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Speeding is a prevalent and risky behavior. This study leveraged the MIT Advanced Vehicle Technology naturalistic driving data to identify and characterize speeding behavior across automation levels including Manual Driving, Adaptive Cruise Control (ACC), and Pilot Assist (PA) that supports both longitudinal and lateral vehicle control. We analyzed 146 hours of motorway driving from 15 participants who each drove an instrumented vehicle for one month. Speeding prevalence, magnitude, and duration distributions were compared across automation levels using logistic mixed effect models. Findings indicated that although PA was the most prevalent driving state during free flow motorway driving, drivers were more likely to speed with ACC compared to during Manual Driving or with PA. Automation, ACC and PA, were associated with longer speeding durations and lower speeding magnitudes compared to driving manually. These findings can inform the development of automation systems that may help reduce speeding behaviors and promote safe driving.

Tesla auto lane change is a feature within Autopilot (AP) – a collection of automated driving features designed to assist the driver with automatic steering and adaptive cruise control. In order to determine how drivers engage in maneuvers performed automatically by AP, 5,154 manual and automated lane change events along with video, system data, and vehicle kinematics were extracted from over 691 hours of naturalistic driving from 19 different drivers. Findings indicate that drivers performed manual lane changes more frequently than auto lane changes despite the use of Autopilot being more prevalent than manual driving on motorways. Auto lane changes occurred at higher speed and were associated with fewer acceleration and braking events and narrower distributions of kinematic characteristics. The lower frequency and kinematic variability in auto lane changes may suggest that drivers use this feature in a limited set of circumstances.

This paper explores a holistic approach to understanding the impacts of on-road factors on continuous driver-automation interaction in naturalistic environments. We processed and synchronized CAN Bus signals, GPS coordinates, and high-definition videos collected from two drivers in 98 trips (~97 hours) involving Tesla Autopilot engagement collected over three years as part of the MIT Advanced Vehicle Technology naturalistic driving study. Bayesian generalized linear models were trained with the synchronized data and revealed that steering wheel rotations and speed changes, even to a small extent, cause Autopilot disengagement. Road types and experience were also associated with drivers’ probability of using driver assistance, while speed and surrounding vehicles had little impact (when traveling in stable states). This data-driven approach enables a more comprehensive understanding of driver-automation interaction to enhance safety.

A field operational test assessed visual-manual disengagement when driving with adaptive cruise control (ACC) relative to manual driving. Ten volunteers drove instrumented vehicles on public roads for 4 weeks, using the vehicles as they would their own. To study change over time, the 4-week trial was divided evenly into two periods. Analyses were based on video of drives on limited-access highways when speed was above 25 mph. Visual-manual disengagement from driving was defined as periods when drivers had both hands off the steering wheel or performed visual-manual secondary activity with electronics. Odds of visual-manual disengagement increased from period 1 (weeks 1 and 2) to period 2 (weeks 3 and 4) more during ACC use than during manual driving. Conversely, odds of cellphone manipulation and hands-offwheel behavior increased in period 2 during manual driving only, suggesting a nuanced connection between behavioral adaptation to ACC use after a month of exposure.

As the characteristics of in-vehicle human-machine interfaces (HMIs), the driving task, and the expectations and behavior of drivers have evolved, so too should our thinking and approach to HMI design and evaluation. This panel will present background and perspectives on the current status, emerging needs, challenges, and opportunities in this area. A key focus for the panel is an emphasis on attention support. Detailed contextual description is provided below for reference to allow panelists to keep their opening remarks relatively brief to allow for substantive question and discussion time with the audience.

Previous research indicates that drivers may forgo their supervisory role with partial-automation. We investigated if this behavior change is the result of the time automation was active. Naturalistic data was collected from 16 Tesla owners driving under free-flow highway conditions. We coded glance location and steering-wheel control level around Tesla Autopilot (AP) engagements, driver-initiated AP disengagements, and AP steady-state use in-between engagement and disengagement. Results indicated that immediately after AP engagement, glances downwards and to the center-stack increased above 18% and there was a 32% increase in the proportion of hands-free driving. The decrease in driver engagement in driving was not gradual over-time but occurred immediately after engaging AP. These behaviors were maintained throughout the drive with AP until drivers approached AP disengagement. In conclusion, drivers may not be using AP as recommended (intentionally or not), reinforcing the call for improved ways to ensure drivers’ supervisory role when using partial-automation.

Transportation users are increasingly aware of a possible future where they may ride in or share the road with automated vehicles. However, even with recent technological advances, many are still hesitant to accept automated driving. This study explores effects of the following characteristics on acceptance: demographics including age, gender, level of education and income; experiences with technology in general, as well as technology in drivers’ current vehicles; and media exposure. Survey responses from 3505 adults in the United States indicate that younger age, a high level of education, positive affect for advanced driver assistance systems currently in use, and high self-rated early technology adoption tendency have significant influence on acceptance. Data reflecting time course and salience of media coverage on knowledge and attitudes, especially with high-profile events such as a fatal crash, is presented with implications around the need for balanced media reporting with more focus on the safety benefits.

Driving simulator validation is an important and ongoing process. Advances in in-vehicle human machine interfaces (HMI) mean there is a continuing need to reevaluate the validity of use cases of driving simulators relative to real world driving. Along with this, our tools for evaluating driver demand are evolving, and these approaches and measures must also be considered in evaluating the validity of a driving simulator for particular purposes. We compare driver glance behavior during HMI interactions with a production level multi-modal infotainment system on-road and in a driving simulator. In glance behavior analysis using traditional glance metrics, as well as a contemporary modified AttenD measure, we see evidence for strong relative validity and instances of absolute validity of the simulator compared to on-road driving.

The use of a driving simulator as a tool to evaluate secondary task performance elicits the question of simulator validity. After upgrading an existing driving simulator from a medium-fidelity to a high-fidelity configuration with a new software environment, a study was run to benchmark this simulator against previously published highway-driving data. A primary goal was to assess relative and absolute validity in a simulated highway environment. Data from 72 participants who performed manual and voice-based contact dialing tasks with a center-stack-mounted smartphone in either the driving simulator or driving on the highway in one of two vehicles is considered. This analysis compared secondary task demand between the simulator and on-road vehicles by primarily considering driver off-road glance behavior. Mean total eyes-off-road time, mean single-glance duration, and the number of long off-road glances showed similar patterns relative to the manual versus voice-based tasks in the simulator and the two on-road vehicles. A driving performance metric, percentage change of standard deviation of velocity, showed differing results between the simulator and on-road vehicles. It is concluded that these data make a strong argument for relative validity, and in some cases absolute validity, for this simulator for studying glance behavior associated with in-vehicle devices under a highway-driving scenario.

Advanced Driver Assistance Systems (ADAS) have the potential to increase driver safety. However, driver misuse or failure to use ADAS could mitigate potential benefits. Appropriate training is one established method for encouraging proper use of technology. An online survey of 2364 respondents revealed significant differences between utilized and preferred methods for learning to use technologies. Drivers who learned through their preferred methods reported higher understanding and use of in-vehicle systems. Providing readily available methods of learning that align with learning preferences may improve safe use of ADAS.

Allergen information on food labels is not standardized, making allergen avoidance difficult for consumers. This study investigated the speed and accuracy of allergen identification on commercial packaging across different types of warning labels. The results identified packaging label characteristics significantly correlated with faster and more accurate identification of allergens. Standardizing warning and safe-to-consume labels may reduce risk of accidental allergen exposure for consumers managing food allergies.

Visual-manual demand placed on drivers through interactions with operational functions, embedded telematics, infotainment systems, and nomadic technologies has raised concerns associated with diverting attention from the roadway. This analysis draws on data from field studies of five different infotainment systems representing a range of screen placements and control characteristics that diverge in significant ways from the relatively standard car radio layout of previous generations. Participants performed a set of classic visual-manual radio tuning tasks under highway driving conditions. There were significant differences in task completion time, number of off-road glances, mean single off-road glance duration, and total off-road glance time across vehicles. These results highlight that the range of configurations appearing in modern infotainment systems have changed the extent to which they can be used in the classic radio tuning task to provide a standard demand benchmark.

As the first phase of a larger project, drivers were recruited to drive for a month one of two different vehicles with a range of advanced driver assistance systems (ADAS). Training methods for introducing the systems and questionnaire and structured interview methods were tested for collecting driver perceptions and understanding of the technologies. Participant perceptions and selected observations are detailed.

Multitasking related demands can adversely affect drivers’ allocation of attention to the roadway, resulting in delays or missed responses to roadway threats and to decrements in driving performance. Robust methods for obtaining evidence and data about demands on and decrements in the allocation of driver attention are needed as input for design, training, and policy. The detection response task (DRT) is a commonly used method (ISO 17488) for measuring the attentional effects of cognitive load. The AttenD algorithm is a method intended to measure driver distraction through real-time glance analysis, in which individual glances are converted into a scalar value using simple rules considering glance duration, frequency, and location. A relationship between the two tools is explored. A previous multitasking driving simulation study, which used the remote form of the DRT to differentiate the demands of a primary visual–manual human–machine interface from alternative primary auditory–vocal multimodal human–machine interfaces, was reanalyzed using AttenD, and the two analyses compared. Results support an association between DRT performance and AttenD algorithm output. Summary statistics produced from AttenD profiles differentiate between the demands of the human–machine interfaces considered with more power than analyses of DRT response time and miss rate. Among discussed implications is the possibility that AttenD taps some of the same attentional effects as the DRT. Future research paths, strategies for analyses of past and future data sets, and possible application for driver state detection are also discussed.

Older drivers represent the fastest-growing segment of the driving population. Aging is associated with well-known declines in reaction time and visual processing, and, as such, future roadway infrastructure and related design considerations will need to accommodate this population. One potential area of concern is the legibility of highway signage. FHWA recently revoked an interim approval that allowed optional use of the Clearview typeface in place of the traditional Highway Gothic typeface for signage. The legibility of the two fonts was assessed with color combinations that maximized the contrast (positive or negative) or approximated a color configuration used in highway signage. Psychophysical techniques were used to establish thresholds for the time needed to decide accurately—under glancelike reading conditions—whether a string of letters was a word, as a proxy for legibility. These thresholds were lower for Clearview (indicating superior legibility) than for Highway Gothic across all conditions. Legibility thresholds were lowest for negative-contrast conditions and highest for positive-contrast conditions, with colored highway signs roughly between the two extremes. These thresholds also increased significantly across the age range studied. The method used to investigate the legibility of signage fonts adds methodological diversity to the literature along with evidence supporting the superior legibility of the Clearview font over Highway Gothic. The results do not suggest that the Clearview typeface is the optimal solution for all signage but they do indicate that additional scientific evaluations of signage legibility are warranted in different operating contexts.

Vehicle manufacturers have developed advanced driver assistance systems (ADASs) to reduce driver workload and enhance safety. The delivery of these systems to consumers occurs through dealerships not owned by manufacturers. Limited research is available on how dealerships provide consumers with information and training on ADASs. In an exploratory study seeking information on new safety technologies, semistructured blind interviews of salespeople at 18 Boston, Massachusetts, area dealerships were conducted in the context of potential vehicle purchase across six vehicle brands. Although some dealerships were making concerted efforts to introduce and educate customers on ADASs, a number of salespeople interviewed were not well positioned to provide adequate information to their customers. In select instances, salespeople explicitly provided inaccurate information on safety-critical systems. The dealerships in the sample that represented mass-market brands (Ford and Chevrolet) were the poorest performers. Sales staff at Subaru dealers were well trained and had print and digital content to drive consumer engagement. Educational staff, or “geniuses,” at BMW dealers presented a potentially innovative way of segmenting the sales process from technology education. In the absence of some technology introduction and education at dealerships, consumers may remain underinformed or misinformed about the disruptive safety technologies that are rapidly being introduced across the vehicle fleet.

Older drivers comprise an undue percentage of roadway crashes and fatalities, and existing data implicates decrements to situational awareness as one factor. Although forward attention in older drivers is well studied, rearward attention for this population is little explored. What evidence exists has suggested reduced mirror checks, especially under conditions of multitasking. Voice-enabled in-vehicle systems may represent a partial solution, requiring fewer resources and freeing drivers for behavior which maintains better rearward attention. The present study asked participants to drive on a highway in an instrumented vehicle under conditions of baseline driving, manual radio tuning, and radio tuning assisted by a voice-enabled interface. Results indicate that multitasking greatly reduced mirror checks for all groups. Older participants devoted a greater amount of time to mirror checks than younger participants when just driving, but dropped to levels similar to younger drivers while multitasking. Voice-enabled radio tuning was associated with reduced decrements in mirror checks for all age groups. Discussion centers around this new understanding of differing attentional patterns across lifespan, as well as the impact of voice-enabled interface.

This panel addresses current efforts associated with the evaluation of demands associated with the use of voice-based in-vehicle interfaces. As generally implemented, these systems are perhaps best characterized as mixed-mode interfaces drawing upon varying levels of auditory, vocal, visual, manual and cognitive resources. Numerous efforts have quantified demands associated with these systems and several have proposing evaluation methods. However, there has been limited discussion in the scientific literature on the benefits and drawbacks of various measures of workload; appropriate reference points for comparison (i.e. just driving, visual-manual versions of the task one is looking to replace, etc.); the relationship of demand characteristics to safety; and practical design considerations that can be gleamed from efforts to date. Panelists will discuss scientific progress in the topic areas. Each panelist is expected to present a brief perspective followed by discussion and Q&A.

To investigate possible relationships between drivers’ sensation seeking and glance behavior while interacting with human-machine interfaces, a total of 70 drivers’ eye-glance data, Sensation Seeking Scale (SSS), and Driver Behavior Questionnaire (DBQ) data were collected and analyzed. Participants conducted radio tuning tasks with two standard production interfaces while driving on a highway, and their glance allocations to defined regions were recorded and manually annotated. Results showed that sensation seeking scores were related with self-reported violation scores, off-road glance patterns, and driving speed: (1) violation scores of DBQ were positively correlated with sensation seeking, (2) mean and standard deviation of off-road glance duration were positively correlated with sensation seeking for younger drivers (under 40 years), (3) total off-road glance time per minute and number of off-road glances per minute were positively correlated with sensation seeking for older drivers (over 40 years), and (4) percentage of speed change was negatively correlated with sensation seeking for both younger and older drivers. The results indicate that sensation seeking is associated with drivers’ off-road glance patterns and driving behavior. These observations further highlight the relationship between personal traits and driver behavior.

A simulator study evaluated the extent to which the use of a smartwatch to initiate phone calls while driving impacts driver workload, attention, and performance, relative to visual-manual (VM) and auditory-vocal (AV) calling methods on a smartphone. Participants completed four calling tasks using each method while driving in a simulator and completing a remote detection response task (R-DRT). Among the 36 participants evaluated, R-DRT miss rates and reaction time were comparable between AV calling on the smartwatch and smartphone, but significantly higher using VM calling on the smartphone. Participants also exhibited more erratic driving behavior (lane position deviation and major steering wheel reversals) with smartphone VM calling compared to both AV calling methods. Finally, participants rated AV calling on both devices as entailing lower workload than VM calling on the smartphone. Overall, few differences emerged for the metrics reported between voice calling on a smartphone versus a smartwatch while driving.

New technologies are increasingly being implemented into the automobile. However, little is known about what factors most influence user acceptance. This study investigates factors and relationships described in the Technology Acceptance Model (TAM) to explore the effects of perceived usefulness and perceived ease-of-use on people’s attitudes and behavioral intentions to use new technologies in the automobile. In particular, this study draws upon in-vehicle voice interfaces as an example of rapidly evolving in-vehicle technologies. An experiment was conducted where an age- and gender-balanced sample of 80 participants completed tasks using voice interface in one of two vehicles while driving on-road. As part of the debriefing, participants answered a structured questionnaire about their experience with the technology. The results confirmed the applicability of TAM for describing the effects of perceived usefulness and perceived ease-of-use on attitudes and behavioral intentions to use the voice interface in the car, but also suggested a need for extending the model to include a direct effect of perceived ease-of-use on behavioral intentions. Also, comparative analyses between interface types and user characteristics showed that perceptions and acceptance may be influenced more by design attributes and interaction methods than by demographic characteristics such as age or gender.

A driving simulation study assessed the impact of vocally entering an alpha numeric destination into Google Glass relative to voice and touch-entry methods using a handheld Samsung Galaxy S4 smartphone. Driving performance (standard deviation of lateral lane position and longitudinal velocity) and reaction to a light detection response task (DRT) were recorded for a gender-balanced sample of 24 young adult drivers. Task completion time and subjective workload ratings were also measured. Using Google Glass for destination entry had a statistically higher miss rate than using the Samsung Galaxy S4 voice interface, the Google Glass method took less time to complete, and the two methods were given comparable workload ratings by participants. In agreement with previous work, both voice interfaces performed significantly better than touch entry; this was seen in workload ratings, task duration, lateral lane control, and DRT metrics. Finally, irrespective of device or modality, destination entry significantly decreased responsiveness to events in the forward scene (as measured by the DRT reaction time) as compared to the baseline driving. Keywords: Driver distraction; Language: en

This panel addresses a research methodology that is becoming more essential for driving safety research. The methods of driving simulation and naturalistic driving have been prominent and remain important, but their limitations are becoming more evident. On-the-road experimental methods offer some of the advantages of naturalistic driving while providing a degree of experimental control and manipulation more typical of simulator experiments. Five expert researchers will discuss both scientific and pragmatic issues in the conduct of on-the-road experimentation, making use of lessons learned from their own studies. Each panelist will present a brief perspective on the problem from the point of view of their experience and expertise. This will be followed by an open discussion period.

A simulation study compared 23 young adult drivers’ task completion time, mean glance time, number of glances, and percentage of long glances while performing a navigation entry task with a Garmin portable GPS system and a mobile navigation application (iOS 5 Google Maps) on an iPod Touch. We compared participants’ performance on these tasks using the National Highway Traffic Safety Administration (NHTSA) eye-glance acceptance criteria. We found that, irrespective of the device used, no participant was able to complete the task within the recommended total time window of 12 seconds. When entering a destination into the iOS interface, only 73.9% of the drivers meet the NHTSA criteria for long duration glances. With the Garmin system, 91.3% of the participants meet this criterion. All participants were able to maintain adequate mean off road glance durations. Finally, we compared the NHTSA recommended method of assessing all off road glances to more traditional methods of assessing glances only to the task interface. Differences between the two methods are discussed.

A simulation study compared 36 young adult drivers’ task completion time, eye behavior, and driving performance while dialing a flip-phone with tactile pushbuttons and an iPhone which provides a touch screen interface. Participants who often use a traditional manual button phone completed the dialing task faster when using the flip-phone compared to touch screen users using the iPhone. Females using the flip phone had the highest percentage of time spent with eyes on the road. Females were also less likely to exhibit glances greater than 2 seconds in duration with both phone types and particularly with the flip-phone. Some advantages may exist in a traditional tactile manual interface in terms of the percentage of time drivers kept their eyes on the road.

Objective: To assess the sensitivity of two physiological measures for discriminating between levels of cognitive demand under driving conditions across different age groups. Background: Previous driving research presents a mixed picture concerning the sensitivity of physiological measures for differentiating tasks with presumed differences in mental workload. Method: A total of 108 relatively healthy drivers balanced by gender and across three age groups (20–29, 40–49, 60–69) engaged in three difficulty levels of an auditory presentation–verbal response working memory task. Results: Heart rate and skin conductance level (SCL) both increased in a statistically significant fashion with each incremental increase in cognitive demand, whereas driving performance measures did not provide incremental discrimination. SCL was lower in the 40s and 60s age groups; however, the pattern of incremental increase with higher demand was consistent for heart rate and SCL across all age groups. Although each measure was quite sensitive at the group level, considering both SCL and heart rate improved detection of periods of heightened cognitive demand at the individual level. Conclusion: The data provide clear evidence that two basic physiological measures can be utilized under field conditions to differentiate multiple levels of objectively defined changes in cognitive demand. Methodological considerations, including task engagement, may account for some of the inconsistencies in previous research. Application: These findings increase the confidence with which these measures may be applied to assess relative differences in mental workload when developing and optimizing human machine interface (HMI) designs and in exploring their potential role in advanced workload detection and augmented cognition systems.

Growing evidence supports the idea that patterns of gaze are important to human-machine trust, as they are to human-to-human trust (LaFrance & Mayo, 1976; Kendon, 1967), and indeed potentially all primate social dynamics (Emery, 2000). A growing literature explores trust and gaze toward anthropomorphic robots (Mutlu et al., 2009; Stanton & Stevens, 2014; Van de Brule et al., 2014, Hancock et al., 2011). Less work has investigated far-more-common non-anthropomorphic systems, despite evidence suggesting that operators deploy the same trust patterns toward such interface that they might toward fellow humans (Nass, 1996; Fogg & Nass, 1997), and that they change patterns of visual allocation based upon that trust (Hergeth et al., 2016; Geitner at al., 2017). In critical operational settings, such as driving while multitasking, maximum safety and stability is associated with maximum visual attention devoted to the road (Hancock & Warm, 1989; Strayer, Drews & Johnston, 2003; Sawyer et al., 2014). Social gaze strategies deployed toward an interface suggest competition for these resources, and so applied consequences in terms of adopting appropriate information gathering strategies.

In a field study, we examined turn signal use characteristics during highway-lane changes across three age groups (20-29, 40-49, and 60-69) and under varying levels of secondary cognitive demand. Secondary cognitive demand decreased the likelihood of turn signal use, and delayed the onset and turnoff of the turn signals. These results suggest that added cognitive load can lead to a neglect of operational tasks in face of competition over limited resources. No major effects of age or relative level of secondary cognitive demand were observed. Language: en

Recordings of drivers' gaze under three levels of cognitive demand were captured under actual driving conditions in individuals in their 20's, 40's and 60's. Changes in the allocation of visual attention between single-task driving and the three levels of cognitive secondary tasks are summarized. Under the conditions studied here, gaze centralization varies by task difficulty and appears predominantly in the horizontal plane. The degree of horizontal gaze centralization with added cognitive workload is not related to age in the relatively healthy individuals studied.

This study examined the sensitivity of two physiological measures to the demand of structured cognitive tasks representing low, moderate and relatively high levels of secondary cognitive workload during actual highway driving. In a sample of 108 relatively healthy drivers, balanced by gender, and drawn from three age groups (20–29, 40–49, and 60–69), both heart rate and skin conductance were found to increase in a statistically significant and relatively linear fashion across 4 levels of workload. Issues associated with the study of sensitivity, task engagement and age are considered.

A comparison under actual driving conditions of 3 manual destination entry techniques (keypad, touch screen, and rotational controller) indicates that visual behavior measures are sensitive for detecting differences that are not apparent in standard driving performance measures when drivers are under low to moderate levels of load.

Cognitive distractions have been shown to affect drivers adversely and are a leading cause of accidents. Research indicates that drivers alter how they allocate their visual attention while engaging in secondary cognitive tasks. To evaluate the potential impact of secondary cognitive tasks on the allocation of drivers’ visual attention and on vehicle control, drivers were presented with increasingly complex forms of an auditory cognitive task while driving an instrumented vehicle. Measures of vehicle performance and eye gaze were assessed. Consistent with theories of visual tunneling, gaze distributions were significantly smaller while drivers performed certain levels of the secondary task; peripheral vision was thereby reduced. During the most difficult level of the secondary task, gaze dispersion was smaller than during any other level of the task. Changes in visual attention may provide earlier indications of cognitive distraction than changes in vehicle control, the latter of which were observed only during the most difficult level of the secondary task. Observed changes in vertical eye position suggest that drivers compensate for moderate cognitive demands by increasing their sight distance before further incremental increases in workload exceeded their abilities. In summary, the workload of a secondary cognitive task affected drivers’ visual attention. A low to moderate increase in workload was detectable as a change in gaze before vehicle control suffered. Gaze restriction appears related to the degree of cognitive workload. This work shows that visual attention is a potential method of detecting changes in driver state associated with cognitive workload.

This study examined the sensitivity of heart rate, skin conductance, and respiration rate as measures of mental workload in a simulated driving environment. Workload was systematically manipulated by using increasingly difficult levels of a secondary cognitive task. In a sample of 121 young adults, heart rate increased incrementally with increasing task demand. Significant elevations in skin conductance and respiration rate were also observed. At the lower levels of added workload, secondary task performance was nearly perfect and changes in indices of driving performance were negligible. At the highest level of workload, all three physiological measures appeared to plateau, and a subtle drop in simulated driving performance became detectable. Taken together, the pattern of results indicates that physiological measures can be sensitive to changes in workload before the appearance of clear decrements in driving performance. These findings further highlight a role for physiological monitoring as a means to measure mental workload in product design and functionality research. They also support work exploring the potential for incorporating physiological measures of driver workload and attentional state in future safety systems.

Many older adults experience declines in their flexibility, which in turn can affect their safe operation of a vehicle and their driving performance. Previous research has shown that reduced trunk flexibility is associated with greater accident risks. To explore further questions about and measures of spinal flexibility, differences in spinal flexibility between younger and older drivers were explored in two contexts: a stretching exercise and a real driving maneuver. Results indicated that younger drivers had on average greater stretch spinal flexibility than older drivers, but that during the driving maneuver, study participants did whatever necessary, such as bracing themselves on the seatback or steering wheel, to overcome any flexibility limitations to complete the driving task. Using a multivariate regression mode, the degree of spinal rotation in the stretch and driving tasks were significantly affected by age and by the use of the vehicle to brace while rotating. In the stretch tasks, self-reported health and driving frequency were jointly statistically significant predictors of degree of rotation. In terms of driving, however, the use of bracing suggests that drivers may have evolved strategies to compensate for any physical limitations they may experience to continue driving.

Decisions about limiting or stopping driving are among the most difficult that older adults face. There is little research related to this decision making of older drivers, family members, and other trusted individuals who contribute to the decision. To report on the preferences that older drivers have for conversations with others with concerns about their driving, this paper draws on a series of focus groups and a U.S. survey of drivers age 50 and older. A majority of survey respondents indicated that they engaged in some degree of voluntary self-regulation of their driving. People generally preferred to be approached by individual family members as opposed to those outside the family for conversations about their driving. In terms of preferences for who should speak with the older driver, differences emerged on the basis of household status. Most older adults who were married preferred to hear first from a spouse, although the choice was not universal. Doctors and adult children were also preferred choices for conversations. The paper concludes with a discussion of pragmatic implications of the research for conducting conversations with older adults about concerns with their driving.