can infer distracting smartphone interactions with greater than 80% accuracy and a 70% reduction in smartphone usage during driving.
References
[1]
Joseph Stromberg. 2012. RFID Device Could Jam Your Cell Phone While Your Car Is Moving. Retrieved July 12, 2024 from https://www.smithsonianmag.com/science-nature/new-rfid-device-could-jam-your-cell-phone-while-your-car-is-moving-922232/
[2]
Will T. 2016. PSSUQ (Post-Study System Usability Questionnaire). Retrieved July 12, 2024 from https://uiuxtrend.com/pssuq-post-study-system-usability-questionnaire/
[3]
AutoGluon. 2019. AutoGluon: AutoML for Text, Image, and Tabular Data. Retrieved July 12, 2024 from https://auto.gluon.ai/stable/index.html
[4]
NHTSA. 2019. Distracted Driving. Retrieved July 12, 2024 from https://www.nhtsa.gov/risky-driving/distracted-driving
[5]
Cambridge Mobile Telematics. 2020. Determining Scoring and Reporting Mobile Phone Distraction of a Driver. Retrieved July 12, 2024 from https://www.cmtelematics.com/patent/determining-scoring-and-reporting-mobile-phone-distraction-of-a-driver-patent-no-us-10672249/
[6]
Oludare Isaac Abiodun, Aman Jantan, Abiodun Esther Omolara, Kemi Victoria Dada, Nachaat AbdElatif Mohamed, and Humaira Arshad. 2018. State-of-the-art in artificial neural network applications: A survey. Heliyon 4, 11 (2018), e00938.
[7]
Hari Balakrishnan. 2020. Notifications for ambient dangerous situations. Google Patents. Retrieved July 12, 2024 from https://patents.google.com/patent/US10529236B1/
[8]
Hari Balakrishnan, William Bradley, Samuel Ross, and MaddenJun Geun Park. 2024. Determining, scoring, and reporting mobile phone distraction of a driver. Google Patents. Retrieved July 12, 2024 from https://patents.google.com/patent/US11932257B2/
[9]
Hari Balakrishnan, Lewis David, and GirodIlan Ossin. 2019. System and method for obtaining vehicle telematics data. Google Patents. Retrieved July 12, 2024 from https://patents.google.com/patent/US10440451B2/
[10]
Sandra Bardot, Bradley Rey, Lucas Audette, Kevin Fan, Da-Yuan Huang, Jun Li, Wei Li, and Pourang Irani. 2022. One ring to rule them all: An empirical understanding of day-to-day smartring usage through in-situ diary study. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 3 (2022), 1–20.
[11]
Mariana Belgiu and Lucian Drăguţ. 2016. Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing 114 (2016), 24–31.
[12]
Rafael A. Berri, Alexandre G. Silva, Rafael S. Parpinelli, Elaine Girardi, and Rangel Arthur. 2014. A pattern recognition system for detecting use of mobile phones while driving. In Proceedings of the 2014 International Conference on Computer Vision Theory and Applications (VISAPP’14). 411–418.
[13]
Cheng Bo, Xuesi Jian, Xiang-Yang Li, Xufei Mao, Yu Wang, and Fan Li. 2013. You’re driving and texting: Detecting drivers using personal smart phones by leveraging inertial sensors. In Proceedings of the 19th Annual International Conference on Mobile Computing and Networking. 199–202.
[14]
Patricia M. Brown, Amanda M. George, and Debra Rickwood. 2019. Perceived risk and anticipated regret as factors predicting intentions to text while driving among young adults. Transportation Research Part F: Traffic Psychology and Behaviour 62 (2019), 339–348.
[15]
Ginés Cervantes-Madrid, Ramón Peral-Orts, Nuria Campillo-Davo, and Héctor Campello-Vicente. 2021. Inverse transfer path analysis, a different approach to shorten time in NVH assessments. Applied Acoustics 181 (2021), 108178.
[16]
Chun-Yu Chen and Kang G. Shin. 2022. In-vehicle phone localization for prevention of distracted driving. IEEE Transactions on Mobile Computing. Published Online, January 6, 2022.
[17]
Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785–794.
[18]
Haiming Cheng, Wei Lou, Yanni Yang, Yi-Pu Chen, and Xinyu Zhang. 2023. TwinkleTwinkle: Interacting with your smart devices by eye blink. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, 2 (2023), 1–30.
[19]
Hyojin Chin, Hengameh Zabihi, Sangkeun Park, Mun Yong Yi, and Uichin Lee. 2017. WatchOut: Facilitating safe driving behaviors with social support. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. 2459–2465.
[20]
Hon Chu, Vijay Raman, Jeffrey Shen, Aman Kansal, Victor Bahl, and Romit Roy Choudhury. 2014. I am a smartphone and I know my user is driving. In Proceedings of the 2014 6th International Conference on Communication Systems and Networks (COMSNETS’14). 1–8.
[21]
Cambridge Mobile Telematics. 2024. The DriveWell® Platform. Retrieved July 12, 2024 from https://www.cmtelematics.com/safe-driving-technology/how-it-works/
[22]
Ronan Collobert, Samy Bengio, and Johnny Mariéthoz. 2002. Torch: A Modular Machine Learning Software Library. Technical Report. IDIAP Publications.
[23]
Henrik Detjen, Stefan Geisler, and Stefan Schneegass. 2021. Driving as side task: Exploring intuitive input modalities for multitasking in automated vehicles. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. 1–6.
[24]
Patrick Ebel, Moritz Berger, Christoph Lingenfelder, and Andreas Vogelsang. 2022. How do drivers self-regulate their secondary task engagements? The effect of driving automation on touchscreen interactions and glance behavior. In Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. 263–273.
[25]
European Commission. 2017. A European Strategy on Cooperative Intelligent TransportSystems, a Milestone Towards Cooperative, Connected and Automated Mobility. European Commission.
[26]
Ahmed Farooq, Tomi Nukarinen, Antti Sand, Hanna Venesvirta, Oleg Spakov, Veikko Surakka, and Roope Raisamo. 2021. Where’s my cellphone: Non-contact based hand-gestures and ultrasound haptic feedback for secondary task interaction while driving. In Proceedings of the 2021 IEEE Sensors Conference. IEEE, 1–4.
[27]
Michael A. Gerber, Ronald Schroeter, Li Xiaomeng, and Mohammed Elhenawy. 2020. Self-interruptions of non-driving related tasks in automated vehicles: Mobile vs head-up display. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–9.
[28]
Pierre Geurts, Damien Ernst, and Louis Wehenkel. 2006. Extremely randomized trees. Machine Learning 63, 1 (2006), 3–42.
[29]
Gongde Guo, Hui Wang, David Bell, Yaxin Bi, and Kieran Greer. 2003. KNN model-based approach in classification. In On the Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE. Lecture Notes in Computer Science, Vol. 2888. Springer, 986–996.
[30]
John T. Hancock and Taghi M. Khoshgoftaar. 2020. CatBoost for big data: An interdisciplinary review. Journal of Big Data 7, 1 (2020), 1–45.
[31]
Honda. 2024. Honda Assist Driver Attention Monitor. Retrieved July 12, 2024 from https://www.hondainfocenter.com/2024/CR-V/Feature-Guide/Interior-Features/Driver-Attention-Monitor/
[32]
Graeme Horsman and Lynne R. Conniss. 2015. Investigating evidence of mobile phone usage by drivers in road traffic accidents. Digital Investigation 12 (2015), S30–S37.
[33]
J. Hou, Xiaofeng Xie, Qian Cai, Zhengjie Deng, Houqun Yang, Hongnian Huang, Xun Wang, Lei Feng, and Yizhen Wang. 2022. Early warning system for drivers’ phone usage with deep learning network. EURASIP Journal on Wireless Communications and Networking 2022 (2022), 42.
[34]
Jeremy Howard and Sylvain Gugger. 2020. Fastai: A layered API for deep learning. Information 11, 2 (2020), 108.
[35]
Jizhou Huang, Haifeng Wang, Shiqiang Ding, and Shaolei Wang. 2022. DuIVA: An intelligent voice assistant for hands-free and eyes-free voice interaction with the Baidu Maps app. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3040–3050.
[36]
Hongbo Jiang, Jingyang Hu, Daibo Liu, Jie Xiong, and Mingjie Cai. 2021. DriverSonar: Fine-grained dangerous driving detection using active sonar. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 3 (2021), 1–22.
[37]
Jingun Jung, Sangyoon Lee, Jiwoo Hong, Eunhye Youn, and Geehyuk Lee. 2020. Voice+Tactile: Augmenting in-vehicle voice user interface with tactile touchpad interaction. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–12.
[38]
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30 (2017), 1–9.
[39]
Inayat Khan, Sanam Rizvi, Shah Khusro, Shaukat Ali, and Tae-Sun Chung. 2021. Analyzing drivers’ distractions due to smartphone usage: Evidence from AutoLog dataset. In Proceedings of the 2021 ACM Mobile Information Systems Conference (MIS’21). 1–14.
[40]
Amith Khandakar, Muhammad E. H. Chowdhury, Rashid Ahmed, Ahmed Dhib, Mohammed Mohammed, Nasser Ahmed M. A. Al-Emadi, and Dave Michelson. 2019. Portable system for monitoring and controlling driver behavior and the use of a mobile phone while driving. Sensors 19, 7 (2019), 1563.
[41]
Rushil Khurana and Mayank Goel. 2020. Eyes on the road: Detecting phone usage by drivers using on-device cameras. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI’20). 1–11.
[42]
Sheila Klauer, Thomas A. Dingus, T. V. Neale, Jeremy Sudweeks, and D. J. Ramsey. 2006. The Impact of Driver Inattention onNear-Crash/Crash Risk: An Analysis Using the 100-Car NaturalisticDriving Study Data. U.S. Department of Transportation.
[43]
Sarita Kumari. 2021. Vibration measurement using accelerometer sensor and fast Fourier transform. Trends in Wireless Communication and Information Security 2021 (2021), 273–280.
[44]
Helene Høgh Larsen, Alexander Nuka Scheel, Toine Bogers, and Birger Larsen. 2020. Hands-free but not eyes-free: A usability evaluation of Siri while driving. In Proceedings of the 2020 Conference on Human Information Interaction and Retrieval. 63–72.
[45]
Ja Young Lee, Madeleine C. Gibson, and John D. Lee. 2016. Error recovery in multitasking while driving. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 5104–5113.
[46]
S. K. Lee and P. R. White. 2000. Application of wavelet analysis to the impact harshness of a vehicle. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 214, 11 (2000), 1331–1338.
[47]
Seong Kyu Leem, Faheem Khan, and Sung Ho Cho. 2017. Vital sign monitoring and mobile phone usage detection using IR-UWB radar for intended use in car crash prevention. Sensors 17, 6 (2017), 1240.
[48]
Yanlin Li. 2022. Building a Cross-Platform Bridging Library for Native Mobile SDKs. Ph. D. Dissertation. Massachusetts Institute of Technology.
[49]
Zhe Liu, Yunkai Gao, James Yang, Xiang Xu, Jianguang Fang, Yuexing Duan, and Chao Ma. 2021. Transfer path analysis and its application to diagnosis for low-frequency transient vibration in the automotive door slamming event. Measurement 183 (2021), 109896.
[50]
Dang-Nhac Lu, Thi-Thu-Trang Ngo, Duc-Nhan Nguyen, Thi-Hau Nguyen, and Ha-Nam Nguyen. 2017. A novel mobile online vehicle status awareness method using smartphone sensors. In Information Science and Applications 2017. Lecture Notes in Electrical Engineering, Vol. 424. Springer, 30–37.
[51]
Shih-Nan Lu, Hsien-Wei Tseng, Yang-Han Lee, Yih-Guang Jan, and Wei-Chen Lee. 2010. Intelligent safety warning and alert system for car driving. Journal of Applied Science and Engineering 13, 4 (2010), 395–404.
[52]
Nikolas Martelaro, Jaime Teevan, and Shamsi T. Iqbal. 2019. An exploration of speech-based productivity support in the car. In Proceedings of the 2019 ACM CHI Conference (CHI’19). 1–12.
[53]
Mary L. McHugh. 2012. Interrater reliability: The kappa statistic. Biochemia Medica 22, 3 (2012), 276–282.
[54]
Mercedes-Benz. 2024. What Is Mercedes-Benz ATTENTION ASSIST®? Retrieved July 12, 2024 from https://www.fjmercedes.com/mercedes-benz-attention-assist/
[55]
Michael Nees. 2021. Driver monitoring systems: Perceived fairness of consequences when distractions are detected. In Proceedings of the 13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. 57–61.
[56]
Changhoon Oh and Joongseek Lee. 2014. Understanding in-car smartphone usage pattern with an un-obfuscated observation. In Extended Abstracts of the 2014 CHI Conference on Human Factors in Computing Systems. 1795–1800.
[57]
Oscar Oviedo-Trespalacios, Verity Truelove, and Mark King. 2020.“It is frustrating to not have control even though I know it’s not legal!”: A mixed-methods investigation on applications to prevent mobile phone use while driving. Accident Analysis & Prevention 137 (2020), 105412.
[58]
Sugandh Pargal, Soumyajit Chatterjee, Utkarsh Sinha, Bivas Mitra, and Sandip Chakraborty. 2022. My mobile knows that I am driving! In-vehicle (relative) blind localization of a smartphone. In Proceedings of the 2022 23rd IEEE International Conference on Mobile Data Management (MDM’22). 494–499.
[59]
Vamsi Paruchuri and Aravind Kumar. 2015. Detecting driver distraction using smartphones. In Proceedings of the 2015 IEEE 29th International Conference on Advanced Information Networking and Applications (AINA’15). 468–475.
[60]
Rob Semmens, Nikolas Martelaro, Pushyami Kaveti, Simon Stent, and Wendy Ju. 2019. Is now a good time? An empirical study of vehicle-driver communication timing. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–12.
[61]
Keshav Seshadri, Felix Juefei-Xu, Dipan K. Pal, Marios Savvides, and Craig P. Thor. 2015. Driver cell phone usage detection on Strategic Highway Research Program (SHRP2) face view videos. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW’15). 35–43.
[62]
H. Abdul Shabeer and R. S. D. Wahidabanu. 2012. Averting mobile phone use while driving and technique to locate the mobile phone used vehicle. Procedia Engineering 30 (2012), 623–630.
[63]
Jaroslaw Sobieszczanski-Sobieski, Srinivas Kodiyalam, and R. Y. Yang. 2001. Optimization of car body under constraints of noise, vibration, and harshness (NVH), and crash. Structural and Multidisciplinary Optimization 22, 4 (2001), 295–306.
[64]
Toyota. 2024. Toyota Teammate™ Advanced Drive FAQs. Retrieved July 12, 2024 from https://www.toyota.com/content/dam/toyotaowners/pdfs/Toyota_Advanced_Drive_FAQ_FINAL.pdf
[65]
Herman Van der Auweraer, Katrien Wyckaert, and Wim Hendricx. 1997. From sound quality to the engineering of solutions for NVH problems: Case studies. Acta Acustica United with Acustica 83, 5 (1997), 796–804.
[66]
Trent Victor, Marco Dozza, Jonas Bärgman, Christian-Nils Boda, Johan Engström, Carol Flannagan, John D. Lee, and Gustav Markkula. 2015. Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk. Technical Report. National Academies of Sciences, Engineering, and Medicine.
[67]
Crest Volvo Cars. 2017. How Driver Alert Control Works. Retrieved July 12, 2024 from https://www.crestvolvocars.com/blog/2017/march/28/how-driver-alert-control-works.htm
[68]
Johan Wahlström, Isaac Skog, Peter Händel, Bill Bradley, Samuel Madden, and Hari Balakrishnan. 2019. Smartphone placement within vehicles. IEEE Transactions on Intelligent Transportation Systems 21, 2 (2019), 669–679.
[69]
Johan Wahlström, Isaac Skog, Peter Händel, and Arye Nehorai. 2016. IMU-based smartphone-to-vehicle positioning. IEEE Transactions on Intelligent Vehicles 1, 2 (2016), 139–147.
[70]
Dan Wang, Mingtao Pei, and Lan Zhu. 2014. Detecting driver use of mobile phone based on in-car camera. In Proceedings of the 2014 10th International Conference on Computational Intelligence and Security (CIS’14). 148–151.
[71]
Ruxin Wang, Long Huang, and Chen Wang. 2024. Fast detection of handheld phone-distracted driving by sensing the driver’s hand-grip. IEEE Transactions on Vehicular Technology. Early Access, March 12, 2024.
[72]
Jacob O. Wobbrock. 2019. Situationally aware mobile devices for overcoming situational impairments. In Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems. 1–18.
[73]
Priscilla N. Y. Wong, Duncan P. Brumby, Harsha Vardhan Ramesh Babu, and Kota Kobayashi. 2019. Voices in self-driving cars should be assertive to more quickly grab a distracted driver’s attention. In Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. 165–176.
[74]
Priscilla N. Y. Wong, Duncan P. Brumby, Harsha Vardhan Ramesh Babu, and Kota Kobayashi. 2019. “Watch out!” Semi-autonomous vehicles using assertive voices to grab distracted drivers’ attention. In Proceedings of the 2019 CHI Conference Extended Abstracts on Human Factors in Computing Systems. 1–6.
[75]
Jie Yang, Simon Sidhom, Gayathri Chandrasekaran, Tam Vu, Hongbo Liu, Nicolae Cecan, Yingying Chen, Marco Gruteser, and Richard P. Martin. 2011. Detecting driver phone use leveraging car speakers. In Proceedings of the 17th Annual International Conference on Mobile Computing and Networking (MobiCom’11). 97–108.
[76]
Lichao Yang, Kuo Dong, Arkadiusz Jan Dmitruk, James Brighton, and Yifan Zhao. 2019. A dual-cameras-based driver gaze mapping system with an application on non-driving activities monitoring. IEEE Transactions on Intelligent Transportation Systems 21, 10 (2019), 4318–4327.
[77]
Hayrullah Yasar. 2017. Detection of driver’s mobile phone usage. In Proceedings of the 2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM’17). 1–4.
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- Zero-Configuration Alarms: Towards Reducing Distracting Smartphone Interactions while Driving
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Publication History
Published: 16 September 2024
Online AM: 11 July 2024
Accepted: 31 March 2024
Revised: 27 March 2024
Received: 04 December 2023
Published in�ACMJCSS�Volume 2, Issue 3
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