Driver Cell Phone Research Articles

Drivers Glance Like Lizards during Cell Phone Distraction in Assisted Driving

Head pose has been proposed as a surrogate for eye movement to predict areas of interest (AOIs) where drivers allocate their attention. However, head pose may disassociate with AOIs in glance behavior involving zero or subtle head movements, commonly known as “lizard” glance pattern. In contrast, “owl” glance pattern is used to describe glance behavior along with larger head movements. It remains unclear which glance pattern is prevalent during driver cell phone distraction and what are appropriate metrics to detect such distraction. To address this gap, we analyzed the gaze direction and head pose of 36 participants who completed an email-sorting task using a cell phone while driving a Tesla on the test track in Autopilot mode. The dispersion-threshold algorithm identified driver gaze fixations and synchronized them with head movements. The results showed that when using a cell phone either near the lap or behind the steering wheel, participants exhibited a dominant lizard-type glance pattern with minimal shift in head position. As a result, head pose alone may not provide sufficient information for cell phone distraction detection, and gaze metrics should be involved in enhancing this application.

Vision Based Detection of Driver Cell Phone Usage and Food Consumption

Distracted driving is a problem which yearly causes a large amount of road traffic crashes with high rates of fatalities and injured persons. Recently, car manufacturers started to integrate driver monitoring systems to detect visual distraction. This paper proposes a method to extend such systems by driver posture classification to detect driver cell phone usage and food consumption. Such an extension can be beneficial since systems that focus on the detection of visual distraction mainly rely on head pose and gaze information. Thus, distraction caused by cell phone usage or food consumption can not be detected by these systems when the driver is looking to the road ahead. To robustly detect those types of manual and cognitive distraction, different deep learning models were trained and evaluated based on a new image dataset which was captured by two infrared cameras to ensure that a large range of head angles can be covered by the system. Separate Convolutional Neural Networks (CNNs) were trained and evaluated for the dataset of the left and the right camera to optimize the classification accuracy. The trained CNNs revealed a competitive test accuracy of 92.88% and 90.36% for the left and the right camera, respectively. In inference mode, the models achieve a frame rate of 44Hz and 28Hz for the left and the right camera, respectively. The combination of the classification output of both networks revealed a test accuracy of 92.54%.

Driver Cell-phone Use Detection Based on CornerNet-Lite Network

Driver requires a high level of concentration when driving at high speed. Cell-phone use while driving can cause serious problems. To solve this issue, this paper proposes a proposal scheme about driver cell-phone use detection based on CornerNet-Lite Network. The scheme can eliminate the traffic accident risk caused by cell-phone use through detecting driver cell-phone use. The scheme includes two stages: model training and simulation test. Firstly, the data set of cell-phone use was established. Secondly, the data set was preprocessed by the preset processing method. Finally, the CornerNet-Lite network architecture was optimized to reduce the training time and improve the detection accuracy and real-time detection. Through a large number of experiments, the results showed that the scheme had a good detection effect, with the accuracy of 86.2% and with 30fps. Under the noise interference of simulated cab environment, it still had a high robustness.

Characteristics of driver cell phone use and their influence on driving performance: A naturalistic driving study

Cell phone use while driving is becoming a key problem in traffic safety as it causes visual-manual distraction and has been linked to increases in crash rates. The use of hand-held phones has been banned in several countries, yet research comparing the safety of hands-free phone use with hand-held has produced inconsistent results. Analysis of specific phone use characteristics could help move this traffic safety problem toward a solution, but few studies have considered the influence on driving performance of specific sets of phone use characteristics in combination with other factors such as driving context and driver demographic characteristics. The main objective of this paper is therefore to identify and analyze these factors to determine their effects on driving performance indicators such as speed changes. To this end, 1244 phone events were collected from 52 drivers from the Shanghai Naturalistic Driving Study (SH-NDS), the first naturalistic driving data in China. Because subtasks within a phone event may cause different visual-manual distractions, a hierarchical coding structure for phone events was built. A total of 5662 eyes-off-road (EOR) cases and 4237 subtasks were extracted. The results showed that on average, the participating drivers used the phone for 6.08 % of their driving time; for 17 % of phone use time, drivers used both hands to manipulate the phone; and their average EOR time was 3.16 s, which is equivalent to driving blindly for 22.82 m at an average speed of 7.22 m/s, or 26 km/hr. The effect of phone use on driving performance, including speed, headway, and lane offset, was analyzed with ANOVA. Results showed that standard deviations (SD) of all three parameters were significantly lower during phone periods than during baseline periods. The speed SD during phone use was 0.95 lower than baseline, the headway SD was 2.48 lower, and the absolute lane offset SD was 685.72 lower than baseline. These lower SDs indicate that drivers operated their vehicles with less fluctuation during phone use. While we were unable to find similar differences in mean speed and mean headway, mean lane offset was also significantly lower with phone use than without. A decision tree was developed to identify the factors influencing driver speed change. Results showed that drivers increased, decreased, or maintained speed depending on the type and duration of phone tasks, the duration of the trip, and the type of roadway. Greater understanding of the specific aspects of phone use and their influences on driver distraction and performance will permit the development of more effective countermeasures, including legislation, enforcement, blocking technologies, social norms education, and sending Do Not Disturb messages to callers, all of which will be required to mitigate continued deaths and injuries from phone use while driving.

A Deep-Learning-Based Scheme for Detecting Driver Cell-Phone Use

Cell-phone use while driving results in potentially severe safety hazards. In this paper, a scheme for detecting cell-phone use that is based on deep learning is proposed, which can eliminate the potential risk by detecting the driver behavior and issuing an early warning. The proposed scheme consists of two stages: model training and practical testing. In the former, a multi-angle arrangement of cameras is first designed. Then, based on self-established data set, two independent convolutional neural networks (CNNs) are trained by optimizing the size and number of the convolution kernels, which can efficiently recognize cell-phones and hands in real time. In the testing stage, dynamic region extraction and skin color detection are employed as preprocessing to improve the accuracy of target recognition. Then, with the trained CNNs, the detection of cell-phone and hand targets is carried out, and the corresponding early warning is issued based on the distance of the interaction between the cell-phone and the hand. Numerous experiments are conducted and the results demonstrate that the proposed scheme can accurately detect cell-phone use during driving in real time, with a running time of 144 fps and an accuracy of 95.7%.

Teen driver licensure provisions, licensing, and vehicular fatalities

Between 1996 and 2015, vehicular fatalities per capita involving 16- to 17-year-old drivers declined by 68.7%. During this same period, states enacted teen driver licensure provisions in an á la carte fashion, now collectively referred to as graduated driver licensing (GDL) programs, that restricted teen driving. While the literature demonstrates that ‘good’ GDL programs reduce vehicular fatalities, how these reductions occur remains open. In this study, separate GDL provisions and no pass, no drive laws are studied to understand reduction mechanisms. The evaluation is based on a state-by-year panel and uses difference-in-difference and triple-difference specifications to identify causal impacts on rates of licensing, vehicular fatalities, and fatalities per licensee.The empirical results find that the minimum intermediate licensing age of 16.5 or older provision reduces licensing of 16- to 17-year-old teens by 20.1%, and no other licensure provision consistently impacts licensing. In addition, vehicular fatalities decrease from the minimum intermediate licensing age of 16.5 or older provision by 22.7%, the driver’s education reduces supervised hours provision by 5.9%, and no pass, no drive laws by 7.3%, while vehicular fatalities increase from the supervised driving hours required provision by 6.3%. Furthermore, only teen driver cellphone or texting bans have impacts on vehicular fatalities per 16- to 17-year-old licensed female and few long-term impacts are identified on those ages 18–20 who ‘graduated’ from licensing programs. This research suggests that GDL programs affect vehicular fatalities mostly through incapacitation, rather than programmatically.

Quantifying driver cell phone use at signalized intersections using software-defined radio

Objective: This article outlines a data collection process that quantifies driver cell phone use using a software-defined radio (SDR) at a signalized intersection. Cell phone use while driving has been shown to be factor that increases the risk of a crash incident. Both operational and enforcement strategies can be applied at locations where high driver cell phone use is identified.Methods: A baseline driver cell phone use observation was made at the intersection, where 9,699 vehicles were observed at the intersection of Carlton Road and State Route 31 (Pennington Road) in Ewing, New Jersey. An SDR cell phone detection device created as part of this study was then deployed at the same intersection to determine whether the SDR device could detect an active cell phone signal. The identification of vehicle cell phone activity using the SDR was conducted a sample of 4,000 vehicles. A visual observation, along with a motion detection camera, was made alongside the SDR to visually confirm cell phones use.Results: Of the 4,000 vehicles sampled using the SDR cell phone detection device, 6.1% of the a.m. peak travel time and 7.6% of the p.m. peak travel time had an active cellular device. A concurrent visual field verification of driver cell phone use showed that approximately 57% (a.m. peak) and 67% (p.m. peak) of the SDR-detected cell phones were visually confirmed to be associated with distracted cell phone use.Conclusions: Once characterized, the frequency of driver cell phone use can be used to justify changes to signal timing protocols. These adjustments could include extending the signal’s “all-red time” or holding “yellow time” longer in order to properly clear the intersection. These data can also be used to identify locations that may require more enforcement measures to dissuade driver cell phone use. Furthermore, the impact of anti–cell phone campaigns or new laws can be quantified by measuring before and after cell phone use in the near term rather than waiting for crash studies at intersections to be completed and analyzed.

Editorial: What Can We Expect from Data Scientists

Data Scientist - The New ProfessionData scientist is probably the most trendy job in Information Technology (IT) nowadays. This new profession emerged with the Big Data wave. Even though there is no such thing like an exact job profile, we expect that the data scientist can handle all the Big Data challenges that are novel to us. Without being a magician she or he shall help to deliver us all the magic Big Data promises. The data scientist capitalizes on unstructured data without taking the roles of a programmer, database expert, statistician, or content manager. All these professions are around for decades. So, why invent a new one?More than anything, what data scientists do is make discoveries while swimming in data [...] they are able to bring structure to large quantities of formless data and make analysis possible. [3] They sketch, orchestrate, and control the discovery process. The leading paradigm in this process it to find information that meets a certain need or provides an answer for a certain problem. need to avoid the temptation of following a data-driven approach instead of a problem-driven one. [5] The data scientist has to develop an idea about the required information that meets our information need. We can expect that data scientists have a deep understanding of the foundations of both, the nature of information and the domain. They follow a mental model on the information demand that abstractly reflects the facts they expect to encounter in data and the way to detect and present them to us. We expect the data scientist to discover novel information that may provide us with new insights. And we want these insights to be true. It is thus part of the data scientist's responsibility to make sure that the discovered information is not only novel but also trustworthy. The data scientists cannot prove data analysis models. That exceeds their capabilities. We cannot hold them liable for information that eventually turns out to be wrong. Nevertheless their skills should include a sound sensation of plausibility that helps them to raise doubts and to prompt a closer look when the results of data analysis seem too questionable to them. However, separating questionable results from plausible ones is a task that is far from being trivial.Separating Data Science from Data FictionWhen driving a car we often encounter these nice roadside signs that sometimes make us realize that we're driving too fast. Furthermore, on some occasions there is official personnel not far from these signs measuring our speed, explaining to us our traffic infraction face-to-face and documenting it on a speeding ticket. Doesn't this sound a bit outdated? There are enough sensors in a car that measure location, speed, time, and more. Even if the car's sensors don't measure those, the driver's cellphone can do it. Combining this sensor information with the cartographic data about roads and speed limits we can easily imagine that, by the end of day, the car or the phone exactly knows every infraction we committed and can automatically trigger the issuing of speeding tickets. Aren't there just legal aspects that hamper this technically feasible scenario making its way into reality?One trait of Big Data is the availability of sensor data and their combination with already available data generating new information, like the detection of traffic infraction. We can extrapolate this scenario in many directions, including more types of infraction, driving for an irresponsibly long time without a substantial break, or detecting patterns of aggressive driving. We can extend it also towards future possibilities if we think about sensor information from smart watches indicating a possibly problematic pulse rate or from the car's air control sensor that the driver may drive under the influence of alcohol.Much like in Data Mining, the strength of Big Data originates from the combination of facts producing new information. …

Passenger Compartment Violation Detection in HOV/HOT Lanes

Due to the high volume of traffic on modern roadways, transportation agencies have proposed high occupancy vehicle (HOV) and high occupancy tolling (HOT) lanes to promote carpooling. Enforcement of the rules of these lanes is currently performed by roadside enforcement officers using visual observation. Officer-based enforcement is, however, known to be inefficient, costly, potentially dangerous, and ultimately ineffective. Violation rates up to 50%–80% have been reported, whereas manual enforcement rates of less than 10% are typical. Near-infrared (NIR) camera systems have been recently proposed to monitor HOV/HOT lanes and enforce the regulations. These camera systems bring an opportunity to automatically determine vehicle occupancy from captured HOV/HOT NIR images. Due to their ability to see through windshields of vehicles, these cameras also enable enforcement of other passenger compartment violations such as seatbelt violation and driver cell phone usage, in addition to determining vehicle occupancy. In this paper, we propose computer vision methods for detecting vehicle occupancy, seatbelt violation, and driver cell phone usage from NIR images captured from HOV/HOT lanes. Our methods consist of two stages. First, we localize the vehicle's front windshield and side window from captured HOV/HOT images using the deformable part model (DPM). Next, we define a region of interest in the localized images for each violation type and perform image classification using one of the local aggregation-based image features, i.e., bag-of-visual-words (BOW), vector of locally aggregated descriptors (VLAD), and Fisher vectors (FV), and compare their performances for each case. We also compare the performance of DPM-based detection with the image classification methods for vehicle occupancy and seatbelt violation detection. A data set over 4000 images including front/side view vehicle images with seatbelt and cell phone violations was collected on a public roadway and is used to perform the experiments.

Preliminary research developing a theory of cell phone distraction and social relationships

Motor vehicle crashes remain the leading cause of death and injury for people aged 5–34, accounting annually for over 3000 deaths, and 100 times as many injuries. It is well established that distracted driving, and cell phone use while driving in particular, pose significant crash risk to drivers. Research has demonstrated that drivers are well aware of this danger but over 90% of drivers report using a cell phone while driving. Given the likely role that social influence plays in how people use cell phones while driving surprisingly little research has been conducted investigating to whom drivers are talking or texting. We report the results of a national survey to determine who drivers are most likely to call or text when behind the wheel and compared these results with general cell phone calling and texting patterns as well as previous findings on the prevalence of calling and texting while driving. The results suggest that social distance is a key factor in cell phone use while driving: Teens are more likely to talk with parents, and adults are more likely to talk with spouses than general calling patterns would suggest. We discuss whether the purpose of calls made while driving, such as coordination, could help explain these patterns. We propose next steps for further examining the role social relationships play in cell phone use while driving to potentially reduce teen driver cell phone use by lowering the number of calls from parents.

Relationship of Near-Crash/Crash Risk to Time Spent on a Cell Phone While Driving

Objectives: The objective of this study was to examine in a naturalistic driving setting the dose–response relationship between cell phone usage while driving and risk of a crash or near crash. How is the increasing use of cell phones by drivers associated with overall near-crash/crash risk (i.e., during driving times both on and off the phone)?Methods: Day-to-day driving behavior of 105 volunteer subjects was monitored over a period of 1 year. A random sample was selected comprised of 4 trips from each month that each driver was in the study, and in-vehicle video was used to classify driver behavior. The proportion of driving time spent using a cell phone was estimated for each 3-month period and correlated with overall crash and near-crash rates for each period. Thus, it was possible to test whether changes in an individual driver's cell phone use over time were associated with changes in overall near-crash/crash risk.Results: Drivers in the study spent 11.7% of their driving time interacting with a cell phone, primarily talking on the phone (6.5%) or simply holding the phone in their hand or lap (3.7%). The risk of a near-crash/crash event was approximately 17% higher when the driver was interacting with a cell phone, due primarily to actions of reaching for/answering/dialing, which nearly triples risk (relative risk = 2.84). However, the amount of driving time spent interacting with a cell phone did not affect a driver's overall near-crash/crash risk. Vehicle speeds within 6 s of the beginning of each call on average were 5–6 mph lower than speeds at other times.Conclusions: Results of this naturalistic driving study are consistent with the observation that increasing cell phone use in the general driving population has not led to increased crash rates. Although cell phone use can be distracting and crashes have occurred during this distraction, overall crash rates appear unaffected by changes in the rate of cell phone use, even for individual drivers. Drivers compensate somewhat for the distraction by conducting some of the more demanding tasks, such as reaching for or dialing a cell phone, at lower speeds. It is also possible that cell phones and other electronic devices in cars are changing how drivers manage their attention to various tasks and/or changing the kinds of secondary tasks in which they engage.

DO CELL PHONE BANS CHANGE DRIVER BEHAVIOR?

In response to concerns that distracted driving due to cell phone use has become a threat to roadway safety, many states have passed laws that prohibit drivers from texting and talking on handheld cell phones. In light of recent evidence that these bans do not reduce traffic accidents, this article asks whether this is because the laws are ineffective in reducing usage. Using data on observed driver cell phone usage combined with a difference‐in‐differences approach that exploits the within‐state variation in the adoption of bans, I find that prohibiting drivers from texting and talking on handheld cell phones reduces each by 60% and 50%, respectively. This suggests the policy is effective at reducing the targeted behavior, which leads me to discuss other factors and behavioral responses that may counteract the reduction in observed usage (JEL K32, D04, R41).

Effects of a Driver Cellphone Ban on Overall, Handheld, and Hands-Free Cellphone Use While Driving: New Evidence from Canada.

We provide new evidence on the effects of increasingly common driver cellphone bans on self-reported overall, handheld, and hands-free cellphone use while driving by studying Ontario, Canada, which instituted a 3-month education campaign in November 2009 followed by a binding driver cellphone ban in February 2010. Using residents of Alberta as a control group in a difference-in-differences framework, we find visual and regression-based evidence that Ontario's cellphone ban significantly reduced overall and handheld cellphone use. We also find that the policies significantly increased hands-free cellphone use. The reductions in overall and handheld use are driven exclusively by women, whereas the increases in hands-free use are much larger for men. Our results provide the first direct evidence that cellphone bans have the unintended effect of inducing substitution to hands-free devices.

Active transportation: Do current traffic safety policies protect non-motorists?

ObjectiveThis study investigated the impact that state traffic safety regulations have on non-motorist fatality rates. MethodsData obtained from the National Highway Traffic Safety Administration (NHTSA), the Federal Highway Administration (FHWA), and the National Institute on Alcohol Abuse and Alcoholism (NIAAA) were analyzed through a pooled time series cross-sectional model using fixed effects regression for all 50 states from 1999 to 2009. Two dependent variables were used in separate models measuring annual state non-motorist fatalities per million population, and the natural log of state non-motorist fatalities. Independent variables measuring traffic policies included state expenditures for highway law enforcement and safety per capita; driver cell phone use regulations; graduated driver license regulations; driver blood alcohol concentration regulations; bike helmet regulations; and seat belt regulations. Other control variables included percent of all vehicle miles driven that are urban and mean per capita alcohol consumption per year. ResultsNon-motorist traffic safety was positively impacted by state highway law enforcement and safety expenditures per capita, with a decrease in non-motorist fatalities occurring with increased spending. Per capita consumption of alcohol also influenced non-motorist fatalities, with higher non-motorist fatalities occurring with higher per capita consumption of alcohol. Other traffic safety covariates did not appear to have a significant impact on non-motorist fatality rates in the models. ConclusionOur research suggests that increased expenditures on state highway and traffic safety and the initiation/expansion of programs targeted at curbing both driver and non-motorist intoxication are a starting point for the implementation of traffic safety policies that reduce risks for non-motorists.

Driving under the (Cellular) Influence

We investigate the causal link between driver cell phone use and crash rates by exploiting a natural experiment induced by the 9 pm price discontinuity that characterizes a majority of recent cellular plans. We first document a 7.2 percent jump in driver call likelihood at the 9 pm threshold. Using a prior period as a comparison, we next document no corresponding change in the relative crash rate. Our estimates imply an upper bound in the crash risk odds ratio of 3.0, which rejects the 4.3 asserted by Redelmeier and Tibshirani (1997). Additional panel analyses of cell phone ownership and cellular bans confirm our result. (JEL R41)

CELL PHONE USE AND TRAFFIC CRASH RESPONSIBILITY: A CULPABILITY ANALYSIS OF COLLISION-INVOLVED DRIVERS

BackgroundCell phone use while driving is illegal in many jurisdictions. Restrictions are supported by studies showing: (i) increasing cell phone use by motorists, (ii) an increased proportion of crashes where...

Effect of North Carolina's restriction on teenage driver cell phone use two years after implementation

A majority of states now restrict teenagers from using a mobile communication device while driving. The effect of these restrictions is largely unknown. In a previous study, we found North Carolina's teenage driver cell phone restriction had little influence on young driver behavior four months after the law took effect (Foss et al., 2009). The goal of the present study was to examine the longer-term effect of North Carolina's cell phone restriction. It was expected that compliance with the restriction would increase, as awareness of the restriction grew over time. Teenagers were observed at high schools in North Carolina approximately two years after the law was implemented. Observations were also conducted in South Carolina, which did not have a cell phone restriction. In both states, there was a broad decrease in cell phone use. A logistic regression analysis showed the decrease in cell phone use did not significantly differ between the two states. Although hand-held cell phone use decreased, there was an increase in the likelihood that drivers in North Carolina were observed physically manipulating a phone. Finally, a mail survey of teenagers in North Carolina showed awareness for the cell phone restriction now stands at 78% among licensed teens. Overall, the findings suggest North Carolina's cell phone restriction has had no long-term effect on the behavior of teenage drivers. Moreover, it appears many teenage drivers may be shifting from talking on a phone to texting.

Driving Under the (Cellular) Influence

Previous research in the laboratory and by epidemiologists has compared the danger of cell phone use while driving to that of illicit levels of alcohol. This paper investigates the causal link between driver cell phone use and crash rates by exploiting a natural experiment -- the discontinuity in marginal pricing at 9pm on weekdays from 2002 to 2005 when cellular plans transitioned from peak to off-peak pricing. We first documented that the pricing threshold induced a 7.2% jump on Mondays to Thursdays in call likelihood for a large and proprietary sample of drivers in California from 2005. Two additional datasets of calls, drawn from drivers and non-drivers nationwide, affirm the price sensitivity of cell phone users. We next document the corresponding change in the crash rate for California as well as the eight additional states for which we have the universe of crash data. Using a period prior to the prevalence of 9pm plans as a comparison group, we found no evidence for a relative rise in crashes after 9pm on Mondays to Thursdays in 2005, or during an extended period from 2002 to 2005. These results are robust to alternative estimation strategies and controls. Our preferred estimates imply an upper bound in the odds ratio of crash risk associated with cell phone use of 3.0, which rejects the findings of most existing research including the 4.3 asserted in the influential paper by Redelmeier and Tibshirani (1997). A panel analysis of regional trends in cell phone ownership and legislation banning driver cell phone use confirms our basic result. We present possible explanations for this counterintuitive finding, and discuss implications for policy.

Mobile Phone Accidents - Experience Of India

Mobile Phone Accidents - Experience Of India Every year nearly 1.4 million people have been killed because of they are wireless customers and their over-bearing cell phones. While in India, an estimated 1.35 lakhs person died due to road accident in 2010, which is approximately 10% of road accident fatalities worldwide and these figures are the highest in the world. But still no research has been carried out to find the number of drivers using cell phone involved in road accident and very limited efforts has been carried out to prevent accident due to cell phone usage. To our knowledge this is the first survey carried out in India to determine the number of drivers involved in an accident due to mobile phone use. With the aim of preventing such accidents, it is proposed to develop a highly efficient automatic system for early detection of incoming and outgoing call, by placing an antenna along with mobile detection unit above the driver seat. This unit is capable of distinguishing whether the cell phone used either by the driver or by the passenger, if the driver uses of cell phone is detected, a safety application named Cellphone Accident Preventer (C.A.P.) which is developed using J2ME will be automatically load on the driver's cell phone which helps in eliminating the risk of accidents from occurring, at the same time ensuring that the user does not miss any emergency call. The research has been extended to show how far the system will help in preventing accidents and to what extent this system will help in reducing the Indian economic loss incurred unnecessarily due to road accident fatalities.

Effect of Driver Cell Phone Use on Queue Discharge Patterns at Signalized Intersections

Queue discharge patterns and driver cell phone use were monitored and analyzed at four intersections in Charlotte, North Carolina. Of the more than 3,700 drivers observed, about 13.3% were talking on a cell phone and approximately 1.8% were texting while driving. The saturation headways and start-up lost times of queues containing cell phone users were compared with queues lacking the presence of cell phone users. Results show that although text messaging has a clear negative effect on discharge patterns, talking on a cell phone has an insignificant effect. Secondary analyses imply that drivers talking on a cell phone have a stabilizing effect because they likely perform worse than attentive drivers but much better than those engaged in a visually demanding distraction. Findings also show that the performance of drivers talking on a cell phone is fairly consistent, whereas the performance of nonusers is inconsistent because of myriad other distractions with widely varying effects on driver abilities. The performance of texting drivers was found to be the most variable, perhaps because of the sporadic, often intense visual demands associated with texting while driving.