In-vehicle information system (IVIS) use is prevalent among young adults. However, their interaction with IVIS needs to be better understood. Therefore, an on-road study aims to explore the effects of input modalities and secondary task types on young drivers\' secondary task performance, driving performance, and visual glance behavior. A 2 × 4 within-subject design was undertaken. The independent variables are input modalities (auditory-speech and visual-manual) and secondary task types (calls, music, navigation, and radio). The dependent variables include secondary task performance (task completion time, number of errors, and SUS), driving performance (average speed, number of lane departure warnings, and NASA-TLX), and visual glance behavior (average glance duration, number of glances, total glance duration, and number of glances over 1.6 s). The statistical analysis result showed that the main effect of input modalities is significant, with more distraction during visual-manual than auditory-speech. The main impact of secondary task types was also substantial across most metrics, aside from average speed and average glance duration. Navigation and music were the most distracting, followed by calls, and radio came in last. The distracting effect of input modalities is relatively stable and generally not moderated by the secondary task types, except radio tasks. The findings practically benefit the driver-friendly human-machine interface design, preventing IVIS-related distraction.

Driver distraction, crucial for road safety, can benefit from multimodal physiological signals assessment. However, fusion of heterogeneous data is highly challenging. In this study, we address this challenge by exploring 1D convolution neural network (CNN) with squeeze and excitation networks (SEcNN) on multimodal data. For this, electrocardiogram (256Hz) and respiration (128Hz) are obtained from subjects (N=10) while using textile electrodes and driving in different scenarios namely normal, texting and calling. The obtained multimodal data is preprocessed and SEcNN to identify driver distraction. Experiments are performed using Leave-one-out-subject cross validation. The proposed approach is able to discriminate drivers distraction. It is observed that SEcNN yields average accuracy 57.03% and average F1 score 54.90% for shorter segments. Thus, the proposed approach using wearable shirts could be useful for non-intrusive monitoring in real world driver scenarios.

Driver monitoring systems (DMS) are crucial in autonomous driving systems (ADS) when users are concerned about driver/vehicle safety. In DMS, the significant influencing factor of driver/vehicle safety is the classification of driver distractions or activities. The driver\'s distractions or activities convey meaningful information to the ADS, enhancing the driver/ vehicle safety in real-time vehicle driving. The classification of driver distraction or activity is challenging due to the unpredictable nature of human driving. This paper proposes a convolutional block attention module embedded in Visual Geometry Group (CBAM VGG16) deep learning architecture to improve the classification performance of driver distractions. The proposed CBAM VGG16 architecture is the hybrid network of the CBAM layer with conventional VGG16 network layers. Adding a CBAM layer into a traditional VGG16 architecture enhances the model\'s feature extraction capacity and improves the driver distraction classification results. To validate the significant performance of our proposed CBAM VGG16 architecture, we tested our model on the American University in Cairo (AUC) distracted driver dataset version 2 (AUCD2) for cameras 1 and 2 images. Our experiment results show that the proposed CBAM VGG16 architecture achieved 98.65% classification accuracy for camera 1 and 97.85% for camera 2 AUCD2 datasets. The CBAM VGG16 architecture also compared the driver distraction classification performance with DenseNet121, Xception, MoblieNetV2, InceptionV3, and VGG16 architectures based on the proposed model\'s accuracy, loss, precision, F1 score, recall, and confusion matrix. The drivers\' distraction classification results indicate that the proposed CBAM VGG16 has 3.7% classification improvements for AUCD2 camera 1 images and 5% for camera 2 images compared to the conventional VGG16 deep learning classification model. We also tested our proposed architecture with different hyperparameter values and estimated the optimal values for best driver distraction classification. The significance of data augmentation techniques for the data diversity performance of the CBAM VGG16 model is also validated in terms of overfitting scenarios. The Grad-CAM visualization of our proposed CBAM VGG16 architecture is also considered in our study, and the results show that VGG16 architecture without CBAM layers is less attentive to the essential parts of the driver distraction images. Furthermore, we tested the effective classification performance of our proposed CBAM VGG16 architecture with the number of model parameters, model size, various input image resolutions, cross-validation, Bayesian search optimization and different CBAM layers. The results indicate that CBAM layers in our proposed architecture enhance the classification performance of conventional VGG16 architecture and outperform the state-of-the-art deep learning architectures.

Distracted driving is responsible for nearly 1 million crashes each year in the United States alone, and a major source of driver distraction is handheld phone use. We conducted a randomized, controlled trial to compare the effectiveness of interventions designed to create sustained reductions in handheld use while driving (NCT04587609). Participants were 1,653 consenting Progressive® Snapshot® usage-based auto insurance customers ages 18 to 77 who averaged at least 2 min/h of handheld use while driving in the month prior to study invitation. They were randomly assigned to one of five arms for a 10-wk intervention period. Arm 1 (control) got education about the risks of handheld phone use, as did the other arms. Arm 2 got a free phone mount to facilitate hands-free use. Arm 3 got the mount plus a commitment exercise and tips for hands-free use. Arm 4 got the mount, commitment, and tips plus weekly goal gamification and social competition. Arm 5 was the same as Arm 4, plus offered behaviorally designed financial incentives. Postintervention, participants were monitored until the end of their insurance rating period, 25 to 65 d more. Outcome differences were measured using fractional logistic regression. Arm 4 participants, who received gamification and competition, reduced their handheld use by 20.5% relative to control (P < 0.001); Arm 5 participants, who additionally received financial incentives, reduced their use by 27.6% (P < 0.001). Both groups sustained these reductions through the end of their insurance rating period.

Navigating through complex road geometries, such as roundabouts, poses significant challenges and safety risks for drivers. These challenges may be exacerbated when drivers are distracted by mobile phone conversations. The interplay of road geometry, driving state, and driver characteristics in creating compound risks remains an underexplored area in existing literature. Proper understanding of such compound crash risk is not only crucial to improve road geometric design but also to educate young drivers, who are particularly risk-takers and to devise strict penalties for mobile phone usage whilst driving. To fill this gap, this study examines crash risks associated with gap acceptance manoeuvres at roundabouts in the simulated environment of the CARRS-Q driving simulators, where 32 licenced young drivers were exposed to a gap acceptance scenario in three phone conditions: baseline (no phone conversation), handheld, and hands-free. A parametric random parameters survival modelling approach is adopted to understand safety margins-characterised by gap times-during gap acceptance scenarios at roundabouts, concurrently uncover driver-level heterogeneity with mobile phone distraction and capture repeated measures of experiment design. The model specification includes the handheld phone condition as a random parameter and hands-free phone condition, acceleration noise, gap size, crash history, and gender as non-random parameters. Results suggest that the majority of handheld distracted drivers have smaller safety margins, reflecting the negative consequences of engaging in handheld phone conversations. Interestingly, a group of drivers in the same handheld phone condition have been found to exhibit cautious/safer behaviour, as evidenced by longer gap times, reflecting their risk compensation behaviour. Female distracted drivers are also found to exhibit safer gap acceptance behaviour compared to distracted male drivers. The findings of this study shed light on the compound risk of mobile phone distraction and gap acceptance at roundabouts, requiring policymakers and authorities to devise strict penalties and laws for distracted driving.

The present study investigated the effects of a driver monitoring system that triggers attention warnings in case distraction is detected. Based on the EuroNCAP protocol, distraction could either be long glances away from the forward roadway (≥3s) or visual attention time sharing (>10 cumulative seconds within a 30 s time interval). In a series of manual driving simulator drives, 30 participants completed both driving related tasks (e.g., changing multiple lanes in dense traffic) and non-driving related tasks (e.g., infotainment operations). Results of warning frequencies revealed that visual attention time sharing warnings occurred more frequently than long distraction warnings. Moreover, there was a large number of attention warnings during driving related tasks. Results also revealed that participants\' mental models tended to be less accurate when it came to understanding of the visual attention time sharing warnings as compared to the long distraction warnings, which were understood more accurately. Based on these observations, the work discusses the applicability and design of driver monitoring warnings.

UNASSIGNED: Healthy older drivers may be at high risk of fatal traffic accidents. Our recent study showed that volumetric alterations in gray matter in the brain regions within the dorsal attention network (DAN) were strongly related to the risk of unsafe driving in healthy older people. However, the relationship between white matter (WM) structural connectivity and driving ability in healthy older people is still unclear.
UNASSIGNED: We used diffusion tensor imaging to examine the association between microstructural alterations in the DAN and the risk of unsafe driving among healthy older people. We enrolled 32 healthy older individuals aged over 65 years and screened unsafe drivers using an on-road driving test. We then determined the pattern of WM aberrations in unsafe drivers using tract-based spatial statistics.
UNASSIGNED: The analysis demonstrated that unsafe drivers had significantly higher axial diffusivity values in nine WM clusters compared with safe drivers. These results were primarily observed bilaterally in the dorsal superior longitudinal fasciculus, which is involved in the DAN. Furthermore, correlation analyses showed that higher axial diffusivity values in the superior longitudinal fasciculus were associated with lower Trail Making Test A scores within unsafe drivers. This result suggests that functionally, WM microstructural alterations in the DAN are associated with attention problems, which may contribute to the risk of unsafe driving among healthy older people.
UNASSIGNED: Our findings may elucidate the neurobiological mechanisms underlying the increased risk of unsafe driving in healthy older people, potentially facilitating the development of new interventions to prevent fatal accidents.

UNASSIGNED: Distracted driving is a leading cause of motor vehicle crashes, and cell phone use is a major source of in-vehicle distraction. Many states in the United States have enacted cell phone use laws to regulate drivers\' cell phone use behavior to enhance traffic safety. Numerous studies have examined the effects of such laws on drivers\' cell phone use behavior based on self-reported and roadside observational data. However, little was known about who actually violated the laws at the enforcement level. This study sought to uncover the demographic characteristics of drivers cited for cell phone use while driving and whether these characteristics changed over time since the enactment of cell phone laws.
UNASSIGNED: We acquired useable traffic citation data for 7 states in the United States from 2010 to 2020 and performed descriptive and regression analyses.
UNASSIGNED: Male drivers were cited more for cell phone use while driving. Handheld and texting bans were associated with a greater proportion of cited drivers aged 40 and above, compared to texting-only bans. Trends in the citations issued based on drivers\' age group following the enactment of different cell phone laws were also uncovered. The proportion of citations issued to drivers aged 60 and above increased over time but the temporal trend remained insignificant when population effect was considered.
UNASSIGNED: This study examined the demographic characteristics of drivers cited for cell phone use while driving in selected states with texting-only bans or handheld and texting bans. The results reveal policy-based differences in trends in the proportion of citations issued to drivers in different age groups.

BACKGROUND: Driver distraction from handheld cellphone use contributes to fatal crashes every year but is underreported in terms of the proportion of crashes attributed to any distraction or cellphone use specifically. Existing methods to estimate the prevalence of cellphone distractions are also limited (e.g., observing drivers stopped at intersections, when crash risk is low). Our study used data from Cambridge Mobile Telematics to estimate the prevalence of drivers\' handheld calls and cellphone manipulation while driving, with \"cellphone motion\" based on movement recorded by the phones\' gyroscopes used as a surrogate for manipulation.
METHODS: We compared the telematics measures with the National Highway Traffic Safety Administration\'s roadside observations of driver electronic device use, and logistic regression tested relationships between regional, legislative, and temporal factors and the odds of cellphone behaviors occurring on a trip or at a given point in time.
RESULTS: Results showed 3.5% of trips included at least one handheld phone call and 33.3% included at least an instance of cellphone motion, with handheld calls occurring during 0.78% of overall trip duration and cellphone motion during 2.4% of trip duration.
CONCLUSIONS: Correspondence between trends in cellphone distractions across regional, legislative, and temporal factors suggest telematics data have considerable utility and appear to complement existing datasets.

BACKGROUND: Aggressive behavior of drivers is a source of crashes and high injury severity. Aggressive drivers are part of the driving environment, however, excessive aggressive driving by fellow drivers may take the attention of the recipient drivers away from the road resulting in distracted driving. Such external distractions caused by the aggressive and discourteous behavior of other road users have received limited attention. These distractions caused by fellow drivers (DFDs) may agitate recipient drivers and ultimately increase crash propensity. Aggressive driving behaviors are quite common in South Asia and, thus, it is necessary to determine their contribution to distractions and crash propensity.
METHODS: Our study aimed to evaluate the effects of DFDs using primary data collected through a survey conducted in Lahore, Pakistan. A total of 801 complete responses were obtained. Various hypotheses were defined to explore the associations between the latent factors such as DFDs, anxiety/stress (AS), anxiety-based performance deficits (APD), hostile behavior (HB), acceptability of vehicle-related distractions (AVRD), and crash propensity (CP). Structural Equation Modeling (SEM) was employed as a multivariate statistical technique to test these hypotheses.
RESULTS: The results supported the hypothesis that DFDs lead to AS among recipient drivers. DFDs and AS were further found to have positive associations with APDs. Whereas, there was a significant negative association between DFD, AS, and AVRD. As hypothesized, DFD and AS had positive associations with CP, indicating that distractions caused by aggressive behaviors leads to stress and consequently enhances crash propensity.
CONCLUSIONS: The results of this study provide a statistically sound foundation for further exploration of the distractions caused by the aggressive behaviors of fellow drivers. Further, the results of this study can be utilized by the relevant authorities to alter aggressive driving behaviors and reduce DFDs.