Driver Distraction

Naturalistic driving

Distracted driving is a significant concern for traffic safety. We use the SHRP2 NEST dataset to investigate distraction engagement while driving. Our findings reveal that drivers modulate their distraction engagement based on environmental demands and the chosen speed. Environments with higher visual difficulty (e.g., busy urban areas during rainy weather) decrease the likelihood of distraction engagement, whereas speed increase is associated with a decrease in the likelihood of distraction engagement only in higher motor control difficulty situations (i.e., curvy and wet roads). Our research has been featured in Toyota CSRC Report for 2017.

Sponsor(s): Toyota Collaborative Safety Research Center (CSRC)

Effects of searching for street parking on driver behaviour

Searching for street parking adds to traffic congestion and time/fuel wasted for drivers however its effect on driving behaviour and driver performance is unknown. This study investigates how searching for street parking affects driver physiology, performance, and visual attention allocation. Participants drive a vehicle instrumented with sensors and cameras in a busy urban centre (downtown Toronto). They will also be outfitted with physiological sensors (ECG and GSR) and a head-mounted eye tracker. The analysis of the physiological and vehicle data is expected to draw conclusions on increased mental workload of drivers while searching for street parking as well as the effect on their driving performance.

Sponsor(s): NSERC (Discovery Grant)

Student PI(s): Canmanie Teresa Ponnambalam; George Liu

Emerging technologies and driver distraction

Smartwatches and other wearables are new devices that are making their way into the driving space, and have been marketed as being easily accessible and glanceable ways of getting information. We conducted two driving simulator experiments comparing how drivers interact and respond to notifications using smartwatches and smartphones. We found that our participants spent more time looking at smartwatches than smartphones when they read notifications. Our participants were also slower in reaction to a lead vehicle braking while responding to notifications on the smartwatch than the smartphone. These results show that smartwatches may have detrimental effects on driving that are similar to or worse than smartphones, and can be a potential source of distraction on the road.

Sponsor(s): NSERC (Discovery Grant)

Student PI(s): Wayne Giang

Perception-response time to emergency roadway hazards and the effect of cognitive distraction

A driver’s ability to detect potential hazards and respond accordingly is crucial for traffic safety. The main objectives of this study are to (1) investigate the use of eye movement recordings combined with other motor response measurements in order to sub-divide a driver’s perception-response time interval when responding to an emergency roadway hazard, (2) to use this method of sub-diving to gain further insight into the effects of cognitive distraction on a driver’s ability to perceive and respond to an emergency roadway hazard by analyzing the effects at each stage.

Sponsor(s): NSERC (Discovery Grant)

Effects of driver distraction on crash injury severity and crash type

The objective of this research is to determine how different distraction types interact with driver age to influence crash outcomes. The particular crash outcomes we focus on consist of the crash type as well as the injury severity sustained by occupants involved in the crash. The findings of this research will have implications for policy making as well as prioritizing capabilities of distraction-related safety systems.

Sponsor(s): NSERC (Discovery Grant)

Effects of distractions on injury severity in police-involved crashes

A police cruiser can have multiple devices integrated in the cab, such as a laptop, radio, as well as strobe and siren controls. Although distractions might be a concern for police drivers, the effects of distractions on police-involved crashes have not been empirically studied before. As a first step in addressing this research gap, we built an ordered logit model to investigate the likelihood of severe injuries when a crash involves distracted police drivers.

Sponsor(s): University of Toronto (Start-up grant)

Estimating drowsiness in drivers through wearable devices

The driver state can be monitored using vehicle-based (e.g., speed, acceleration), physiological (e.g., heart rate, brain activity), and face and body expression (e.g., head position, eye movements) measures (Aghaei et al., 2016). Although there has been extensive research on the application of driver state monitoring techniques in non-automated driving, most of the previous research used research-grade devices, which are not suitable or too expensive for in-vehicle application. Recent advancement in technologies allows for integration of a variety of sensors into smaller, more affordable and less intrusive wearable (e.g., smartwatch) and portable (e.g., smartphone) devices. For example, recent models of Apple Watch and Fitbit are equipped with sensors to monitor heart rate or even ECG; Garmin watch provides a function of measuring blood oxygen saturation level; Muse, a consumer-based headband, can record users’ EEG from channels on the forehead, along with users’ heart rate (HR), head movement and respiration in real-time. The increasing computing power in smartphones also makes it possible to assess driver state and even use cameras to analyze drivers’ gaze location and facial expressions in real-time. All these technologies provide the potential for low-cost in-vehicle driver state monitoring.

The objectives of this project are thus to design experiments and collect data to test the feasibility of different measures in identifying drowsiness in drivers, and train machine learning models using the collected data to estimate driver drowsiness.

Student PI(s): Dengbo He; Ziquan (William) Wang

Combining vehicle, physiological, visual data to provide driver feedback

This project investigates how mental distractions could influence drivers’ physiological states, facial expressions, and driving performance and further explores how physiological states, facial expressions and driving performance can be used to estimate drivers’ states. The cognitive state of drivers at varying levels of mental workload will be examined using the following physiological measures: heart rate, galvanic skin response (GSR), respiration and electroencephalogram (EEG), as well as information on facial expression and driving performance information. We also adopted and modified the commonly used n-back task to make it suitable for imposing cognitive load in in-vehicle EEG-related studies. Ultimately, this project aims to transform potentially distracting technologies into adaptive interfaces that offer constructive feedback to enhance driving experience by maintaining an optimal level of driver arousal during various driving conditions.

Student PI(s): Dengbo He

Enhancing voice-activation technologies to improve driving behaviour

Voice-activated infotainment systems are becoming increasingly common in modern vehicles, especially as an alternative to the more dangerous and often illegal option of hand-held cell phone usage. However, the voice-activated systems are not without their shortcomings. In the presence of loud background noise, such as talking passengers or music, voice recognition accuracy decreases. Working with an industry leader in voice control technologies, this project aims to evaluate how drivers interact with and feel about a novel voice control system which can still work effectively under heavy background noise.

Student PI(s): Neil Sokol; Joey Chakraborty

Publications

Naturalistic driving

Chen, H. Y. W. & Donmez, B.* (accepted pending review of minor revisions). A naturalistic driving study of feedback timing and financial incentives in promoting speed limit compliance. IEEE Transactions on Human Machine Systems.

Kanaan, D., Ayas, S., Donmez, B., Risteska, M., & Chakraborty, J. (2019). Using naturalistic vehicle-based data to predict distraction and environmental demand. International Journal of Mobile Human Computer Interaction, 11(3), 59-70.

Risteska, M., Donmez, B., Chen, H. Y. W., & Modi, M. (2018). Prevalence of engagement in single vs. multiple types of secondary tasks: results from the Naturalistic Engagement in Secondary Tasks (NEST) dataset. Transportation Research Record, 2672(37), 1-10.

Risteska, M., Chakraborty, J., & Donmez, B. (2018). Predicting environmental demand and secondary task engagement using vehicle kinematics from naturalistic driving data. In Proceedings of the 10^th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 66-73), Toronto, ON. (37% acceptance)

Risteska, M., (2018). Exploration of naturalistic driving data: Development of distracted driver behaviour models (MASc Thesis). University of Toronto.

Donmez, B., Chen, H. Y. W., & Risteska, M. (2017). Naturalistic Engagement in Secondary Tasks (NEST): Driver Behavior and Secondary Task Engagement in Crashes and Near-Crashes. (HFASt2017-01), University of Toronto Human Factors and Applied Statistics Laboratory. Technical report submitted to Toyota Collaborative Safety Research Center.

Domoyer, J. E., Lee, J., Reimer, B., Seaman, S., Angell, L., Zhang, C., & Donmez, B. (2016). SHRP2 NEST Database: Exploring conditions of secondary task engagement in naturalistic trip data. In Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Ann Arbor, MI.

Chen, H. Y. W., Donmez, B., & Chung, I. (2016). Designing feedback to induce safer driving behaviors: A naturalistic study of feedback characteristics and speed limit compliance behaviour in automobile driving. (HFASt2016-01), University of Toronto Human Factors and Applied Statistics Laboratory. Technical report submitted to Toyota Collaborative Safety Research Center.

Merrikhpour, M., Donmez, B., Wang, C-B., Hayes, B., & Grush, B. (2013). Associations between drivers’ safety records and driving styles: A naturalistic study. In Proceedings of the Human Factors and Ergonomics Society 57th Annual Meeting, San Diego, CA.

Effects of searching for street parking on driver behaviour

Ponnambalam, C. T. & Donmez, B. (2020). Searching for street parking: Effects on driver vehicle control, workload, physiology, and glances. Frontiers in Psychology: Performance Science, 11, 02618.

Ponnambalam, C. T., Cheng, R., & Donmez, B. (2018). Effects of searching for street parking on driver behaviour and physiology: Results from an on-road instrumented vehicle study. In Proceedings of the Human Factors and Ergonomics Society 62^nd Annual Meeting (pp. 1404-1408), Philadelphia, PA.

Emerging technologies and driver distraction

Giang, W. C. W., Chen, H. Y. W., & Donmez, B. (2019). Smartwatches vs. smartphones: Notification engagement while driving. In Multigenerational Online Behavior and Media Use: Concepts, Methodologies, Tools, and Applications. Information Resources Management Association, pp. 453-473.

Giang, W. C. W., Chen, H. Y. W., & Donmez, B. (2017). Smartwatches vs. smartphones: Notification engagement while driving. International Journal of Mobile Human Computer Interaction, 9(2), 40-58.

Giang, W. C. W., Shanti, I., Chen, H. Y. W., Zhou, A., & Donmez, B. (2015). Smartwatches vs. smartphones: A preliminary report of driver behavior and perceived risk while responding to notifications. In Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Nottingham, UK.

Giang, W. C. W., Hoekstra-Atwood, L., & Donmez, B. (2014). Driver engagement in notifications: A comparison of visual manual interaction between smartwatches and smartphones. In Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting, Chicago, IL.

Hoekstra-Atwood, L., Chen, H. Y. W., Giang, W. C. W., & Donmez, B. (2014). Measuring inhibitory control in driver distraction. In Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Seattle, WA.

Chen, H. Y. W., Marulanda, S., Hoesktra-Atwood, L., Donmez, B., & Giang, W. C. W. (2014). Designing feedback to induce safer driving behaviors: A laboratory study of driver characteristics and susceptibility to driver distractions. (HFASt2014-01), University of Toronto Human Factors and Applied Statistics Laboratory. Technical report submitted to Toyota Collaborative Safety Research Center.

Perception-response time to emergency roadway hazards and the effect of cognitive distraction

D’Addario, P. (2014). Perception-response time to emergency roadway hazards and the effect of cognitive distraction (MASc Thesis). University of Toronto.

Effects of driver distraction on crash injury severity and crash type

Liu, Z. (2012). The effects of distractions and driver’s age on the type of crash and the injury severity sustained by occupants involved in a crash (MASc Thesis). University of Toronto.

Effects of distractions on injury severity in police-involved crashes

Liu, Z. & Donmez, B. (2011). Effects of distraction on injury severity in police-involved crashes. In Proceedings of the Transportation Research Board 90th Annual Meeting (11-3591), Washington, D.C.

Estimating drowsiness in drivers through wearable devices

He, D., Risteska, M., Donmez, B., & Chen, K. (2021). Driver cognitive load classification based on physiological data. In P.Eslambolchilar, A.Komninos, & M. Dunlop (Eds.), Intelligent Computing for Interactive System Design: Statistics, Digital Signal Processing, and Machine Learning in Practice. Association of Computing Machinery, New York, pp. 409-429.

He, D., Donmez, B., Liu C., & Plataniotis, K. N. (2019). High cognitive load assessment in drivers through wireless electroencephalography and the validation of a modified n-back task. IEEE Transactions on Human Machine Systems, 49(4), 362-371.

He, D., Kanaan, D., & Donmez, B. (2019). A taxonomy of strategies for supporting time-sharing with non-driving tasks in automated driving. In Proceedings of the Human Factors and Ergonomics Society 63^rd Annual Meeting (pp. 2088-2092), Seattle, WA.

He, D. & Donmez, B. (2018). The effects of distraction on anticipatory driving. In Proceedings of the Human Factors and Ergonomics Society 62^nd Annual Meeting (pp. 1960-1964), Philadelphia, PA. (HFES the Alphonse Chapanis Award Finalist for Best Student Paper, 1 out of 3).

Combining vehicle, physiological, visual data to provide driver feedback

\He, D., Kanaan, D., & Donmez, B. (2021). In-vehicle displays to support driver anticipation of traffic conflicts in automated vehicles. Accident Analysis and Prevention, 149, 105842. [post-print]

He, D., & Donmez, B. (2020). The Influence of Visual-Manual Distractions on Anticipatory Driving. Human Factors.

He, D., Liu C., Donmez, B., & Plataniotis, K. (2017). Assessing high cognitive load in drivers through Electroencephalography. In Proceedings of the Transportation Research Board 96th Annual Meeting (17-02615), Washington, D.C.

Enhancing voice-activation technologies to improve driving behaviour

Sokol, N., Chen, H. Y. W., & Donmez, B. (2017). Voice-controlled in-vehicle systems: Effects of voice-recognition accuracy in the presence of background noise. In Proceedings of the 9th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, Manchester Village, VT.

Sokol, N. (2017). The effects of noise-robustness of in-car voice-controlled systems on user perceptions and driving behaviour (MASc Thesis). University of Toronto.