Individual differences, ADHD diagnosis, and driving performance: effects of traffic density and distraction type.

The present study examined the impact of individual differences, attention, and memory deficits on distracted driving. Drivers with ADHD are more susceptible to distraction which results in more frequent collisions, violations, and licence suspensions. Consequently, the present investigation had 36 participants complete preliminary questionnaires, memory tasks, workload indices, and four, 4-min simulated driving scenarios to evaluate such impact. It was hypothesised ADHD diagnosis, type of cellular distraction, and traffic density would each differentially and substantively impact driving performance. Results indicated traffic density and distraction type significantly affected the objective driving facets measured, as well as subjective and secondary task performance. ADHD diagnosis directly impacted secondary task performance. Results further showed significant interactions between distraction type and traffic density on both brake pressure and steering wheel angle negatively impacting lateral and horizontal vehicle control. Altogether, these findings provide substantial empirical evidence for the deleterious effect of cellphone use on driving performance.Practitioner summary: This study examined how ADHD diagnosis, traffic density, and distraction type affect driver behaviour. Participants completed driving behaviour questionnaires, memory tasks, workload indices, and driving scenarios. Results showed that ADHD diagnosis impacted secondary task performance, while traffic and distractions significantly impacted driving performance as well secondary task performance and workload.

Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review.

Driver behaviour monitoring is a broad area of research, with a variety of methods and approaches. Distraction from the use of electronic devices, such as smartphones for texting or talking on the phone, is one of the leading causes of vehicle accidents. With the increasing number of sensors available in vehicles, there is an abundance of data available to monitor driver behaviour, but it has only been available to vehicle manufacturers and, to a limited extent, through proprietary solutions. Recently, research and practice have shifted the paradigm to the use of smartphones for driver monitoring and have fuelled efforts to support driving safety. This systematic review paper extends a preliminary, previously carried out author-centric literature review on smartphone-based driver monitoring approaches using snowballing search methods to illustrate the opportunities in using smartphones for driver distraction detection. Specifically, the paper reviews smartphone-based approaches to distracted driving behaviour detection, the smartphone sensors and detection methods applied, and the results obtained.

EFFNet-CA: An Efficient Driver Distraction Detection Based on Multiscale Features Extractions and Channel Attention Mechanism.

Driver distraction is considered a main cause of road accidents, every year, thousands of people obtain serious injuries, and most of them lose their lives. In addition, a continuous increase can be found in road accidents due to driver's distractions, such as talking, drinking, and using electronic devices, among others. Similarly, several researchers have developed different traditional deep learning techniques for the efficient detection of driver activity. However, the current studies need further improvement due to the higher number of false predictions in real time. To cope with these issues, it is significant to develop an effective technique which detects driver's behavior in real time to prevent human lives and their property from being damaged. In this work, we develop a convolutional neural network (CNN)-based technique with the integration of a channel attention (CA) mechanism for efficient and effective detection of driver behavior. Moreover, we compared the proposed model with solo and integration flavors of various backbone models and CA such as VGG16, VGG16+CA, ResNet50, ResNet50+CA, Xception, Xception+CA, InceptionV3, InceptionV3+CA, and EfficientNetB0. Additionally, the proposed model obtained optimal performance in terms of evaluation metrics, for instance, accuracy, precision, recall, and F1-score using two well-known datasets such as AUC Distracted Driver (AUCD2) and State Farm Distracted Driver Detection (SFD3). The proposed model achieved 99.58% result in terms of accuracy using SFD3 while 98.97% accuracy on AUCD2 datasets.

Anticipatory Driving in Automated Vehicles: The Effects of Driving Experience and Distraction.

OBJECTIVE: To understand the influence of driving experience and distraction on drivers' anticipation of upcoming traffic events in automated vehicles. BACKGROUND: In nonautomated vehicles, experienced drivers spend more time looking at cues that indicate upcoming traffic events compared with novices, and distracted drivers spend less time looking at these cues compared with nondistracted drivers. Further, pre-event actions (i.e., proactive control actions prior to traffic events) are more prevalent among experienced drivers and nondistracted drivers. However, there is a research gap on the combined effects of experience and distraction on driver anticipation in automated vehicles. METHODS: A simulator experiment was conducted with 16 experienced and 16 novice drivers in a vehicle equipped with adaptive cruise control and lane-keeping assist systems (resulting in SAE Level 2 driving automation). Half of the participants in each experience group were provided with a self-paced primarily visual-manual secondary task. RESULTS: Drivers with the task spent less time looking at cues and were less likely to perform anticipatory driving behaviors (i.e., pre-event actions or preparation for pre-event actions such as hovering fingers over the automation disengage button). Experienced drivers exhibited more anticipatory driving behaviors, but their attention toward the cues was similar to novices for both task conditions. CONCLUSION: In line with nonautomated vehicle research, in automated vehicles, secondary task engagement impedes anticipation while driving experience facilitates anticipation. APPLICATION: Though Level 2 automation can relieve drivers of manually controlling the vehicle and allow engagement in distractions, visual-manual distraction engagement can impede anticipatory driving and should be restricted.

Factors That Affect Drivers' Perception of Closing and an Immediate Hazard.

OBJECTIVE: To measure the looming threshold for when drivers perceive closing and an immediate hazard and determine what factors affect these thresholds. BACKGROUND: Rear-end collisions are a common type of crash. One key issue is determining when drivers first perceive they need to react. The looming threshold for closing and an immediate hazard are critical perceptual thresholds that reflect when drivers perceive they need to react. METHOD: Two driving simulator experiments examined whether engaging in a cell phone conversation and whether the complexity of the roadway environment affect these thresholds for the perception of closing and immediate hazard. Half of the participants engaged in a cognitive task, the last letter task, to emulate a cell phone conversation, and all participants experienced both simple and complex roadway environments. RESULTS: Drivers perceived an immediate hazard later when engaged in a cell phone conversation than when not engaged in a conversation but only when the driving task was relatively less demanding (e.g., simple roadway, slow closing velocity). Compared to simple scenes, drivers perceived closing and an immediate hazard later for complex scenes but only when closing velocity was 30 mph (48.28 km/h) or greater. CONCLUSION: Cell phone conversation can affect when drivers perceive an immediate hazard when the roadway is less demanding. Roadway complexity can affect when drivers perceive closing and an immediate hazard when closing velocity is high. APPLICATION: Results can aid accident analysis cases and the design of driving automation systems by suggesting when a typical driver would respond.

Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators.

This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger-Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.

Assessing Distraction Potential of Augmented Reality Head-Up Displays for Vehicle Drivers.

OBJECTIVE: To develop a framework for quantifying the visual and cognitive distraction potential of augmented reality (AR) head-up displays (HUDs). BACKGROUND: AR HUDs promise to be less distractive than traditional in-vehicle displays because they project information onto the driver's forward-looking view of the road. However, AR graphics may direct the driver's attention away from critical road elements. Moreover, current in-vehicle device assessment methods, which are based on eyes-off-road time measures, cannot capture this unique challenge. METHOD: This article proposes a new method for the assessment of AR HUDs by measuring driver gaze behavior, situation awareness, confidence, and workload. An experimental user study (n = 24) was conducted in a driving simulator to apply the proposed method for the assessment of two AR pedestrian collision warning (PCW) design alternatives. RESULTS: Only one of the two tested AR interfaces improved driver awareness of pedestrians without visually and cognitively distracting drivers from other road elements that were not augmented by the display but still critical for safe driving. CONCLUSION: Our initial human-subject experiment demonstrated the potential of the proposed method in quantifying both positive and negative consequences of AR HUDs on driver cognitive processes. More importantly, the study suggests that AR interfaces can be informative or distractive depending on the perceptual forms of graphical elements presented on the displays. APPLICATION: The proposed methods can be applied by designers of in-vehicle AR HUD interfaces and be leveraged by designers of AR user interfaces in general.

The Influence of Visual-Manual Distractions on Anticipatory Driving.

OBJECTIVE: The aim of this study is to investigate how anticipatory driving is influenced by distraction. BACKGROUND: The anticipation of future events in traffic can allow potential gains in recognition and response times. Anticipatory actions (i.e., control actions in preparation for potential traffic changes) have been found to be more prevalent among experienced drivers in simulator studies when driving was the sole task. Despite the prevalence of visual-manual distractions and their negative effects on road safety, their influence on anticipatory driving has not yet been investigated beyond hazard anticipation. METHODS: A simulator experiment was conducted with 16 experienced and 16 novice drivers. Half of the participants were provided with a self-paced visual-manual secondary task presented on a dashboard display. RESULTS: More anticipatory actions were observed among experienced drivers; experienced drivers also exhibited more efficient visual scanning behaviors as indicated by higher glance rates toward and percent times looking at cues that facilitate the anticipation of upcoming events. Regardless of experience, those with the secondary task displayed reduced anticipatory actions and paid less attention toward anticipatory cues. However, experienced drivers had lower odds of exhibiting long glances toward the secondary task compared to novices. Further, the inclusion of glance duration on anticipatory cues increased the accuracy of a model predicting anticipatory actions based on on-road glance durations. CONCLUSION: The results provide additional evidence to existing literature supporting the role of driving experience and distraction engagement in anticipatory driving. APPLICATION: These findings can guide the design of in-vehicle systems and guide training programs to support anticipatory driving.

Effects of Gesture-Based Interaction on Driving Behavior: A Driving Simulator Study Using the Projection-Based Vehicle-in-the-Loop.

OBJECTIVE: We observe the driving performance effects of gesture-based interaction (GBI) versus touch-based interaction (TBI) for in-vehicle information systems (IVISs). BACKGROUND: As a contributing factor to a number of traffic accidents, driver distraction is a significant problem for traffic safety. More specifically, visual distraction has a strong negative impact on driving performance and risk perception. Thus, the implementation of new interaction systems that use midair gestures to encourage glance-free interactions could reduce visual distraction among drivers. METHODS: In this experiment, participants drove a projection-based Vehicle-in-the-Loop. The projection-based technology combines a visual simulation with kinesthetic, vestibular, and auditory feedback from a car on a test track. While driving, participants used GBI or TBI to perform IVIS tasks. To investigate driving behavior related to critical driving situations and car-following maneuvers, vehicle data based upon longitudinal and lateral driving were collected. RESULTS: Participants reacted faster to critical driving situations when using GBI compared to TBI. For drivers using TBI, steering performance decreased and time headway to a preceding vehicle was higher. CONCLUSION: Gestures provide a safe alternative to in-vehicle interactions. Moreover, GBI has fewer effects on driver distraction than TBI. APPLICATION: Potential applications of this research include all in-vehicle interaction systems used by drivers.

How (where) Does Music Background Hamper Driver Behaviour?

OBJECTIVE: The current study examined the effects of music on Hazard Perception - a skill that serves as a proxy for safe driving. BACKGROUND: There is contradictory evidence whether or not music engagement leads to decremented driver performance and compromises traffic safety. METHOD: In the study, 36 participants performed a standard Video-Based Hazard Perception Test under three aural conditions: Road and Traffic Sounds (RS); RS + Driver-Preferred Music; RS + Alternative Music. RESULTS: The results show no effect of aural backgrounds (including music) on the situation awareness portion of the driving task. CONCLUSION: Music background might affect later stages of the driving task such as response selection and/or response execution (mitigation). APPLICATION: The investigation of human factors related to vehicular control should include how (where) music might trigger failures in perception and/or behaviour.

A Hybrid Deep Learning Model for Recognizing Actions of Distracted Drivers.

With the rapid spreading of in-vehicle information systems such as smartphones, navigation systems, and radios, the number of traffic accidents caused by driver distractions shows an increasing trend. Timely identification and warning are deemed to be crucial for distracted driving and the establishment of driver assistance systems is of great value. However, almost all research on the recognition of the driver's distracted actions using computer vision methods neglected the importance of temporal information for action recognition. This paper proposes a hybrid deep learning model for recognizing the actions of distracted drivers. Specifically, we used OpenPose to obtain skeleton information of the human body and then constructed the vector angle and modulus ratio of the human body structure as features to describe the driver's actions, thereby realizing the fusion of deep network features and artificial features, which improve the information density of spatial features. The K-means clustering algorithm was used to preselect the original frames, and the method of inter-frame comparison was used to obtain the final keyframe sequence by comparing the Euclidean distance between manually constructed vectors representing frames and the vector representing the cluster center. Finally, we constructed a two-layer long short-term memory neural network to obtain more effective spatiotemporal features, and one softmax layer to identify the distracted driver's action. The experimental results based on the collected dataset prove the effectiveness of this framework, and it can provide a theoretical basis for the establishment of vehicle distraction warning systems.

Effect of Fixed and sEMG-Based Adaptive Shared Steering Control on Distracted Driver Behavior.

Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the forearm muscle activity of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane change tasks. The experimental conditions comprised two driver states: attentive and distracted. Under each condition, evaluations were performed on three types of haptic guidance: none (manual), fixed authority, and adaptive authority based on feedback from the forearm surface electromyography of the driver. Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority. Moreover, there was a tendency for distracted drivers to reduce grip strength on the steering wheel to follow the haptic guidance with fixed authority, resulting in a relatively shorter double lane change duration.

Examination of Driver Visual and Cognitive Responses to Billboard Elicited Passive Distraction Using Eye-Fixation Related Potential.

Distractions external to a vehicle contribute to visual attention diversion that may cause traffic accidents. As a low-cost and efficient advertising solution, billboards are widely installed on side of the road, especially the motorway. However, the effect of billboards on driver distraction, eye gaze, and cognition has not been fully investigated. This study utilises a customised driving simulator and synchronised electroencephalography (EEG) and eye tracking system to investigate the cognitive processes relating to the processing of driver visual information. A distinction is made between eye gaze fixations relating to stimuli that assist driving and others that may be a source of distraction. The study compares the driver's cognitive responses to fixations on billboards with fixations on the vehicle dashboard. The measured eye-fixation related potential (EFRP) shows that the P1 components are similar; however, the subsequent N1 and P2 components differ. In addition, an EEG motor response is observed when the driver makes an adjustment of driving speed when prompted by speed limit signs. The experimental results demonstrate that the proposed measurement system is a valid tool in assessing driver cognition and suggests the cognitive level of engagement to the billboard is likely to be a precursor to driver distraction. The experimental results are compared with the human information processing model found in the literature.

Mitigating Teen Driver Distraction: In-Vehicle Feedback Based on Peer Social Norms.

OBJECTIVE: To investigate the efficacy of in-vehicle feedback based on peer social norms in mitigating teen driver distraction. BACKGROUND: Distraction is a significant problem among teen drivers. Research into the use of in-vehicle technologies to mitigate this issue has been limited. In particular, there is a need to study whether social norms interventions provided through in-vehicle feedback can be effective. Peers are important social referents for teens; thus, normative intervention based on this group is promising. Socially proximal referents have a greater influence on behavior; thus, tailoring peer norm feedback based on gender may provide additional benefits. METHOD: In this study, 57 teens completed a driving simulator experiment while performing a secondary task in three between-subject conditions: (a) postdrive feedback incorporating same-gender peer norms, (b) postdrive feedback incorporating opposite-gender peer norms, and (c) no feedback. Feedback involved information based on descriptive norms (what others do). RESULTS: Teens' self-reported frequency of distraction engagement was positively correlated with their perceptions of their peers' engagement in and approval of distractions. Feedback based on peer norms was effective in reducing distraction engagement and improving driving performance, with no difference between same- and opposite-gender feedback. CONCLUSION/APPLICATION: Feedback based on peer norms can help mitigate driver distraction among teens. Tailoring social norms feedback to teen gender appears to not provide any additional benefits. Longer-term effectiveness in real-world settings should be investigated.

Effects of Distraction in On-Road Level 2 Automated Driving: Impacts on Glance Behavior and Takeover Performance.

OBJECTIVE: The paper aimed to investigate glance behaviors under different levels of distraction in automated driving (AD) and understand the impact of distraction levels on driver takeover performance. BACKGROUND: Driver distraction detrimentally affects takeover performance. Glance-based distraction measurement could be a promising method to remind drivers to maintain enough attentiveness before the takeover request in partially AD. METHOD: Thirty-six participants were recruited to drive a Tesla Model S in manual and Autopilot modes on a test track while engaging in secondary tasks, including temperature-control, email-sorting, and music-selection, to impose low and high distractions. During the test drive, participants needed to quickly change the lane as if avoiding an immediate road hazard if they heard an unexpected takeover request (an auditory warning). Driver state and behavior over the test drive were recorded in real time by a driver monitoring system and several other sensors installed in the Tesla vehicle. RESULTS: The distribution of off-road glance duration was heavily skewed (with a long tail) by high distractions, with extreme glance duration more than 30 s. Moreover, being eyes-off-road before takeover could cause more delay in the urgent takeover reaction compared to being hands-off-wheel. CONCLUSION: The study measured off-road glance duration under different levels of distraction and demonstrated the impacts of being eyes-off-road and hands-off-wheel on the following takeover performance. APPLICATION: The findings provide new insights about engagement in Level 2 AD and are useful for the design of driver monitoring technologies for distraction management.

Haptic Lane-Keeping Assistance for Truck Driving: A Test Track Study.

OBJECTIVE: This study aims to compare the effectiveness and subjective acceptance of three designs for haptic lane-keeping assistance in truck driving. BACKGROUND: Haptic lane-keeping assistance provides steering torques toward a reference trajectory, either continuously or only when exceeding a bandwidth. These approaches have been previously investigated in driving simulators, but it is unclear how these generalize toward real-life truck driving. METHOD: Three haptic lane-keeping algorithms to assist truck drivers were evaluated on a 6.3-km-long oval-shaped test track: (1) a single-bandwidth (SB) algorithm, which activated assistance torques when the predicted lateral deviation from lane center exceeded 0.4 m; (2) a double-bandwidth (DB) algorithm, which activated as SB, but deactivated after returning within 0.15 m lateral deviation; and (3) an algorithm providing assistance torques continuously (Cont) toward the lane center. Fifteen participants drove four trials each, one trial without and one for each haptic assistance design. Furthermore, participants drove with and without a concurrent visually distracting task. RESULTS: Compared to unsupported driving, all three assistance systems provided similar safety benefits in terms of decreased absolute lateral position and number of lane departures. Participants reported higher satisfaction and usability for Cont compared to SB. CONCLUSION: The continuous assistance was better accepted than bandwidth assistance, a finding consistent with prior driving simulator research. Research is still needed to investigate the long-term effects of haptic assistance on reliance and after-effects. APPLICATION: The present results are useful for designers of haptic lane-keeping assistance, as driver acceptance and performance are determinants of reliance and safety, respectively.

Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research.

Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed with this purpose in mind. This study is based on retrospective data obtained from two driver monitoring systems to study distraction and drowsiness risk factors. The data includes information about the trips performed by 330 drivers and corresponding distraction and drowsiness alerts emitted by the systems. The drivers' historical travel data allowed defining two groups with different mobility patterns (short-distance and long-distance drivers) through a cluster analysis. Then, the impacts of the driver's profile and trip characteristics (e.g., driving time, average speed, and breaking time and frequency) on inattention were analyzed using ordered probit models. The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers. The driving time increases the probability of inattention, while the breaking frequency is more important to mitigate inattention than the breaking time. Higher average speeds increase the inattention risk, being associated with road facilities featuring a monotonous driving environment.

WiFi-Based Driver's Activity Monitoring with Efficient Computation of Radio-Image Features.

Driver distraction and fatigue are among the leading contributing factors in various fatal accidents. Driver activity monitoring can effectively reduce the number of roadway accidents. Besides the traditional methods that rely on camera or wearable devices, wireless technology for driver's activity monitoring has emerged with remarkable attention. With substantial progress in WiFi-based device-free localization and activity recognition, radio-image features have achieved better recognition performance using the proficiency of image descriptors. The major drawback of image features is computational complexity, which increases exponentially, with the growth of irrelevant information in an image. It is still unresolved how to choose appropriate radio-image features to alleviate the expensive computational burden. This paper explores a computational efficient wireless technique that could recognize the attentive and inattentive status of a driver leveraging Channel State Information (CSI) of WiFi signals. In this novel research work, we demonstrate an efficient scheme to extract the representative features from the discriminant components of radio-images to reduce the computational cost with significant improvement in recognition accuracy. Specifically, we addressed the problem of the computational burden by efficacious use of Gabor filters with gray level statistical features. The presented low-cost solution requires neither sophisticated camera support to capture images nor any special hardware to carry with the user. This novel framework is evaluated in terms of activity recognition accuracy. To ensure the reliability of the suggested scheme, we analyzed the results by adopting different evaluation metrics. Experimental results show that the presented prototype outperforms the traditional methods with an average recognition accuracy of 93 . 1 % in promising application scenarios. This ubiquitous model leads to improve the system performance significantly for the diverse scale of applications. In the realm of intelligent vehicles and assisted driving systems, the proposed wireless solution can effectively characterize the driving maneuvers, primary tasks, driver distraction, and fatigue by exploiting radio-image descriptors.

Allocation of Driver Attention for Varying In-Vehicle System Modalities.

OBJECTIVE: This paper examines drivers' allocation of attention using response time to a tactile detection response task (TDRT) while interacting with an in-vehicle information system (IVIS) over time. BACKGROUND: Longer TDRT response time is associated with higher cognitive workload. However, it is not clear what role is assumed by the human and system in response to varying in-vehicle environments over time. METHOD: A driving simulator study with 24 participants was conducted with a restaurant selection task of two difficulty levels (easy and hard) presented in three modalities (audio only, visual only, hybrid). A linear mixed-effects model was applied to identify factors that affect TDRT response time. A nonparametric time-series model was also used to explore the visual attention allocation under the hybrid mode over time. RESULTS: The visual-only mode significantly increased participants' response time compared with the audio-only mode. Females took longer to respond to the TDRT when engaged with an IVIS. The study showed that participants tend to use the visual component more toward the end of the easy tasks, whereas the visual mode was used more at the beginning of the harder tasks. CONCLUSION: The visual-only mode of the IVIS increased drivers' cognitive workload when compared with the auditory-only mode. Drivers showed different visual attention allocation during the easy and hard restaurant selection tasks in the hybrid mode. APPLICATION: The findings can help guide the design of automotive user interfaces and help manage cognitive workload.

Driver crash risk factors and prevalence evaluation using naturalistic driving data.

The accurate evaluation of crash causal factors can provide fundamental information for effective transportation policy, vehicle design, and driver education. Naturalistic driving (ND) data collected with multiple onboard video cameras and sensors provide a unique opportunity to evaluate risk factors during the seconds leading up to a crash. This paper uses a National Academy of Sciences-sponsored ND dataset comprising 905 injurious and property damage crash events, the magnitude of which allows the first direct analysis (to our knowledge) of causal factors using crashes only. The results show that crash causation has shifted dramatically in recent years, with driver-related factors (i.e., error, impairment, fatigue, and distraction) present in almost 90% of crashes. The results also definitively show that distraction is detrimental to driver safety, with handheld electronic devices having high use rates and risk.