The role of driver head pose dynamics and instantaneous driving in safety critical events: Application of computer vision in naturalistic driving

Khattak, Zulqarnain H.; Li, Wan; Karnowski, Thomas; Khattak, Asad J.

doi:10.1016/j.aap.2024.107545

Title: The role of driver head pose dynamics and instantaneous driving in safety critical events: Application of computer vision in naturalistic driving

Journal Article · 16 March 2024 · Accident Analysis and Prevention

DOI: https://doi.org/10.1016/j.aap.2024.107545 · OSTI ID:2329577

Khattak, Zulqarnain H. ^[1];

^[2];

^[2]; Khattak, Asad J. ^[3]

Carnegie Mellon Univ., Pittsburgh, PA (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Univ. of Tennessee, Knoxville, TN (United States)

This paper investigates the role of driver behavior especially head pose dynamics in safety–critical events (SCEs). Using a large dataset collected in a naturalistic driving study, this paper analyzes the head pose dynamics and driving behavior in moments leading up to crashes or near-crashes. The study uses advanced computer vision and mixed logit modeling techniques to identify patterns and relationships between drivers’ head pose dynamics and crash involvement. The results suggest that driver-head pose dynamics, especially poses that indicate distraction and movement volatility, are important factors that can contribute to undesirable safety outcomes. Marginal effects show that angular deviation for head pose dynamics indicated by yaw, pitch and roll increase the likelihood of crash intensity by 4.56%, 4.92% and 8.26% respectively. Furthermore, traffic flow and lane changing also contribute to increase in likelihood of crash intensity. These findings provide new insights into pre-crash factors, especially human factors and safety–critical events. The study highlights the importance of considering human factors in designing driver assistance systems and developing safer vehicles. This research contributes by examining naturalistic driving data at the microscopic level with early detection of behaviors that lead to SCEs and provides a basis for future research on automation.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2329577

Journal Information:: Accident Analysis and Prevention, Journal Name: Accident Analysis and Prevention Journal Issue: 1 Vol. 200; ISSN 0001-4575

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (41)

On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals Halton, J. H. Numerische Mathematik, Vol. 2, Issue 1 https://doi.org/10.1007/BF01386213	journal	December 1960
Driver's exposure to distractions in their natural driving environment Stutts, Jane; Feaganes, John; Reinfurt, Donald Accident Analysis & Prevention, Vol. 37, Issue 6 https://doi.org/10.1016/j.aap.2005.06.007	journal	November 2005
Median barrier crash severity: Some new insights Hu, Wen; Donnell, Eric T. Accident Analysis & Prevention, Vol. 42, Issue 6 https://doi.org/10.1016/j.aap.2010.04.009	journal	November 2010
Identification of safety-critical events using kinematic vehicle data and the discrete fourier transform Kluger, Robert; Smith, Brian L.; Park, Hyungjun Accident Analysis & Prevention, Vol. 96 https://doi.org/10.1016/j.aap.2016.08.006	journal	November 2016
Investigation of drivers’ behavior towards speeds using crash data and self-reported questionnaire Hassan, Hany M.; Shawky, Mohamed; Kishta, Mohammad Accident Analysis & Prevention, Vol. 98 https://doi.org/10.1016/j.aap.2016.10.027	journal	January 2017
Young driver risky behaviour and predictors of crash risk in Australia, New Zealand and Colombia: Same but different? Scott-Parker, Bridie; Oviedo-Trespalacios, Oscar Accident Analysis & Prevention, Vol. 99 https://doi.org/10.1016/j.aap.2016.11.001	journal	February 2017
Performance of basic kinematic thresholds in the identification of crash and near-crash events within naturalistic driving data Perez, Miguel A.; Sudweeks, Jeremy D.; Sears, Edie Accident Analysis & Prevention, Vol. 103 https://doi.org/10.1016/j.aap.2017.03.005	journal	June 2017
Effects of truck traffic on crash injury severity on rural highways in Wyoming using Bayesian binary logit models Ahmed, Mohamed M.; Franke, Rebecca; Ksaibati, Khaled Accident Analysis & Prevention, Vol. 117 https://doi.org/10.1016/j.aap.2018.04.011	journal	August 2018
The role of pre-crash driving instability in contributing to crash intensity using naturalistic driving data Arvin, Ramin; Kamrani, Mohsen; Khattak, Asad J. Accident Analysis & Prevention, Vol. 132 https://doi.org/10.1016/j.aap.2019.07.002	journal	November 2019
Investigating the relation between instantaneous driving decisions and safety critical events in naturalistic driving environment Khattak, Zulqarnain H.; Fontaine, Michael D.; Li, Wan Accident Analysis & Prevention, Vol. 156 https://doi.org/10.1016/j.aap.2021.106086	journal	June 2021
Multimodal driver state modeling through unsupervised learning Tavakoli, Arash; Heydarian, Arsalan Accident Analysis & Prevention, Vol. 170 https://doi.org/10.1016/j.aap.2022.106640	journal	June 2022
Effects of naturalistic cell phone conversations on driving performance Rakauskas, Michael E.; Gugerty, Leo J.; Ward, Nicholas J. Journal of Safety Research, Vol. 35, Issue 4 https://doi.org/10.1016/j.jsr.2004.06.003	journal	January 2004
Using SHRP2 naturalistic driving data to examine driver speeding behavior Richard, Christian M.; Lee, Joonbum; Atkins, Randolph Journal of Safety Research, Vol. 73 https://doi.org/10.1016/j.jsr.2020.03.008	journal	June 2020
Effects of environment, vehicle and driver characteristics on risky driving behavior at work zones Weng, Jinxian; Meng, Qiang Safety Science, Vol. 50, Issue 4 https://doi.org/10.1016/j.ssci.2011.12.005	journal	April 2012
Work and non-work-related vehicle crashes: The contribution of risky driving practices Mitchell, R. J.; Bambach, M. R.; Friswell, R. Safety Science, Vol. 68 https://doi.org/10.1016/j.ssci.2014.02.025	journal	October 2014
Sensation seeking, parental bond, and risky driving in adolescence: Some relationships, matter more to girls than boys Smorti, Martina; Guarnieri, Silvia Safety Science, Vol. 70 https://doi.org/10.1016/j.ssci.2014.05.019	journal	December 2014
Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles Khattak, Asad J.; Wali, Behram Transportation Research Part C: Emerging Technologies, Vol. 84 https://doi.org/10.1016/j.trc.2017.08.004	journal	November 2017
Cooperative lane control application for fully connected and automated vehicles at multilane freeways Khattak, Zulqarnain H.; Smith, Brian L.; Park, Hyungjun Transportation Research Part C: Emerging Technologies, Vol. 111 https://doi.org/10.1016/j.trc.2019.11.007	journal	February 2020
Harnessing ambient sensing & naturalistic driving systems to understand links between driving volatility and crash propensity in school zones – A generalized hierarchical mixed logit framework Wali, Behram; Khattak, Asad J. Transportation Research Part C: Emerging Technologies, Vol. 114 https://doi.org/10.1016/j.trc.2020.01.028	journal	May 2020
Driver performance in the moments surrounding a microsleep Boyle, Linda Ng; Tippin, Jon; Paul, Amit Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 11, Issue 2 https://doi.org/10.1016/j.trf.2007.08.001	journal	March 2008
Glance behaviours when using an in-vehicle smart driving aid: A real-world, on-road driving study Birrell, Stewart A.; Fowkes, Mark Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 22 https://doi.org/10.1016/j.trf.2013.11.003	journal	January 2014
Distinguishing patterns in drivers’ visual attention allocation using Hidden Markov Models Muñoz, Mauricio; Reimer, Bryan; Lee, Joonbum Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 43 https://doi.org/10.1016/j.trf.2016.09.015	journal	November 2016
Evaluating the impact of adaptive signal control technology on driver stress and behavior using real-world experimental data Khattak, Zulqarnain H.; Fontaine, Michael D.; Boateng, Richard A. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 58 https://doi.org/10.1016/j.trf.2018.06.006	journal	October 2018
‘Owl’ and ‘Lizard’: patterns of head pose and eye pose in driver gaze classification Fridman, Lex; Lee, Joonbum; Reimer, Bryan IET Computer Vision, Vol. 10, Issue 4 https://doi.org/10.1049/iet-cvi.2015.0296	journal	April 2016
Driver crash risk factors and prevalence evaluation using naturalistic driving data Dingus, Thomas A.; Guo, Feng; Lee, Suzie Proceedings of the National Academy of Sciences, Vol. 113, Issue 10 https://doi.org/10.1073/pnas.1513271113	journal	February 2016
Investigation of hit-and-run crash occurrence and severity using real-time loop detector data and hierarchical Bayesian binary logit model with random effects Xie, Meiquan; Cheng, Wen; Gill, Gurdiljot Singh Traffic Injury Prevention, Vol. 19, Issue 2 https://doi.org/10.1080/15389588.2017.1371302	journal	November 2017
Investment information content in Bollinger Bands? Lento, C.; Gradojevic, N.; Wright, C. S. Applied Financial Economics Letters, Vol. 3, Issue 4 https://doi.org/10.1080/17446540701206576	journal	July 2007
RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild Deng, Jiankang; Guo, Jia; Ververas, Evangelos 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR42600.2020.00525	conference	June 2020
Fine-Grained Head Pose Estimation Without Keypoints Ruiz, Nataniel; Chong, Eunji; Rehg, James M. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) https://doi.org/10.1109/CVPRW.2018.00281	conference	June 2018
Face Recognition Oak Ridge (FaRO): A Framework for Distributed and Scalable Biometrics Applications Bolme, David S.; Srinivas, Nisha; Brogan, Joel 2020 IEEE International Joint Conference on Biometrics (IJCB) https://doi.org/10.1109/IJCB48548.2020.9304933	conference	September 2020
Where is the driver looking: Analysis of head, eye and iris for robust gaze zone estimation Tawari, Ashish; Chen, Kuo Hao; Trivedi, Mohan M. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC) https://doi.org/10.1109/ITSC.2014.6957817	conference	October 2014
Adversarial Attacks and Defense in Deep Reinforcement Learning (DRL)-Based Traffic Signal Controllers Haydari, Ammar; Zhang, Michael; Chuah, Chen-Nee IEEE Open Journal of Intelligent Transportation Systems, Vol. 2 https://doi.org/10.1109/OJITS.2021.3118972	journal	January 2021
A Gaze-Based Driver Distraction Warning System and Its Effect on Visual Behavior Ahlstrom, Christer; Kircher, Katja; Kircher, Albert IEEE Transactions on Intelligent Transportation Systems, Vol. 14, Issue 2 https://doi.org/10.1109/TITS.2013.2247759	journal	June 2013
Driver Gaze Tracking and Eyes Off the Road Detection System Vicente, Francisco; Huang, Zehua; Xiong, Xuehan IEEE Transactions on Intelligent Transportation Systems, Vol. 16, Issue 4 https://doi.org/10.1109/TITS.2015.2396031	journal	August 2015
Detecting Drivers' Mirror-Checking Actions and Its Application to Maneuver and Secondary Task Recognition Li, Nanxiang; Busso, Carlos IEEE Transactions on Intelligent Transportation Systems, Vol. 17, Issue 4 https://doi.org/10.1109/TITS.2015.2493451	journal	April 2016
A Review of Alcohol‐Impaired Driving: The Role of Blood Alcohol Concentration and Complexity of the Driving Task Martin, Teri L.; Solbeck, Patricia A. M.; Mayers, Daryl J. Journal of Forensic Sciences, Vol. 58, Issue 5 https://doi.org/10.1111/1556-4029.12227	journal	July 2013
What Can Be Predicted from Six Seconds of Driver Glances? Fridman, Lex; Toyoda, Heishiro; Seaman, Sean Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems https://doi.org/10.1145/3025453.3025929	conference	May 2017
Driver Distraction Detection Method Based on Continuous Head Pose Estimation Zhao, Zuopeng; Xia, Sili; Xu, Xinzheng Computational Intelligence and Neuroscience, Vol. 2020 https://doi.org/10.1155/2020/9606908	journal	November 2020
Estimation of Relationships for Limited Dependent Variables Tobin, James Econometrica, Vol. 26, Issue 1 https://doi.org/10.2307/1907382	journal	January 1958
Random Parameter Model Used to Explain Effects of Built-Environment Characteristics on Pedestrian Crash Frequency Ukkusuri, Satish; Hasan, Samiul; Aziz, H. M. Abdul Transportation Research Record: Journal of the Transportation Research Board, Vol. 2237, Issue 1 https://doi.org/10.3141/2237-11	journal	January 2011
Using New Mode Choice Model Nesting Structures to Address Emerging Policy Questions: A Case Study of the Pittsburgh Central Business District Khattak, Zulqarnain; Magalotti, Mark; Miller, John Sustainability, Vol. 9, Issue 11 https://doi.org/10.3390/su9112120	journal	November 2017

Similar Records

Investigating the relation between instantaneous driving decisions and safety critical events in naturalistic driving environment

Journal Article · 2021 · Accident Analysis and Prevention · OSTI ID:1784110

Khattak, Zulqarnain H.; Fontaine, Michael D.; Li, Wan; +2 more

Exploring Intentional Driving Volatility in Naturalistic Driving Environment Prior to Involvement in Unsafe Events

Conference · 2019 · OSTI ID:1607278

Wali, Behram; Khattak, Asad; Karnowski, Thomas

How Driving Volatility in Time to Collision Relates to Crash Severity in a Naturalistic Driving Environment

Conference · 2017 · OSTI ID:1468220

Wali, Behram; Khattak, Asad; Karnowski, Thomas

Related Subjects

33 ADVANCED PROPULSION SYSTEMS
Computer vision
Head pose dynamics
Mixed logit
Naturalistic driving
Safety critical
Vehicle kinematics

Title: The role of driver head pose dynamics and instantaneous driving in safety critical events: Application of computer vision in naturalistic driving

Citation Formats

References (41)

Similar Records

Related Subjects