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This study examines how conversing with passengers in a vehicle differs from conversing on a cell phone while driving. We compared how well drivers were able to deal with the demands of driving when conversing on a cell phone, conversing with a passenger, and when driving without any distraction. In the conversation conditions, participants were instructed to converse with a friend about past experiences in which their life was threatened. The results show that the number of driving errors was highest in the cell phone condition; in passenger conversations more references were made to traffic, and the production rate of the driver and the complexity of speech of both interlocutors dropped in response to an increase in the demand of the traffic. The results indicate that passenger conversations differ from cell phone conversations because the surrounding traffic not only becomes a topic of the conversation, helping driver and passenger to share situation awareness, but the driving condition also has a direct influence on the complexity of the conversation, thereby mitigating the potential negative effects of a conversation on driving.
Frank A. Drews, Monisha Pasupathi, and David L. Strayer
University of Utah, Salt Lake City, Utah
Previous work on use of cell phones while driving compared cell phone conversations while driving with
driving only conditions. This study investigated how conversing on a cell phone differs from conversing
with a passenger. Participants conversed about close-call situations they experienced. We compared how
well drivers followed task instructions when driving only, when driving and conversing on a cell phone,
and when driving and conversing with a passenger. The results show that the number of driving errors was
highest in the cell-phone condition. Analyzing the conversations we found that in passenger conversations
more references were made to traffic and more turn taking followed those references than in cell phone
conversations. The results show that passenger conversations differ from cell phone conversations because
the surrounding traffic becomes a topic of the conversation, helping driver and passenger to share situation
awareness, and mitigating the potential effects of conversation on driving.
There is ample evidence that conversing on a cell-phone
while driving affects driving performance negatively.
Previous studies have found that cell phone use impairs the
driving performance of younger adults (Alm & Nilsson, 1995;
Briem & Hedman, 1995; Brookhuis, De Vries, & De Waard,
1991; Brown, Tickner, & Simmonds, 1969; Goodman et al.,
1999; McKnight & McKnight, 1993; Redelmeier &
Tibshirani, 1997; Strayer & Johnston, 2001; Strayer, Drews,
& Johnston, 2003), and older adults (Strayer & Drews, 2003).
The level of impairment can be compared to being intoxicated
at a blood alcohol level of .08 (Strayer, Drews, & Crouch,
2003). Still unexamined is whether and how conversing on a
cell phone differs from a conversation with a passenger.
There are at least two competing hypotheses: One hypothesis
is that there is no difference between cell-phone
conversations and passenger conversations, and that both
negatively affect driving performance. An alternative
hypothesis is that the passenger in a passenger conversation
shares the same situation as the driver. The passenger may
monitor the surrounding traffic, and respond to changes in
driving demands. This supportive behavior can be explicit,
for example by referring to traffic dangers, or more implicit
by moderating the conversational flow in response to
increased difficulty of the driving task. Of course, this
assumes that the passenger has at least a rudimentary
understanding of potential dangers of traffic and the driving
task. Directing the driver’s attention towards potential danger
creates situation awareness (Endsley, 1995) of the
surrounding traffic that is shared by the driver and the
passenger. Contrary to this, in a cell-phone conversation, the
person not driving lacks awareness of the traffic surrounding
the driver. As a consequence, he or she is unlikely to support
the driver with regard to the driving task.
One of the major problems for research on impact of cell-
phone conversations on driving performance relates to the
issue of naturalistic conversations. Some investigators have
used conversations in which confederates converse with the
driver about some topic of interest identified earlier, others
have use word repetition tasks to create a situation which is
equivalent to a conversation. These approaches are frequently
criticized because of their failure to mimic naturalistic
conversations. An alternative to these approaches in studying
the impact of conversations on driving is to use close call
stories (Bavelas, Coates, & Johnson, 2000) as the topic of the
conversation. Close call stories are defined as stories about
times when “your life was threatened.” The advantage of
using close call conversations is that they involve the kinds of
stories that are often told among friends, and the type of story
which is engaging for participants. In the current study this
paradigm was chosen with the intention to create a situation
which comes as close as possible to naturalistic
The goal of this research is to increase the understanding
of how conversing on a cell-phone while driving differs from
conversing with a passenger while driving.
Participants. 96 adults participated in the study.
Participants ranged in age 18 from to 49, with an average age
of 20 years. 49 participants were male and 47 participants
were female. All participants had normal or corrected-to-
normal visual acuity, normal color vision (Ishihara, 1993),
and a valid Utah driver’s license. Participants were recruited
in friend dyads, and received course credit for participating.
Stimuli and Apparatus. A PatrolSim™ high-fidelity
driving simulator, manufactured by GE Capital I-Sim was
used in the present study (Figure 1). For the purpose of this
study the computer panel and the radio were removed from
the dashboard of the simulator. The simulated vehicle bases
on the vehicle dynamics of a Crown Victoria® model with
automatic transition build by the Ford Motor Company.
Figure1. I-Sim driving simulator.
A freeway road database simulated a 24-mile multi-lane
beltway with on and off-ramps, overpasses, and two-lane
traffic in each direction. Participants were driving under an
irregular-flow driving condition (Drews, Strayer, Uchino, &
Smith, in press) where vehicles changed lanes and speeds
frequently, making it difficult for the participant to proceed
smoothly and requiring varying attentional demands.
Procedure. After providing informed consent, subjects
answered questionnaires assessing their mood and driving
attitudes. Next, participants were familiarized with the driving
simulator using a standardized 20-minute adaptation
sequence. After finishing the familiarization, one participant
was randomly selected to drive the vehicle, the other, based
on condition was either the passenger or talking on the cell-
phone to the driver from a different location. Speaker
(provides the close call story) and listener assignments were
counterbalanced. The participants were instructed to drive
safely and to follow all the traffic rules. Their task was
described as having a conversation about a close call story,
and as leaving the highway once they arrived at a rest area
located approximately 8 miles after the beginning of the
drive. All driving participants additionally drove in a single
task condition, where they were driving only. The dual task
condition consisted of either driving while conversing on a
cell-phone or driving while talking to a passenger. The order
of the single and dual task conditions was counterbalanced.
Measures. As a measure of performance in dealing with
the driving task the number of occasions when the drivers
exited the highway at the designated destination was counted.
In addition, references to the traffic while conversing were
analyzed. The rationale for this measure was that referring to
the surrounding traffic partly directs attention towards an
event, thus participants share situation awareness. A third
measure was the number of turn takes after a reference to
traffic was made. The number of turn takes reflects the
interest both partners have towards conversing about traffic
rather than the close call story.
Design. In the current study a one factorial design
(passenger and cell phone conversation) with conversation as
a between subject factor was used (24 couplets in the
passenger conversation condition, 24 dyads in the cell-phone
conversation condition). In addition every driver had to drive
in a control condition, where they were driving only.
Task completion. One part of the analysis focused on
driving performance, that is successfully accomplishing the
driving task. Table 1 shows the number of participants that
finished the task successfully or failed to finish the task for
the two experimental conditions and the control condition.
Table 1. Successful task completion.
Cell-phone Passenger Control
Correct exit 12 21 46
Missed exit 12 3 2
Analyzing task accomplishment for cell-phone
conversation and passenger conversation a difference
between the two conditions (ȋ2
(1)=7.9; p<.05) was found:
drivers in the cell-phone condition were four times more
likely to fail in finishing the task than drivers in the passenger
condition. No change in performance was observed in the
passenger conversation condition compared to the control
condition (driving only), though the change in performance
between cell-phone condition and control condition was
significant (ȋ2
(1)=8.9; p<.01) .
Shared situation awareness. The transcripts of the
conversations were analyzed for references to traffic and
number of turn takes following such reference. The latter
indicates the extent to which the driving task became a
conversational topic in its own right, temporarily superceding
the close-call stories. The number of references to
surrounding traffic in the passenger conversation condition
and the cell phone conversation condition are shown in Table
2. Fewer references to traffic were made in the cell phone
condition (t(46)=3.0; p<.01).
Cell-phone Passenger
References 2.1 (1.6) 3.8 (2.4)
Turns at speech 8.6 (6.7) 19.2 (13.8)
Table 2. Mean number (sd) of references to traffic and turns.
The next analysis focused on the number of turns
between the two partners which continued conversing about
traffic after an initial reference to traffic was made. The
number of turns for both conditions is shown in Table 2.
Overall more than twice as many turns occurred in the
passenger condition compared to the cell-phone condition
(t(46)=3.4; p<.01).
The present study investigated the question how
driving while talking on a cell-phone differs from driving
while conversing with a passenger. The findings about task
completion demonstrate that a driver who converses on a cell
phone pays less attention to the surrounding traffic as
indicated by the large number of drivers who missed the exit,
because they did not notice it. This failure to successfully
complete the task in the cell-phone condition can be
explained by the fact that a person on a cell-phone is less
likely to extract information from his environment than
someone who is not conversing on a cell-phone (Strayer,
Drews, & Johnston, 2003). The analysis of the conversation
data suggests that the driver and the passenger are more
frequently talking about the surrounding traffic and that the
traffic and driving task become part of the conversation, as
indicated by the fact that pairs spent more conversational
turns on the traffic topic in the passenger condition. This
indicates that the passenger supports the driver in his task of
driving by directing attention to the surrounding traffic when
necessary and by supporting the driver in devoting attention
to the traffic rather than the storytelling. Thus, the better
driving performance of participants in the passenger condition
is partly due to the fact that the driver and the passenger share
situation awareness.
The present findings indicate that when a driver
converses with a passenger, the dyad more often collaborates
in the task of driving safely by referring to traffic and
conversing about it to a larger extent. This helps to maintain a
higher level of shared situation awareness something a person
on the other end of a cell-phone can not do.
One important limitation of this study is that a high
fidelity driving simulator was used to study passenger and
cell-phone conversations. Despite the fact that there is more
and more evidence indicating the validity of driving simulator
based findings with regard to real driving, additional research
investigating passenger conversations and cell-phone
conversations in real driving would be important to show that
the current findings can be generalized beyond simulated
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... For example, driving performance degrades as the difficulty and complexity of the conversation increases (McKnight, & McKnight, 1993;Nunes & Recarte, 2002). Similarly, a conversation degrades as the demands of driving increase (Nunes & Recarte, 2002;Drews, Pasupathi, & Strayer, 2008). Tillman, Strayer, Eidels, and Heathcote (2017) measured the cognitive workload of a dyad engaged in a natural conversation. ...
... DriveSafety software was utilized to program a 19mile driving scenario, which included two-and three-lane divided highways with speed limits between 55 and 65 miles per hour and moderate traffic. The other simulated vehicles changed speed and lanes to create irregular-flow traffic (Drews, Pasupathi, & Strayer, 2008), which simulated realistic traffic. Participants drove for approximately 15-minutes in each block. ...
... The data help to explain why a conversation can lead to driver-restricted attention (e.g., Regan, Hallett and Cordon, 2011). This impairment is most apparent with cell phone conversations due to the compensatory factors associated with passenger conversations (e.g., Drews, Pasupathi, and Strayer, 2008). ...
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Tillman et al. (2017) used evidence-accumulation modeling to ascertain the effects of a conversation (either with a passenger or on a hands-free cell phone) on a drivers’ mental workload. They found that a concurrent conversation increased the response threshold but did not alter the rate of evidence accumulation. However, this earlier research collapsed across speaking and listening components of a natural conversation, potentially masking any dynamic fluctuations associated with this dual-task combination. In the present study, a unique implementation of the Detection Response Task was used to simultaneously measure the demands on the driver and the non-driver when they were speaking or when they were listening. We found that the natural ebb and flow of a conversation altered both the rate of evidence accumulation and the response threshold for drivers and non-drivers alike. The dynamic fluctuations in cognitive workload observed with this novel method illustrate how quickly the parameters of cognition are altered by real-time task demands.
... Thus, if the goal of research is to increase ecological validity while maintaining appropriate laboratory controls, one approach would be to include some aspect of the real world in an experimental, laboratorybased task that still gives the researcher control over extraneous variables. We refer to this approach as the simulated naturalistic environment, which has been adopted with success in a variety of domains of learning and memory such as contextdependent learning in outdoor environments (Godden & Baddeley, 1975), statedependent learning using tetrahydrocannabinol (Eich et al., 1975), flashbulb memories of realworld events (Talarico & Rubin, 2007), category learning using naturalistic materials like paintings (Kang & Pashler, 2012;Kornell & Bjork, 2008), rocks (Nosofsky et al., 2019), birds (Morehead et al., 2017;Wahlheim et al., 2011), and driving simulations (Drews et al., 2008;Strayer & Johnston, 2001). Because we were interested in the effects of dividing attention on memory in a simulated environment (e.g., video of driving route), of particular concern to the present study is the work of Strayer and Johnston (2001) who used different simulated naturalistic environments to investigate the effects of dividing attention on driv ing performance. ...
... To approximate a true driving experience more closely, Drews et al. (2008) had participants sit in a driving simulator in a laboratory that allowed par ticipants to steer a wheel and apply gas and brakes while viewing monitors that displayed movement on a road with traffic. Participants were instructed to drive to an exit, exit a highway, and stop at a stop sign that followed. ...
... The present study investigated whether incidental learning was affected by efforts to occupy dif ferent components of working memory using divided attention tasks in a simulated naturalistic environment. Prior research has shown that divid ing attention adversely affects driving simulation performance (Drews et al., 2008) and incidental learning of paired associates (NavehBenjamin & Brubaker, 2019). Furthermore, research has shown that dividing attention by occupying the phonologi cal loop affects verbal working memory, whereas dividing attention by occupying the visuospatial sketchpad affects nonverbal working memory (Baddeley & Lieberman, 1980;Brooks, 1968;Logie et al., 1990). ...
Clothing type can have a significant impact on the way people are perceived. In this study, we were interested in the effect of business versus casual clothing on the perception of Asian American women, given various stereotypes about them. We used a between-subjects design with a sample of college students from a university in the United States. Participants saw 3 Asian American women (and 1 European American woman to distract from the nature of the study) in either business attire or casual outfits, and rated each woman on a series of descriptors based off various stereotypes of Asian American women. We used the Scale of Anti-Asian American Stereotypes to measure internal prejudice toward Asian Americans and the Ambivalent Sexism Inventory to measure sexism. The Scale of Anti-Asian American stereotypes was a significant covariate, F(4, 233) = 6.09, p < .001, ηp2 = .10. Participants rated models in business attire as less stereotypically Asian, F(1, 239) = 46.56, p < .001, ηp2 = .17, less sexualized, F(1, 239) = 12.91, p < .001, ηp2 = .05, and less invisible, F(1, 239) = 42.01, p < .001, ηp2 = .15. Our results show that stereotypes can indeed be influenced by business attire. It is important to note that future research may be oriented toward changing the attitudes of those who hold harmful stereotypes, rather than the actions (i.e., clothing choices) of the subjects of prejudice.
... This finding appears to be like observations made in previous reports. 52,53 Limitations ...
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Background: Aggressive driving is prevalent and may be associated with impulsivity. The relationships between these variables among Saudi drivers have received scant attention. In this study, we aimed to examine the level of aggressive driving and its relationships with impulsivity among Saudi drivers in Shaqra. Methods: Overall, 504 Saudi drivers were recruited and assessed in this cross-sectional study using demographic and driving proforma, a self-reporting Barratt impulsiveness scale (BIS), and an Aggressive Driving Behavior Scale (ADBS). Results: BIS and ADBS had mean scores of 37.97 (3.24) and 21.74 (8.51), respectively. In linear regression analysis, the value of the BIS non-planning subscale negatively predicted the value of the ADBS Conflict subscale (beta = -.151, p = .002) and Speeding subscale (beta = -.103, p = .031). In contrast, the value on the score of the BIS Motor subscale statistically significantly and positively predicted the value on the score of the ADBS Speeding subscale (Beta = -.103, p = .032). Conclusion: The result shows a differential link between the component of impulsivity and aggressive driving. The lack of foresight is negatively linked with conflict behavior and high- speed driving, whereas acting without thinking is positively associated with high-speed driving.
... Different distractions, such as texting or listening to music, could induce attention deficit and lead to neglecting of critical traffic information [13], [14]. For example, navigation devices provide more traffic information to aid with navigation, but they can also cause distractions while driving, resulting in attention loss [4], [15], [16]. Moreover, attention shifts from different sensory modalities while driving, such as shifting from auditory distractors to visual driving tasks, could lead to divided attention. ...
... For example, using cellphones has been recognized as the most common activity among many types of driver distraction (Caird et al., 2008;Kidd et al., 2016;Simmons et al., 2016). Besides, cellphone use while driving (e.g., calling, texting, or watching videos) can cause deterioration in many aspects of driving performance, such as reaction time, speed maintenance, and decision making (Chaudhary et al., 2012;Drews et al., 2008;Oviedo-Trespalacios et al., 2016;Jannusch et al., 2021;NHTSA, 2013). In 2018, a total of 385 people died and 3300 people were injured in crashes involving cellphone-related activities in the United States (NHTSA, 2020). ...
With the popularity of smartphones and the increasing dependence on cellphones, cellphone-use-involved distracted driving has become a global traffic safety concern. Calling, texting, or watching videos while driving could have harmful impacts on driving abilities and increase crash-injury severities. To investigate the temporal stability and the heterogeneity of cellphone-involved crash injury severity determinants, a series of likelihood ratio tests and random parameters logit models with heterogeneity in means and variances are estimated. Cellphone-involved single-vehicle crash datasets of Pennsylvania from 2004 to 2019 are utilized. Marginal effects are also applied to investigate the impact of explanatory variables on injury severity outcomes. The results indicate an overall temporal instability of cellphone-involved crashes across different periods. However, driving without seatbelts and overturns are observed to produce relatively stable and positive influence on the increased injury severities of cellphone-involved crashes. Besides, it is noteworthy that a combination of cellphone usage with risky driving behaviors (aggressive driving, alcohol- or drug-related driving, speeding, or fatigue driving) significantly increase driver injury-severities. This finding highlights the necessity of identifying drivers with multiple risk-taking behaviors and enacting laws to prohibit these drivers from using cellphones while driving. Applications of smartphones provide another feasible approach to prevent using cellphones while driving. Insights and suggestions of this study would be valuable to mitigate the negative outcomes of cellphone-involved crashes and prevent the crashes caused by cellphone-involved distracted driving in the future.
... Driving speeds were regulated between 55 and 65 mph by speed limit signs in both scenarios. Programmed trigger points in both scenarios controlled surrounding traffic to match with the drivers' progression through the loop, creating the irregular-flow traffic (Drews et al., 2008). ...
We examined the hidden costs of intermittent multitasking. Participants performed a pursuit-tracking task (Experiment 1) or drove in a high-fidelity driving simulator (Experiment 2) by itself or while concurrently performing an easy or difficult backwards counting task that periodically started and stopped, creating on-task and off-task multitasking epochs. A novel application of the Detection Response Task (DRT), a standardized protocol for measuring cognitive workload (ISO 17488, 2016), was used to measure performance in the on-task and off-task intervals. We found striking costs that persisted well after the counting task had stopped. In fact, the multitasking costs dissipated as a negatively accelerated function of time with the largest costs observed immediately after multitasking ceased. Performance in the off-task interval remained above baseline levels throughout the 30-s off-task interval. We suggest that loading new procedures into working memory occurs fairly quickly, whereas purging this information from working memory takes considerably longer. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
... To achieve effective proactive communication, in-car speech-based agents will need to manage when they communicate to avoid distracting or overloading the driver. For example, prior research has shown that passengers who can see the driving scene better adjust their conversation rate and timing when compared to cell-phone conversations, leading to better driving performance [22]. Hence, we would expect that in-car agents that can better time their interactions are less likely to negatively impact the driver's attention. ...
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This paper examines sensor fusion techniques for modeling opportunities for proactive speech-based in-car interfaces. We leverage the Is Now a Good Time (INAGT) dataset, which consists of automotive, physiological, and visual data collected from drivers who self-annotated responses to the question "Is now a good time?," indicating the opportunity to receive non-driving information during a 50-minute drive. We augment this original driver-annotated data with third-party annotations of perceived safety, in order to explore potential driver overconfidence. We show that fusing automotive, physiological, and visual data allows us to predict driver labels of availability, achieving an 0.874 F1-score by extracting statistically relevant features and training with our proposed deep neural network, PazNet. Using the same data and network, we achieve an 0.891 F1-score for predicting third-party labeled safe moments. We train these models to avoid false positives---determinations that it is a good time to interrupt when it is not---since false positives may cause driver distraction or service deactivation by the driver. Our analyses show that conservative models still leave many moments for interaction and show that most inopportune moments are short. This work lays a foundation for using sensor fusion models to predict when proactive speech systems should engage with drivers.
Objective To explore how passenger presence and the degree of association between young driver and passenger influences young drivers’ eye glance behavior when they are subjected to distraction. Background Young drivers (18–20 years old) are at an elevated crash risk when subjected to distraction. They are likely to be distracted even further when they drive with passengers. However, the eye glance behavior of these drivers when driving with passengers has not been explored. Method Eye glance data of 34 young drivers between the ages of 18 and 20 years were collected. Participants drove with and without a passenger while subjected to three distracting tasks (visual-manual, cognitive, or visual-cognitive) and driving scenarios that required driver attention. Results Visual-cognitive as well as visual-manual states of distraction result in higher mean and standard deviation of glance duration, along with higher number of glances away from road. Passenger presence is found to negatively influence young drivers’ eye glance behavior. The degree of association between the young driver and the passenger may help reduce the deviation of eye glances towards the task-related objects. Conclusion In addition to distraction, passengers have a negative influence on the eye glance behavior of young drivers. However, a high degree of association between driver and passenger may mitigate the negative impact of distraction on the eye glance behavior of young drivers. Application (non-theoretical works) This research may aid in the design of interventions that improve young drivers’ eye glance behavior when they drive with their peers.
This paper describes a methodology to design computational models to evaluate the workload for driving tasks. A computational model was configured for a driving scenario used in a pilot study that included a secondary task at varying levels of difficulty to increase the driver’s workload. The computational model results provided a workload analysis of the concurrent driving tasks. This analysis can be used to explain the experimental findings from subject experiments and to evaluate the workload trade-offs between primary and secondary driving tasks.
Aviation places significant demands on pilots' perceptual and attentional capacities. The avoidance of other objects both on the ground and in the air is critical to safe flight. Research on automobile driving has revealed the occurrence of ‘inattentional blindness’ (IB) whereby objects clearly located within the visual field may not detected when drivers are concurrently engaged in another attention capturing task such as a cellphone conversation. Almost no comparable research has been conducted within the aviation domain despite the significance of both ground-based and mid-air collisions. The present study was designed to investigate the effects of diverting attentional resources away from the primary task of safely flying a simulated light aircraft from takeoff to cruising. Flight naïve students were trained to proficiency in a flight-simulator and flew two simulated flights with and without a competing attentional task. Detection of a variety of objects placed in the background was measured. The results showed that when distracted by an engaging cellphone conversation novice pilots failed to detect many of the objects located within the visual scene. Recognition accuracy was greater when pilots' attention was not diverted elsewhere. There was a reduction in time spent looking at some key flight instruments but not on others. Inattentional blindness poses significant flight safety risks and further research into both the stimulus and perceiver characteristics that promote or reduce inattentional blindness would be of significant benefit to aviation safety.
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Using a closed-circuit driving track environment, we investigated the influence of using a hands-free mobile (or cell) phone on various biomechanical and perceptual factors that underlie the control of driving. Results showed that in three tasks representative of everyday driving conditions, the perceptual control of action was compromised when compared to a control condition where no mobile phone conversation was present. While conversing, critical control actions related to braking were postponed on approach to a corner. During controlled braking, as when approaching a stationary car at a traffic light, the degree of braking was reduced and braking style was altered in a non-optimal manner. During an obstacle avoidance task, car dynamics were affected as a result of the conversation. Interpretation of the results is motivated by the ecological approach to perception–action and the theory of affordances. It is concluded that a driver’s sensitivity to prospective information about upcoming events and the associated perception and awareness of what the road environment affords may both significantly be degraded when simultaneously using a hands-free mobile phone. Implications for intervention and policy are discussed.
Driving is an intrinsically complex task, comprising some 40 major tasks and over 1700 subtasks. This chapter discusses perceptual and cognitive aspects of the driving task and discusses the way driving task can be understood in psychological terms. It explores the different levels of the driving task and explores the way the activities they comprise interact. The chapter also highlights the fundamental issues needed in a theoretical account of driving. Thereafter, the chapter introduces the accepted levels within the driving task and outlines the simplest driving tasks, including altering speed, heading, and changing gear. The complex aspects of the driving task, such as judgments of speed, distance and arrival time, and the selection of information and appreciation of risk are presented and the mechanism of their control is explored.
Our research examined the effects of hands-free cell-phone conversations on simulated driving. We found that even when participants looked directly at objects in the driving environment, they were less likely to create a durable memory of those objects if they were conversing on a cell phone. This pattern was obtained for objects of both high and low relevance, suggesting that very little semantic analysis of the objects occurs outside the restricted focus of attention. Moreover, in-vehicle conversations do not interfere with driving as much as cell-phone conversations do, because drivers are better able to synchronize the processing demands of driving with in-vehicle conversations than with cell-phone conversations. Together, these data support an inattention-blindness interpretation wherein the disruptive effects of cell-phone conversations on driving are due in large part to the diversion of attention from driving to the phone conversation.
In this study conversation with a remote person (hands-free phone), an in-vehicle person (passenger), and a no conversation (baseline) condition were compared on measures of attention and peripheral detection. We held conversation pace constant so that any difference found in attention or peripheral detection could be attributed to the distinctive feature of the type of conversation (remote, in-vehicle). The difficulty level of the verbal task was included as a second independent variable. Forty-eight undergraduate students participated in all conditions of a within-subjects design. The results revealed that conversation resulted in slower reactions and fewer correct responses on both attention and Peripheral Detection tasks compared to no conversation, while conversation type (remote/in-person) did not make a significant difference. Difficulty of the verbal task affected performance on the Peripheral Detection task but not on the attention task. These findings imply that conversation has a negative effect on attention and peripheral detection which are important components of driving. This effect may be greater with difficult conversations.
Little work has empirically examined the cognitive construct of situation awareness (SA) in driving tasks involving the use of advanced in-vehicle automated technologies and personal communication devices. This research investigated the effects of an adaptive cruise control (ACC) system, and cell phone use in driving, on a direct and objective measure of SA, and assessed the competition of multiple driving and communication tasks for limited mental resources in terms of driving performance. Eighteen participants drove a virtual car in a driving simulation and performed a following task involving changes in speed and lateral position. Half of the participants were required to respond to cell phone calls and all completed trials with and without use of the ACC system. Task performance was measured in terms of lane deviations and speed control in tracking a lead vehicle, as well as headway distance in the following task. SA was measured using a simulation freeze technique and SA queries on the driving situation. Subjective workload was measured using a uni-dimensional mental workload rating. Results indicated use of the ACC system to improve driving task SA under typical driving conditions, and to reduce driver mental workload. However, the cell phone conversation caused deleterious effects on driving SA and increased driver mental load. The cell phone conversation (secondary task) competed for limited mental resources of drivers, leading to less attention to, and accurate knowledge of, the driving situation. Results also revealed the ACC system to improve driving performance along multiple dimensions; however, the cell phone did not have an effect. The latter result may be attributed to a short duration of the cell phone conversations during the experiment. This study has implications for the implementation of in-vehicle automation to support driver SA under normal driving conditions and regulations on the use of cell phones while driving.
Why are hands-free mobile telephones linked to driver distraction and increased involvement in accidents? We suggest that during normal in-car conversation, both the driver and passenger will suppress conversation when the demands of the road become too great. However, a remote speaker on a mobile telephone has no access to the same visual input as the driver, and will be less likely to pace the conversation according to roadway demands. To test this hypothesis pairs of naïve participants drove a circuit of roads including dual carriageways, rural, urban and suburban roads in Nottinghamshire, UK. One of the participants in each pair was the driver, while the other was the conversational partner. Across three laps of the circuit the partner engaged in a verbal task with the driver while sat in the same car (with or without a blindfold), or via a hands-free mobile (cellular) telephone. The number of utterances, words, and questions were analysed for both drivers and passengers across the different types of road. The results demonstrated that the normal in-car conversations were suppressed during the most demanding urban roads. The mobile telephone condition prevented suppression from taking place in the passengers’ conversations, and even encouraged drivers to make more utterances that they would normally do with a normal in-car conversation. The results demonstrate a potential problem when using hands-free mobile telephones while driving.