Visual function correlates of self-reported vision-related nighttime driving difficulties / La función visual se correlaciona con las dificultades de conducción nocturna relacionadas con la visión autoinformadas

Purpose: To investigate the visual function correlates of self-reported vision-related night driving difficulties among drivers. Methods: One hundred and seven drivers (age: 46.06 ± 8.24, visual acuity [VA] of 0.2logMAR or better) were included in the study. A standard vision and night driving questionnaire (VND-Q) was administered. VA and contrast sensitivity were measured under photopic and mesopic conditions. Mesopic VA was remeasured after introducing a peripheral glare source into the participants' field of view to enable computation of disability glare index. Regression analyses were used to assess the associations between VND-Q scores, and visual function measures. Results: The mean VND-Q score was -3.96±1.95 logit (interval scale score: 2.46±1.28). Simple linear regression models for photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index significantly predicted VND-Q score (P<0.05), with mesopic VA and disability glare index accounting for the greatest variation (21 %) in VND-Q scores followed by photopic contrast sensitivity (19 %), and mesopic contrast sensitivity (15 %). A multiple regression model to determine the association between the predictors (photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index) and VND-Q score yielded significant results, F (4, 102) = 8.58, P < 0.001, adj. R2 = 0.2224. Seeing dark-colored cars was the most challenging vision task. Conclusion: Changes in mesopic visual acuity, photopic and mesopic contrast sensitivity, as well as disability glare index are associated with and explain night driving-related visual difficulties. It is recommended to incorporate measurement of these visual functions into assessments related to driving performance.(AU)

Internet of things based smart framework for the safe driving experience of two wheelers.

Several parameters affect our brain's neuronal system and can be identified by analyzing electroencephalogram (EEG) signals. One of the parameters is alcoholism, which affects the pattern of our EEG signals. By analyzing these EEG signals, one can derive information regarding the alcoholic or normal stage of an individual. Many road accident cases around the world, including drinking and driving scenarios, which result in loss of life, have been reported. Another reason for such incidents is that riders avoid wearing helmets while driving two-wheelers. Many road accident cases involving two-wheelers, including drinking, driving, overspeeding, and nonwearing helmets, have been reported. Therefore, to solve such issues, the present work highlights the features of an intelligent model that can predict the alcoholism level of the subject, wearing of a helmet, vehicle speed, location, etc. The system is designed with the latest technologies and is smart enough to make decisions. The system is based on multilayer perceptron, histogram of oriented gradients (HoG) feature extraction, and random forest to make decisions in real time. The accuracy of the proposed method is approximately 95%, which will reduce the fatality rate due to road accidents. The system is tested under different working environments, i.e., indoor and outdoor, and satisfactory outcomes are observed.

Trends and disparities in alcohol-DWI license suspensions by suspension duration, North Carolina, 2007-2016.

PURPOSE: To examine trends and potential disparities in North Carolina (NC) driving while impaired by alcohol (alcohol-DWI) license suspensions from 2007-2016. Specific objectives included: 1) examining personal (e.g., race/ethnicity) and contextual (e.g., residential segregation) characteristics of alcohol-DWI license suspensions by suspension duration; and 2) examining trends in annual suspension rates by race/ethnicity, sex, and duration. METHODS: We linked NC administrative licensing and county-level survey data from several sources from 2007-2016. Suspensions were categorized by duration: 1 to <4 years and 4 years or longer (proxies for initial and repeat suspensions, respectively). We calculated counts, percentages, and suspensions rates (per 1,000 person-years) with 95% confidence intervals, examined trends in annual suspension rates by race/ethnicity, sex, and suspension duration. RESULTS: We identified 220,471 initial and 41,526 repeat license suspensions. Rates among males were three times that of females. 21-24-year-old (rates: 6.9 per 1,000 person-years for initial; 1.5 for repeat) and Black (4.1 for initial; 1.0 for repeat) individuals had the highest suspension rates. We observed decreases in annual initial and repeat suspension rates among males, but only in repeat suspensions for females during the study period. A substantial decrease in annual initial suspension rates was observed among Hispanic individuals relative to other racial/ethnic groups, while annual repeat suspension rates exhibited large decreases for most racial/ethnic groups. The highest overall suspension rates occurred in counties with higher proportions of the population without health insurance and with the highest levels of Black/White residential segregation. CONCLUSIONS: Potential disparities by race/ethnicity and sex existed by alcohol-DWI license suspension duration (i.e., initial vs. repeat suspensions) in NC. Contextual characteristics associated with suspensions, including a high degree of residential segregation, may provide indications of underlying structures and mechanisms driving potential disparities in alcohol-DWI outcomes.

Prevalence of and risk factors for low back pain among professional drivers: a systematic review and meta-analysis.

PURPOSE: A growing body of research indicates a correlation between occupational exposure, particularly among individuals in driving-related occupations, and the incidence of low back pain (LBP). METHODS: Databases were systematically searched, including PubMed, Embase, Web of Science, Cochrane Library, and SinoMed, from their inception through December 2023 for relevant studies of the prevalence and risk factors of LBP among professional drivers. Subsequent meta-analyses were performed utilizing Stata 17.0 and RevMan 5.4 software, while risk factor indicators were assessed using the Grading of Recommendations, Assessment, Development and Evaluation evidence quality grading system. RESULTS: A systematic review and meta-analysis comprising 19 studies involving 7,723 patients indicated that the incidence of LBP among drivers was 39% (95% confidence interval [CI] 0.20-0.57) in the past 7 days and 53% (95% CI 0.43-0.63) in the past 12 months. A subgroup analysis revealed a prevalence of 48% (95% CI 0.33-0.64) in 2005-2015 and 56% (95% CI 0.42-0.70) in 2016-2023. Among the identified factors, robust evidence highlighted age ≥ 41 years (odds ratio [OR] = 2.10; 95% CI 1.36-3.24; P = 0.0008), alcohol consumption (OR = 1.75; 95% CI 1.31-2.34; P = 0.0001), sleeping < 6 h/night (OR = 1.60; 95% CI 1.13-2.24; P = 0.007), uncomfortable seating (OR = 1.71; 95% CI 1.23-2.36; P = 0.001), improper driving posture (OR = 2.37; 95% CI 1.91-2.94; P < 0.00001), and manual handling (OR = 2.23; 95% CI 1.72-2.88; P < 0.00001) as significant risk factors for LBP. There was moderate evidence of a lack of exercise (OR = 1.78; 95% CI 1.37-2.31; P < 0.0001), working > 10 h/day (OR = 2.49; 95% CI 1.89-3.28; P < 0.00001), > 5 years' driving experience (OR = 2.12; 95% CI 1.66-2.69; P < 0.00001), a lack of back support (OR = 1.81; 95% CI 1.25-2.62; P = 0.002), high work-related pressure (OR = 2.04; 95% CI 1.59-2.61; P < 0.00001), and job dissatisfaction (OR = 1.57; 95% CI 1.23-2.01; P = 0.0003) as moderate risk factors. There was no evidence of body mass index or smoking as risk factors for LBP among professional drivers. CONCLUSION: The current evidence indicates an increasing annual trend in the prevalence of LBP among professional drivers. Factors including age ≥ 41 years, alcohol consumption, and sleeping < 6 h/night were among the 12 influential factors contributing to LBP in professional drivers. Enhancing awareness of these factors and formulating targeted preventive strategies may be beneficial.

Improved lightweight YOLOv5 based on ShuffleNet and its application on traffic signs detection.

Traffic signs detection is an important and challenging task in intelligent driving perception system. This paper proposes an improved lightweight traffic signs detection framework based on YOLOv5. Firstly, the YOLOv5's backbone is replaced with ShuffleNet v2, which simplifies the calculation complexity and reduces the parameters of backbone network. Secondly, aiming at the problem of inconspicuous traffic sign characteristics in complex road environment, we use the CA attention mechanism in this paper to improve the saliency of the object. Finally, aiming at the large-scale difference between the traffic signs and the high proportion of small objects, we design the BCS-FPN to fuse multi-scale features and improve the representation ability of the small-scale objects. The TT-100K dataset is also analyzed and the dataset is collated. We test on the collated TT-100K dataset for the improved YOLOv5 in this paper. And the results show that compared with YOLOv5s, the mAP of our algorithm is equivalent to that of YOLOv5s, and the speed is improved by 20.8%. This paper also has carried on the experiment on embedded devices, experimental results show that our framework in computing power less embedded devices has a better effect.

Reliable but multi-dimensional cognitive demand in operating partially automated vehicles: implications for real-world automation research.

The reliability of cognitive demand measures in controlled laboratory settings is well-documented; however, limited research has directly established their stability under real-life and high-stakes conditions, such as operating automated technology on actual highways. Partially automated vehicles have advanced to become an everyday mode of transportation, and research on driving these advanced vehicles requires reliable tools for evaluating the cognitive demand on motorists to sustain optimal engagement in the driving process. This study examined the reliability of five cognitive demand measures, while participants operated partially automated vehicles on real roads across four occasions. Seventy-one participants (aged 18-64 years) drove on actual highways while their heart rate, heart rate variability, electroencephalogram (EEG) alpha power, and behavioral performance on the Detection Response Task were measured simultaneously. Findings revealed that EEG alpha power had excellent test-retest reliability, heart rate and its variability were good, and Detection Response Task reaction time and hit-rate had moderate reliabilities. Thus, the current study addresses concerns regarding the reliability of these measures in assessing cognitive demand in real-world automation research, as acceptable test-retest reliabilities were found across all measures for drivers across occasions. Despite the high reliability of each measure, low intercorrelations among measures were observed, and internal consistency was better when cognitive demand was estimated as a multi-factorial construct. This suggests that they tap into different aspects of cognitive demand while operating automation in real life. The findings highlight that a combination of psychophysiological and behavioral methods can reliably capture multi-faceted cognitive demand in real-world automation research.

Audiovisual messages may improve the processing of traffic information and driver attention during partially automated driving: An EEG study.

Partially autonomous vehicles can help minimize human errors. However, being free from some driving subtasks can result in a low vigilance state, which can affect the driver's attention towards the road. The present study first tested whether drivers of partially autonomous vehicles would benefit from the addition of auditory versions of the messages presented in variable message signs (VMS), particularly, when they find themselves in a monotonous driving situation. A second aim was to test whether the addition of auditory messages would also produce an indirect effect on the driver's vigilance, improving performance on other driving subtasks not related to the message processing. Forty-three volunteers participated in a driving simulator study. They completed two tasks: (a) a VMS task, where they had to regain manual control of the car if the VMS message was critical, and (b) a car-following task, where they had to pay attention to the preceding car to respond to occasional brake events. Behavioral and EEG data were registered. Overall, results indicated that the addition of audio messages helped drivers process VMS information more effectively and maintain a higher level of vigilance throughout the driving time. These findings would provide useful information for the development of partially automated vehicles, as their design must guarantee that the driver remains attentive enough to assume control when necessary.

A qualitative study on apparent and latent contributing factors to driving errors in Iran.

Road traffic crashes (RTCs) disproportionately impact low and middle-income countries (LMICs). Current interventions in LMICs primarily target road user behavior, neglecting systemic issues and casting drivers as mere contributors. Given Iran's unique cultural, financial, and social intricacies, this study aims to explore the latent causes of RTCs, prioritizing drivers' experiences and encompassing insights from various traffic system components. Applying a qualitative approach, data were collected through 46 semi-structured interviews with 38 participants, including drivers and experts from traffic-related organizations. Content analysis identified ten themes as contributing to driving errors. Direct factors included fatigue influenced by economic and occupational conditions, distraction from internal and external sources, reckless behaviors influenced by traffic culture and environment, and Inadequate driving skills due to training. Macro-scale challenges in road infrastructure, vehicle quality, education, and accident investigations were highlighted. The lack of a centralized traffic safety authority and a predominant focus on penalizing drivers overshadowed systemic issues. The study offers valuable insights into the complex interplay of factors contributing to driving errors in LMICs, advocating for a paradigm shift towards holistic, systemic interventions beyond individual driver behavior and challenging the conventional blame-centric view associated with driving errors.

Optimization of stimulus properties for SSVEP-based BMI system with a heads-up display to control in-vehicle features.

Over the years, the driver-vehicle interface has been improved, but interacting with in-vehicle features can still increase distraction and affect road safety. This study aims to introduce brain-machine interface (BMI)- based solution to potentially enhance road safety. To achieve this goal, we evaluated visual stimuli properties (SPs) for a steady state visually evoked potentials (SSVEP)-based BMI system. We used a heads-up display (HUD) as the primary screen to present icons for controlling in-vehicle functions such as music, temperature, settings, and navigation. We investigated the effect of various SPs on SSVEP detection performance including the duty cycle and signal-to-noise ratio of visual stimuli, the size, color, and frequency of the icons, and array configuration and location. The experiments were conducted with 10 volunteers and the signals were analyzed using the canonical correlation analysis (CCA), filter bank CCA (FBCCA), and power spectral density analysis (PSDA). Our experimental results suggest that stimuli with a green color, a duty cycle of 50%, presented at a central location, with a size of 36 cm2 elicit a significantly stronger SSVEP response and enhanced SSVEP detection time. We also observed that lower SNR stimuli significantly affect SSVEP detection performance. There was no statistically significant difference observed in SSVEP response between the use of an LCD monitor and a HUD.

Driving change: Understanding consumers' reasons influencing electric vehicle adoption from the lens of behavioural reasoning theory.

The present study attempts to explore consumer-centric reasons affecting the adoption of electric vehicles (EVs) are investigated using behavioural reasoning theory (BRT). Our study is among the first to examine consumer's EV adoption intention using BRT through the integration of the reasons "for and against" electric vehicle (EV) adoption. On data of 312 urban consumers, second order confirmatory factor analysis (CFA) revealed the existence of underlying reasons and SEM helped in testing the proposed relationships. This study also investigates the interaction effect of financial incentive policy with the consumer reasons on EV adoption. Findings revealed that "reasons for" adoption are environmental concern, perceived technology, and maintenance of knowledge and "reasons against" adoption are scepticism, price, and instrumental utility. Environmental beliefs and values influence the "reasons for" consumer intentions to approve electric vehicle adoption. Financial incentives policy was found significant in dampening the impact of reasons against adoption of electric vehicle. The study delineates the strategies for strengthening the promotion of electric vehicles.

Designing a new integrated control solution for electric power steering systems based on a combination of nonlinear techniques.

An EPS system is used to improve the stability and safety of the car when steering while also simplifying the steering process. This article introduces a novel control solution for the EPS system called BSSMCPID. This algorithm combines two nonlinear techniques, BS and SMC, with the input signal corrected by a PID technique. This algorithm provides three new contributions compared to existing algorithms: reducing system errors and eliminating phase differences, ensuring stability even when exposed to external disturbances, and reducing power consumption. The system's stability is evaluated according to the Lyapunov criterion, while the algorithm's performance is evaluated based on numerical simulation results. According to the article findings, the RMS error of the steering column angle and steering motor angle values (controlled objects) is approximately zero, and the RMS error of the steering column speed and steering motor speed is about 0.01 rad/s, which is much lower than the results obtained with traditional BS and PID controllers. When the EPS system is controlled by the integrated nonlinear method proposed in this work, the output values always closely follow the reference values with negligible errors under all investigated conditions. Additionally, power steering performance increases as speed decreases or driver torque increases, which follows the ideal assisted power steering curve. In general, the responsiveness and stability of the system are always ensured when applying this algorithm.

Meta-analysis of the safety effect of electronic stability control.

OBJECTIVE: Electronic Stability Control (ESC) is a standard feature on most modern cars, due to its reported efficiency to reduce the number of crashes of several types. However, empirical studies of safety effects of ESC for passenger vehicles have not considered some methodological problems that might have inflated the effects. This includes self-selection of drivers who buy/use ESC and behavioral adaptation to the system over long time periods, but also the dominant method of induced exposure. This study aimed to investigate whether such methodological problems might have influenced the results. METHOD: A meta-analysis was undertaken to investigate whether there are systematic differences between published studies. Moderators tested included when the study was undertaken, the type of vehicle studied, the percent ESC in the sample, size of sample, the length of the study, whether matched or un-matched vehicles were studied, whether induced exposure was used, and two variants of types of crashes used as controls. RESULTS: The effects found ranged from 38% to 75% reduction of crashes for the main targets of singles, running off road and rollover crashes. However, these effects were heterogeneous, and differed depending on the methods used. Most importantly, information that could have allowed more precise analyses of the moderators were missing in most publications. CONCLUSIONS: Although average effects were large and in agreement with previous meta-analyses, heterogeneity of the data was large, and lack of information about important moderators means that firm conclusions about what kind of mechanisms were influencing the effects cannot be drawn. The available data on ESC efficiency are not unanimous, and further investigations into the effects of ESC on safety using different methodologies are warranted.

Motor Vehicle Driving-Related Anxiety in Patients Undergoing Cardioverter Defibrillator Implantation and Cardiac Resynchronization Therapy With Defibrillators.

BACKGROUND: Reducing anxiety about motor vehicle driving in patients receiving implantable cardioverter defibrillators and cardiac resynchronization therapy with defibrillators is important not only for improving quality of life but also for preventing vehicle collisions owing to driver distraction. This study aimed to clarify the driving-related anxiety of patients with these defibrillators and the factors that predict such anxiety. METHODS: We conducted a cross-sectional survey using a self-administered questionnaire of patients who had been driving a vehicle after device implantation at a general hospital between August 2018 and November 2019. RESULTS: The mean age was 60.8 ± 12.6 years. The reasons for implantation were primary prevention in 47 patients and secondary prevention in 30 patients. A total of 16 patients experienced anxiety about driving and 61 did not. Significantly more younger patients (mean age of 50.4 vs. 63.6 years, p < 0.001) and those with implantable cardioverter defibrillators had anxiety (100% vs. 73.8%, p = 0.02). Multivariable analysis indicated that age was the only independent factor that predicted driving-related anxiety (odds ratio, 0.937; 95% confidence interval, 0.883-0.993). CONCLUSIONS: Identifying and addressing driving-related anxiety in patients (particularly young patients) with defibrillators is important in preventing motor vehicle collisions and improving quality of life.

TruckSim-based study of the rollover accident mechanisms of container semitrailers on freeway interchange loop ramps.

In this study, to investigate the factors affecting container semitrailer rollovers while driving on interchange loop ramps, a simulation model was established in TruckSim on the basis of data collected from an in-depth investigation of the vehicle, road, and driving behaviors in a real accident. The established model was validated by reproducing the accident in a simulation. The effects of vehicle speed, the height of the cargo's center of gravity, tractor-semitrailer interactions, and the radius of the circular curve on container semitrailer rollover were investigated using the established model. The results reveal that if the height of the container semitrailer is within the limits in Chinese standards and the container semitrailer is moving at a speed of less than 45.3 km/h, it can safely travel through a loop ramp with a circular curve radius of 60 m. The height of the cargo's center of gravity and the lateral load transfer ratio have a negative relationship, and a higher center of gravity reduces the safe speed for a container semitrailer traveling through a loop ramp. During rollover, the rear axle of the semitrailer first begins to roll over and then drives the tractor to overturn.

The tales of two cities: use of evidence for introducing 20 miles per hour speed limits in Edinburgh and Belfast (United Kingdom).

BACKGROUND: In 2016, large-scale 20 miles per hour speed limits were introduced in the United Kingdom cities of Edinburgh and Belfast. This paper investigates the role that scientific evidence played in the policy decisions to implement lower speed limits in the two cities. METHODS: Using a qualitative case study design, we undertook content analysis of a range of documents to explore and describe the evolution of the two schemes and the ways in which evidence informed decision-making. In total, we identified 16 documents for Edinburgh, published between 2006 and 2016, and 19 documents for Belfast, published between 2002 and 2016. FINDINGS: In both cities, evidence on speed, collisions and casualties was important for initiating discussions on large-scale 20 mph policies. However, the narrative shifted over time to the idea that 20 mph would contribute to a wider range of aspirations, none of which were firmly grounded in evidence, but may have helped to neutralize opposing discourses. DISCUSSION AND CONCLUSIONS: The relationship between evidence and decision-making in Edinburgh and Belfast was neither simple nor linear. Widening of the narrative appears to have helped to frame the idea in such a way that it had broad acceptability, without which there would have been no implementation, and probably a lot more push back from vested interests and communities than there was.

IoT-Based Assessment of a Driver's Stress Level.

Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver's arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system.

Multi-Index Driver Drowsiness Detection Method Based on Driver's Facial Recognition Using Haar Features and Histograms of Oriented Gradients.

It is estimated that 10% to 20% of road accidents are related to fatigue, with accidents caused by drowsiness up to twice as deadly as those caused by other factors. In order to reduce these numbers, strategies such as advertising campaigns, the implementation of driving recorders in vehicles used for road transport of goods and passengers, or the use of drowsiness detection systems in cars have been implemented. Within the scope of the latter area, the technologies used are diverse. They can be based on the measurement of signals such as steering wheel movement, vehicle position on the road, or driver monitoring. Driver monitoring is a technology that has been exploited little so far and can be implemented in many different approaches. This work addresses the evaluation of a multidimensional drowsiness index based on the recording of facial expressions, gaze direction, and head position and studies the feasibility of its implementation in a low-cost electronic package. Specifically, the aim is to determine the driver's state by monitoring their facial expressions, such as the frequency of blinking, yawning, eye-opening, gaze direction, and head position. For this purpose, an algorithm capable of detecting drowsiness has been developed. Two approaches are compared: Facial recognition based on Haar features and facial recognition based on Histograms of Oriented Gradients (HOG). The implementation has been carried out on a Raspberry Pi, a low-cost device that allows the creation of a prototype that can detect drowsiness and interact with peripherals such as cameras or speakers. The results show that the proposed multi-index methodology performs better in detecting drowsiness than algorithms based on one-index detection.

Auditing learner driver information about floodwaters: An environmental scan of government issued resources in Australia.

INTRODUCTION: Vehicles driving, or being swept, into floodwaters is a leading cause of flood-related death. Establishing safe behaviors among learner drivers may reduce risk throughout their driving lifetime. METHODS: An environmental scan of publicly available government issued learner and driver handbooks across the eight Australian jurisdictions was conducted to identify information provided regarding floodwaters. Search terms included 'flood,' 'rain,' 'water,' and 'wet.' A visual audit of flood-related signage was also conducted. RESULTS: Twelve documents, across eight jurisdictions, were analyzed. Four jurisdictions' documents provided no information on flooding. Of the four jurisdictions that provided information, content varied. This included highlighting risks and discouraging entering floodwaters in a vehicle, including penalties associated with travel on closed roads, to advising depth and current checks if crossing a flooded roadway, with recommendations based on vehicle size (preference given to bigger vehicles, i.e., 4wds). Information on flood-related signage was found in one jurisdiction. DISCUSSION: Learner and driver handbooks represent a missed opportunity to provide flood safety information. Currently, information is not provided in all jurisdictions, despite flood-related vehicle drowning deaths of drivers and passengers being a national issue. Where information is presented, it is limited, often lacks practical guidance on how to assess water depth, current, and road base stability, and could better use evidence regarding the psychological factors underpinning, and behavioral prompts for performing, or avoiding, risky driving behavior during floods. CONCLUSIONS: The provision and content of information in learner driver and driver handbooks must be improved, particularly within the context of increasing flooding and extreme weather associated with the effects of climate change. PRACTICAL APPLICATIONS: We encourage all jurisdictions to provide practical information that draws on evidence-based risk factors and empirically established psychological factors for behavioral change to help establish safe driver behaviors around floods in the formative years of learning to drive.

Exploring the utility of a social norm approach in reducing younger drivers' engagement in hand-held phone use while driving: A qualitative study.

INTRODUCTION: Engagement in hand-held phone use while driving among young drivers is a prevalent concern in society, despite countermeasures to deter the behavior. The social norm approach has been effective in reducing negative behaviors in young adults (e.g., binge drinking, drink driving). However, whether this approach can reduce hand-held phone use while driving in this population has not been thoroughly investigated. METHOD: The qualitative study explored young drivers' attitudes and opinions on social norm messages designed to reduce hand-held phone use while driving. In addition, young drivers' opinions on current campaigns were explored to provide further insight into the effectiveness of these messages. Thirty young drivers were interviewed and shown six social norm messages. RESULTS: The data were analyzed using reflexive thematic analysis, resulting in five themes and one sub-theme: (1) Road safety messages with minimal impact on hand-held phone use while driving; (2) What constitutes an effective road safety message for hand-held phone use while driving; (3) Comparisons between social norm messages and road safety messages; (4) The potential benefits of combined social norms, (4a) Improving and optimizing the message; and (5) "It's kinda just numbers on a screen": Negative views on social norm messages. Results highlight the diverse opinions towards road safety campaigns and the need to increase exposure to these messages. Further, a combined social norm message was perceived as most effective in reducing engagement in hand-held phone use while driving. CONCLUSIONS: The current study provides preliminary evidence that the social norm approach may be effective in reducing hand-held phone use among young drivers. Further, this study highlights the need to maximize exposure to phone use while driving campaigns in this high-risk cohort. PRACTICAL APPLICATIONS: Results support the development of a social norm messaging intervention to reduce young drivers hand-held phone use while driving.