Types Of Distraction Research Articles

Soft tissue injury patterns in posteromedial rotatory instability with dislocation compared with posteromedial dislocation of the elbow joint

We sought to determine injury mechanisms and soft tissue injury patterns of dislocation caused by posteromedial rotatory instability (PMRI) and simple posteromedial (PM) dislocation of the elbow joint that appear similar on simple radiographs. In this retrospective case-series study, we reviewed 13 patients with PMRI dislocation and 10 patients with simple PM dislocation. Three-dimensional computed tomography and magnetic resonance imaging were performed in both groups. The ulnar collateral ligament, lateral collateral ligament complex (LCLC), overlying extensor muscle, and locus of bone contusion were identified. The direction of dislocation was categorized into the pure-posterior or PM type by simple radiographs. The LCLC was completely ruptured in both groups. A completely torn ulnar collateral ligament was observed in 3 patients (23%)in the PMRI dislocation group and 9 patients (90%)in the simple PM dislocation group (P = .005). Regarding injury patterns of the LCLC and overlying extensor muscle, the distraction type was found in 10 patients (77%) and the stripping type was found in 3 patients (23%) in the PMRI dislocation group, whereas all patients (100%) in the simple PM dislocation group had the distraction type (P = .103). Bone contusion was observed at the posterolateral olecranon in 2 patients (15%)in the PMRI dislocation group andat the PM olecranon in 4 patients (40%),posterolateral olecranon in 1 (10%),posterior olecranon in 1 (10%), and PM-posterolateral olecranon in 1 (10%) in the simple PM dislocation group (P = .008). In the PMRI dislocation group, 7 patients (54%) had the PM type and 6 (46%) had the pure-posterior type. Simple PM and PMRI dislocations of the elbow joint might have different soft tissue injury characteristics because of different injury mechanisms.

Prevalence of mobile phone use among drivers: direct observation in Udine (Northern Italy)

to measure the prevalence of hand-held mobile phone use among drivers in Udine, Italy. Such behaviour is prohibited by law. direct roadside observation of car drivers. groups of nursing students on the roadside observed car drivers either waiting at red traffic lights or driving in selected major urban streets of the city of Udine and of two smaller neighboring towns, in one-hour time slots in different days in November and December 2018, for a total of 24 hours of observation. prevalence of use of handheld mobile phone either for calling or for activities implying visuo-manual interaction (texting or social media browsing), both among drivers waiting at red traffic lights and among those driving along the streets. overall, 8,240 observations of vehicles were carried out. The prevalence of mobile phone use was 9.9% among drivers waiting at red traffic lights and 6.5% among those moving along the streets in Udine; the prevalence was higher in the smaller neighboring towns. Among 6,116 drivers, the type of use has also been recorded: prevalence of texting was 7.2% at traffic lights and 5.0% in moving vehicles; prevalence of phone calls was 3.3% and 3.6%, respectively. Texting represented 68.4% of mobile phone use among drivers waiting at traffic lights and 57.8% among moving drivers. Behaviors of female and male drivers were similar. the use of mobile phones among drivers is very common in Udine, particularly for activities involving both the hands and the eyes. Since the increased risk of road accidents caused by this type of distraction from driving activities is well known, interventions discouraging the use of mobile phones among drivers are needed.

Detection of driver manual distraction via image-based hand and ear recognition

Driving distraction is a leading cause of fatal car accidents, and almost nine people are killed in the US each day because of distracting activities. Therefore, reducing the number of distraction-affected traffic accidents remains an imperative issue. A novel algorithm for detection of drivers’ manual distraction was proposed in this manuscript. The detection algorithm consists of two modules. The first module predicts the bounding boxes of the driver's right hand and right ear from RGB images. The second module takes the bounding boxes as input and predicts the type of distraction. 106,677 frames extracted from videos, which were collected from twenty participants in a driving simulator, were used for training (50%) and testing (50%). For distraction classification, the results indicated that the proposed framework could detect normal driving, using the touchscreen, and talking with a phone with F1-score 0.84, 0.69, 0.82, respectively. For overall distraction detection, it achieved F1-score of 0.74. The whole framework ran at 28 frames per second. The algorithm achieved comparable overall accuracy with similar research, and was more efficient than other methods. A demo video for the algorithm can be found at https://youtu.be/NKclK1bHRd4.

Machine Learning and End-to-End Deep Learning for Monitoring Driver Distractions From Physiological and Visual Signals

It is only a matter of time until autonomous vehicles become ubiquitous; however, human driving supervision will remain a necessity for decades. To assess the driver's ability to take control over the vehicle in critical scenarios, driver distractions can be monitored using wearable sensors or sensors that are embedded in the vehicle, such as video cameras. The types of driving distractions that can be sensed with various sensors is an open research question that this study attempts to answer. This study compared data from physiological sensors (palm electrodermal activity (pEDA), heart rate and breathing rate) and visual sensors (eye tracking, pupil diameter, nasal EDA (nEDA), emotional activation and facial action units (AUs)) for the detection of four types of distractions. The dataset was collected in a previous driving simulation study. The statistical tests showed that the most informative feature/modality for detecting driver distraction depends on the type of distraction, with emotional activation and AUs being the most promising. The experimental comparison of seven classical machine learning (ML) and seven end-to-end deep learning (DL) methods, which were evaluated on a separate test set of 10 subjects, showed that when classifying windows into distracted or not distracted, the highest F1-score of 79% was realized by the extreme gradient boosting (XGB) classifier using 60-second windows of AUs as input. When classifying complete driving sessions, XGB's F1-score was 94%. The best-performing DL model was a spectro-temporal ResNet, which realized an F1-score of 75% when classifying segments and an F1-score of 87% when classifying complete driving sessions. Finally, this study identified and discussed problems, such as label jitter, scenario overfitting and unsatisfactory generalization performance, that may adversely affect related ML approaches.

A Cascaded Multimodal Natural User Interface to Reduce Driver Distraction

Natural user interfaces (NUI) have been used to reduce driver distraction while using in-vehicle infotainment systems (IVIS), and multimodal interfaces have been applied to compensate for the shortcomings of a single modality in NUIs. These multimodal NUIs have variable effects on different types of driver distraction and on different stages of drivers' secondary tasks. However, current studies provide a limited understanding of NUIs. The design of multimodal NUIs is typically based on evaluation of the strengths of a single modality. Furthermore, studies of multimodal NUIs are not based on equivalent comparison conditions. To address this gap, we compared five single modalities commonly used for NUIs (touch, mid-air gesture, speech, gaze, and physical buttons located in a steering wheel) during a lane change task (LCT) to provide a more holistic view of driver distraction. Our findings suggest that the best approach is a combined cascaded multimodal interface that accounts for the characteristics of a single modality. We compared several combinations of cascaded multimodalities by considering the characteristics of each modality in the sequential phase of the command input process. Our results show that the combinations speech + button, speech + touch, and gaze + button represent the best cascaded multimodal interfaces to reduce driver distraction for IVIS.

Quality and Accuracy of Information Available on Websites for Distracted Driving: Qualitative Analysis.

BackgroundDistracted driving has become alarmingly widespread, and its prevalence continues to increase despite efforts by government and nongovernment organizations to educate the public about this pervasive problem. Every year, 1.35 million people die, and nearly 80 million people get injured in road traffic incidents. Motor vehicle crashes are the leading cause of death among young people, and distracted driving plays a huge role in road traffic fatalities and injuries. Considering that most people now use the internet as an information source and Google is the most visited website and number one online search engine in the world, we performed a qualitative analysis of information available through Google on distracted driving and its outcomes.ObjectiveThe goal of this study was to analyze the quality and accuracy of the information on distracted driving and its consequences available to the general public when using Google as a search engine for distracted driving.MethodsIn November 2018, a nonregional Google search on distracted driving was conducted. The first two pages of the Google search results were selected for analysis. Data were collected on the type of website, type of distraction, consequences of distracted driving described, presence and referencing of statistics, and orthopedic and nonorthopedic injuries described, with their acute and chronic sequelae.ResultsIn total, we analyzed 25 websites: 12 websites (48%) were from government bodies, which were the most common type of websites; 19 (76%) of the sites provided statistics; and 15 (60%) referenced the source of the statistic. Mobile phones were the most frequently cited type of distraction, with 17 (68%) sites discussing it, while death was the most commonly mentioned consequence of distracted driving, quoted in 15 (60%) of the websites. Additionally, 52% of the sites provided tips on how to avoid distracted driving. Only one website mentioned orthopedic injuries.ConclusionsThe prevalence of distracted driving is increasing, and so are the consequences associated with it. Nevertheless, the information available online does not accurately describe the current circumstances regarding this issue. The National Highway Traffic Safety Administration attributed 391,000 injuries and 3477 deaths to distracted driving in 2015, which are 5000 more injuries and almost 150 more fatalities compared to 2011. However, despite these figures, most of the websites discussed death as a consequence of distracted driving and often overlooked injuries, even though injuries are over 100 times more likely to occur in distraction-affected crashes. The websites also largely fail to address other forms of driving distractions, like daydreaming or talking to a passenger, and mostly focus on mobile phone–related activities as distractions. More specific information on the dangers of distracted driving and nonlethal trauma may support an overall cultural shift to curb this behavior.

Performance Degradation During Sudden Hazardous Events: A Comparative Analysis of Use of a Phone and a Music Player During Driving

The present study attempts to statistically model and quantify the contribution of two sets of potential factors to accident risk: 1) real-time driving behavioral characteristics and 2) distraction conditions. The effects of phone and music player usage on reaction times and accident probabilities were modeled for two events: 1) a pedestrian crossing (started in peripheral vision) and 2) sudden braking (started in central vision) in an urban scenario. In total, 90 subjects drove for baseline and conversation conditions. In all, 70 and 78 of the 90 subjects drove for texting and music player tasks, respectively. Reaction times and accident probabilities were modeled with the help of generalized linear mixed model approach. The results showed that the reaction time increased by 42%, 113%, and 62% due to the presence of conversation, texting, and music player operations, respectively, for the pedestrian crossing event. Whereas, the increments were lower for the sudden braking event. Drivers with higher approach speed detected the events faster. The accident probability increased due to the texting tasks and by operating the player during driving. An increase in reaction time also led to higher risk for the pedestrian crossing event. For the sudden braking event, the effect of music-listening was not significant, but the accident risk increased if the driver approached the event at high speed. Overall, the results suggest that the algorithm for designing a collision warning system should consider the real-time driving characteristics based on event type and quantify the effects of distraction based on the type of distraction.

Consolidation Time and Relapse: A Systematic Review of Outcomes in Internal versus External Midface Distraction for Syndromic Craniosynostosis.

The choice between internal and external distraction osteogenesis for midface advancement in patients with syndromic craniosynostosis is based primarily on surgeon preference and expertise. However, differences in outcomes between the two techniques have been sparingly compared. In this work, the authors performed a systematic review to compare outcomes between internal versus external midface distraction. A systematic review was performed of studies published between 1998 and 2018 (61 studies included; n = 689 patients). Operative characteristics, early reoperations, complications, and relapse rates were extracted. Bias evaluation was performed using the Newcastle-Ottawa instrument. Statistical analyses were performed with independent samples t tests and linear regression analyses (p < 0.05 considered significant). The authors found that external distraction was associated with more Le Fort III osteotomies and hardware adjustments (p = 0.023), whereas internal distraction was associated with more monobloc osteotomies and longer consolidation times (p = 0.008). No significant differences in the distance of midface advancement, reoperations, complications, or relapse rates were noted between internal versus external distraction, although external distraction trended toward a slightly higher relapse rate. Regardless of distraction protocol, consolidation time was found to be a strong negative predictor for relapse (beta = -0.792; p = 0.02). No significant differences were demonstrated in advancement distance, reoperative rates, complication rates, or relapse rates for internal versus external distraction for midface advancement. Regardless of distraction type, consolidation time was strongly inversely associated with relapse rates. The trend toward higher relapse in external distraction is potentially explained by the significantly lower consolidation times.

Detecting Driver’s Fatigue, Distraction and Activity Using a Non-Intrusive Ai-Based Monitoring System

Abstract The lack of attention during the driving task is considered as a major risk factor for fatal road accidents around the world. Despite the ever-growing trend for autonomous driving which promises to bring greater road-safety benefits, the fact is today’s vehicles still only feature partial and conditional automation, demanding frequent driver action. Moreover, the monotony of such a scenario may induce fatigue or distraction, reducing driver awareness and impairing the regain of the vehicle’s control. To address this challenge, we introduce a non-intrusive system to monitor the driver in terms of fatigue, distraction, and activity. The proposed system explores state-of-the-art sensors, as well as machine learning algorithms for data extraction and modeling. In the domain of fatigue supervision, we propose a feature set that considers the vehicle’s automation level. In terms of distraction assessment, the contributions concern (i) a holistic system that covers the full range of driver distraction types and (ii) a monitoring unit that predicts the driver activity causing the faulty behavior. By comparing the performance of Support Vector Machines against Decision Trees, conducted experiments indicated that our system can predict the driver’s state with an accuracy ranging from 89% to 93%.

S6B-15 SESSION 6B: HFM ROBIN - PART I LONG-TERM ORTHOGNATHIC CONSIDERATIONS IN THE PIERRE ROBIN SEQUENCE PATIENT

Introduction: Pierre Robin Sequence (PRS) is characterized by the presence of micrognathia, glossoptosis, and airway obstruction. While syndromic children may require multiple procedures to address airway obstruction and malocclusion, one of the controversies in non-syndromic PRS is the potential for “catch-up” mandibular growth equivalent to children without craniofacial diagnoses. Here, we evaluate long-term requirements of orthognathic surgery at skeletal maturity in PRS patients. Methods: 115 children diagnosed with PRS were evaluated from institutions. Children ≥13 years of age were included and patients who underwent any type of mandibular distraction or surgery prior to skeletal maturity were excluded. Demographics, surgical, and orthodontic histories were reviewed. Descriptive statistics were reported. Chi-square was used to compare groups. Results: 43 patients with PRS met inclusion criteria (57% female; mean age: 20 ± 3.3 years). Mean length of follow up was 14.2 ± 5.2 years. 18 patients (41%) were diagnosed as syndromic, the majority of which was Stickler Syndrome. 41 patients (95%) had a history of a cleft palate and of those patients, 18 (44%) had velopharyngeal insufficiency necessitating surgery. Data was available for 42 patients in regards to orthodontic and orthognathic care. Orthodontic evaluation revealed that 41 patients (98%) had a history of orthodontic treatment and 20 (48%), 17 (40%), and 5 (12%) were classified as Angle Class I, II, and III, respectively. Subset analysis revealed that 13 and 4 patients with a syndromic diagnosis were Class I and class II, respectively, while 1 was class III (p<0.05). A total of 17 (39.5%) patients underwent or were recommended to undergo orthognathic surgery due to Class II (n=11/17, 65%) or Class III (n=3/17, 18%) malocclusion. When PRS children with surgical Class II malocclusion were separated into nonsyndromic versus syndromic subgroups, 9/11 (80%) were nonsyndromic and 2/11 (18%) were syndromic. Overall, 9/25 (36%) of nonsyndromic and 2/18 (11%) of syndromic PRS children required orthognathic surgery to specifically address maxillomandibular and occlusal abnormalities (p=0.09). Conclusion: The presented study suggests deficient long-term mandibular growth of nonsyndromic PRS children is relatively similar to that of children with syndromic PRS. These findings lie in contrast to the concept of mandibular “catch-up” growth and suggest that the maxillomandibular relationship in PRS may not resolve without intervention.

“Texting &amp; Driving” Detection Using Deep Convolutional Neural Networks

The effects of distracted driving are one of the main causes of deaths and injuries on U.S. roads. According to the National Highway Traffic Safety Administration (NHTSA), among the different types of distractions, the use of cellphones is highly related to car accidents, commonly known as “texting and driving”, with around 481,000 drivers distracted by their cellphones while driving, about 3450 people killed and 391,000 injured in car accidents involving distracted drivers in 2016 alone. Therefore, in this research, a novel methodology to detect distracted drivers using their cellphone is proposed. For this, a ceiling mounted wide angle camera coupled to a deep learning–convolutional neural network (CNN) are implemented to detect such distracted drivers. The CNN is constructed by the Inception V3 deep neural network, being trained to detect “texting and driving” subjects. The final CNN was trained and validated on a dataset of 85,401 images, achieving an area under the curve (AUC) of 0.891 in the training set, an AUC of 0.86 on a blind test and a sensitivity value of 0.97 on the blind test. In this research, for the first time, a CNN is used to detect the problem of texting and driving, achieving a significant performance. The proposed methodology can be incorporated into a smart infotainment car, thus helping raise drivers’ awareness of their driving habits and associated risks, thus helping to reduce careless driving and promoting safe driving practices to reduce the accident rate.

A Machine-Learning Approach to Distinguish Passengers and Drivers Reading While Driving.

Driver distraction is one of the major causes of traffic accidents. In recent years, given the advance in connectivity and social networks, the use of smartphones while driving has become more frequent and a serious problem for safety. Texting, calling, and reading while driving are types of distractions caused by the use of smartphones. In this paper, we propose a non-intrusive technique that uses only data from smartphone sensors and machine learning to automatically distinguish between drivers and passengers while reading a message in a vehicle. We model and evaluate seven cutting-edge machine-learning techniques in different scenarios. The Convolutional Neural Network and Gradient Boosting were the models with the best results in our experiments. Results show accuracy, precision, recall, F1-score, and kappa metrics superior to 0.95.

Outcome of fractures of dorso lumbar spine treated by short segment posterior stabilization with intermediate pedicle screws

&lt;p class="abstract"&gt;&lt;strong&gt;Background:&lt;/strong&gt; Spine fractures are common in today’s world due to high frequency of motor vehicle accidents and work place injuries. These are major cause of disability in adult population. The mortality rate following spinal injuries is 7%. The aim of the study was to study the functional and radiological outcome of fractures of dorso lumbar spine treated by short segment posterior stabilization with intermediate pedicle screws.&lt;/p&gt;&lt;p class="abstract"&gt;&lt;strong&gt;Methods:&lt;/strong&gt; Dorso-lumbar fractures with intact pedicle on the fractured segment, Load sharing classification score of equal or less than 6, Neurologic involvement caused by the fracture, loss of vertebral body height by more than 50% and kyphosis angle more than 20° are included. Patients with multiple level fractures and pathological fractures were excluded from the study. Denis classification and AO classification were used. Load sharing score is used to decision making for intermediate screw fixation.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p class="abstract"&gt;&lt;strong&gt;Results:&lt;/strong&gt; L1 is more frequently fractured followed by D12. Distraction type (AO) and burst (Denis) are most common types. 4 of our patients had complete neurological deficit. 15 had incomplete deficit and 11 patients doesn’t have any neurological involvement. Frankel A grade cases remained in the same grade. Mean Kyphotic correction is 6.7°. Mean AVBCP in the postoperative cases 26. None of the cases developed kyphosis or loss of correction in the follow up. Outcome using Roland Morris disability questionnaire is excellent in 64.3%, good in 21.6% and poor in 14.3% cases.&lt;/p&gt;&lt;p class="abstract"&gt;&lt;strong&gt;Conclusions:&lt;/strong&gt; To conclude that short segment posterior stabilisation with intermediate screws provides better biomechanical stability when compared with conventional short segment fixation.&lt;/p&gt;

Modeling Vehicle Collision Injury Severity Involving Distracted Driving: Assessing the Effects of Land Use and Built Environment

This paper presents the findings of the vehicle occupant injury severity model, particularly focusing on the collisions involving distracted driving. The study develops a latent segmentation-based logit model for analyzing crash injury severity utilizing police-reported collision data from 2007 to 2011 in Nova Scotia, Canada. A segment allocation model is estimated to capture latent heterogeneity based on individual victims’ and drivers’ profiles, and collision attributes including vehicle type, vehicle trajectory, collision object, and collision type. The segment allocation model results suggest the existence of high-risk and low-risk injury severity segments. This study extensively tests the effects of built environment characteristics. The model results suggest that rain, curved road, freeway, and mid-block collisions aggravate vehicle occupant injury severity; whereas, higher land use mix, longer length of sidewalk, and higher population density mitigate injury severity. Significant heterogeneity is found across the high- and low-risk segments. For instance, straight road alignment is found to yield higher injury severity in the high-risk segment and lower severity in the low-risk segment. Moreover, the model unveils the interplay between built environment and distraction type. Driver distraction by communication device increases injury severity at a curved road intersection. Additionally, distraction because of inattentiveness increases injury severity. The findings of this study assist road safety engineers and planners to identify effective countermeasures and awareness programs for reducing the crash injury severity or consequences for vehicle occupants.

Guidelines for mandibular uniplanar & biplanar distraction osteogenesis in tmj ankylosis

Background: Mandibular asymmetry secondary to TMJ Ankylosis (TMJA) presents with varying magnitude and morphology. Distraction Osteogenesis (DO) has been used for such severe deformities but no specific guidelines have been laid as to the type of distraction, sequence as regards release, site of osteotomy, callous moulding.etc.

Use of distraction techniques for the management of anxiety and fear in paediatric dental practice: A systematic review of randomized controlled trials

To determine whether distraction techniques (DT) reduce children's/adolescent's anxiety and fear during dental treatment. Randomized controlled trials (RCTs) in which any type of DT were used to manage dental anxiety and dental fear in children/adolescents were included. A systematic search of PubMed, Web of Science, Scopus, Cochrane Library, Lilacs, and Google Scholar was conducted. Two independent reviewers selected studies, extracted data, assessed methodological quality of studies using the Cochrane Collaboration's Risk of Bias tool (CCRBT), and approached certainty of evidence using GRADE (Grading of Recommendations, Assessment, Development and Evaluation). Data were analysed descriptively. Twenty studies covering several types of DT (audio, audiovisual, instruments camouflage, biofeedback, dental operating microscope, toys) were included. Qualitative analysis showed with very low certainty of evidence that DT effectively reduced dental anxiety and fear depending on the distraction type, instrument used to measure dental anxiety and dental fear, and dental procedure. CCRBT evaluation identified many methodological issues in included studies. There is a very low certainty of evidence that DT can be effective in managing children's/adolescents' dental fear and anxiety during dental treatment. The heterogeneity of methodologies and findings in the studies, however, suggests more robust, and well-executed RCTs are needed.

Video-Based Driver’s Hand Tracking using Fast Normalized Cross Coefficient with Improved Computational Efficiency

Driver distraction is one of the major causes for fatal car accidents. In a distracting activity, manual distraction is a triggered response of other types of distraction, such as cognitive and visual distraction. Therefore, recognition of manual distraction can contribute to the monitoring of overall drivers’ distraction. In this study, a computer vision-based method to track hand movement from the recorded driving behavior is proposed. This method integrates a low computational cost template matching algorithm using fast normalized cross coefficient (NCC) and a novel searching strategy. The proposed method was evaluated by the VIVA hand tracking data set. It achieves 50.83% of marginal accuracy percentage (mAP), 42.18% of multiple object tracking accuracy (MOTA), 31.56% of mostly tracked (MT), and 19.29% of mostly lost (ML), and it outperformed a state of the art algorithm in MOTA and MT. Additionally, the computational cost of the proposed method is greatly improved, and it can run at around 11.1 frames per second. The outcome of this research will further assist driving distraction recognition and mitigation, and improve driving safety.

Recognizing Distractions for Assistive Driving by Tracking Body Parts

Busy life as well as the prevalence of infotainment is increasingly making people more occupied even during tasks that require serious attention. One such task is driving and at the same time getting involved in activities that may distract drivers cognitively from watching the road and cause fatal accidents. This paper presents a method that is capable of monitoring different types of distractions, such as talking and texting on cell phone, casual eating, and operating cabin equipment while driving, so that a driver can be assisted to remain cautious on the road. The proposed method automatically detects and tracks fiducial body parts of a driver from video captured by a camera mounted on the front windshield inside a vehicle. Relative distances between the tracking trajectories are used as features that represent actions of the driver. Then, the well-known kernel support vector machine is applied for recognizing a particular distraction from the features extracted from body parts. The proposed feature is also compared with previously employed features for tracking-based human action recognition schemes to substantiate its better result in terms of mean accuracy and robustness for distraction recognition. The effectiveness of the proposed method of distraction recognition is also analyzed with respect to tracking errors.

Modelling the Effect of Mobile Phone Use on Driving Behaviour Considering Different Use Modes

Mobile phone use while driving is a major cause of driver distraction, affecting driving performance and increasing accident risk. Governments have responded to this with the implementation of legislation prohibiting the use of mobile phones, under specific conditions, mainly adopting the hands-free use. Still, mobile phone is a cause of several types of distraction rather than just manual. This study explores the effect of mobile phone use while driving via a simulator experiment. Participants drive under various types of mobile phone use mode- namely, handheld, hands-free (wired earphone), and speaker to capture this effect. Results highlight the effect of mobile phone use, regardless of the use mode, on driving behaviour through specific indicators: maximum driving speed, reaction time, and lateral position. In particular, considering the aforementioned parameters the handheld mode demonstrates safer driving behaviour compared to the speaker mode. The results of this study stress the need for a reconsideration of the present legislation.

The long road home from distraction: Investigating the time-course of distraction recovery in driving

Driver distraction is a leading cause of accidents. While there has been significant research examining driver performance during a distraction, there has been less focus on how much time is required to recover performance following a distraction. To address this issue, participants in the current study completed a simulated 40-min drive while being presented with distractions. Distractions were followed by a visual Detection Response Task (DRT) to assess participants’ resource availability and potential capacity to respond to hazards, as well as continuous measures of driving performance including their ability to maintain a consistent speed and lane position. We examined recovery for a 40 s period following three types of distraction: cognitive only, cognitive + visual, and cognitive + visual + manual. Since safe driving requires cognitive, visual, and manual resources, we expected recovery to take longer when the distraction involved more of these resources. Consistent with this, each additional level of distraction further slowed DRT response times and increased speed variability during 0–10 s post-distraction. However, DRT accuracy was equally impaired for all conditions during 0–20 s post-distraction, while lane position maintenance from 0 to 10 s post-distraction was only impaired when the distraction included a manual component. In addition, while participants in all three conditions exhibited some degree of post-distraction impairment, only those in the cognitive + visual + manual condition reduced their speed during the time when distracted, suggesting drivers show limited awareness of the potential persistent consequences of distraction.