Assessing drivers' response during automated driver support system failures with non-driving tasks

IntroductionWith the increase in automated driver support systems, drivers are shifting from operating their vehicles to supervising their automation. As a result, it is important to understand how drivers interact with these automated systems and evaluate their effect on driver responses to safety critical events. This study aimed to identify how drivers responded when experiencing a safety critical event in automated vehicles while also engaged in non-driving tasks.MethodIn total 48 participants were included in this driving simulator study with two levels of automated driving: (a) driving with no automation and (b) driving with adaptive cruise control (ACC) and lane keeping (LK) systems engaged; and also two levels of a non-driving task (a) watching a movie or (b) no non-driving task. In addition to driving performance measures, non-driving task performance and the mean glance duration for the non-driving task were compared between the two levels of automated driving.ResultsDrivers using the automated systems responded worse than those manually driving in terms of reaction time, lane departure duration, and maximum steering wheel angle to an induced lane departure event. These results also found that non-driving tasks further impaired driver responses to a safety critical event in the automated system condition.ConclusionIn the automated driving condition, driver responses to the safety critical events were slower, especially when engaged in a non-driving task.Practical applicationTraditional driver performance variables may not necessarily effectively and accurately evaluate driver responses to events when supervising autonomous vehicle systems. Thus, it is important to develop and use appropriate variables to quantify drivers' performance under these conditions.     

Role of motorcycle type in fatal motorcycle crashes

IntroductionMotorcycles vary in design and performance capability, and motorcyclists may select certain motorcycle types based on driving preferences. Conversely, motorcycle performance capability may influence the likelihood of risky driving behaviors such as speeding. Both mechanisms may affect fatal crash risk when examined by motorcycle type. Although it was not possible to estimate the effect of each mechanism, the current study analyzed fatal crash data for evidence of motorcycle type differences in risky driving behaviors and risk of driver death.MethodsStreet legal motorcycles were classified into 10 types based on design characteristics and then further grouped as cruiser/standard, touring, sport touring, sport/unclad sport, supersport, and all others. For each motorcycle type, driver death rates per 10,000 registered vehicle years and the prevalence of fatal crash characteristics such as speeding were analyzed. Differences among motorcycle types concerning the effect of engine displacement were examined using Poisson regression.ResultsOverall, driver death rates for supersport motorcycles were four times as high as those for cruiser/standard motorcycles. Fatally injured supersport drivers were most likely to have been speeding and most likely to have worn helmets, but least likely to have been impaired by alcohol compared with drivers of other motorcycle types. The patterns in driver factors held after accounting for the effects of age and gender. Increased engine displacement was associated with higher driver death rates for each motorcycle type.ConclusionStrong effects of motorcycle type were observed on driver death rates and on the likelihood of risky driving behaviors such as speeding and alcohol impairment. Although the current study could not completely disentangle the effects of motorcycle type and rider characteristics such as age on driver death rates, the effects of both motorcycle type and rider age on the likelihood of risky driving behaviors were observed among fatally injured motorcycle drivers.Impact on IndustryCertain motorcycle designs, particularly supersport motorcycles, are associated with increases in risky driving behaviors and higher driver death rates. At present, there are no proven countermeasures for this situation. However, existing countermeasures such as helmet laws and automated speed enforcement could have a substantial benefit.     

Parental perceptions of teen driving: Restrictions,worry and influence

IntroductionParents play a critical role in preventing crashes among teens. Research of parental perceptions and concerns regarding teen driving safety is limited. We examined results from the 2013 Summer ConsumerStyles survey that queried parents about restrictions placed on their teen drivers, their perceived level of “worry” about their teen driver’s safety, and influence of parental restrictions regarding their teen’s driving.MethodsWe produced frequency distributions for the number of restrictions imposed, parental “worry,” and influence of rules regarding their teen’s driving, reported by teen’s driving license status (learning to drive or obtained a driver’s license). Response categories were dichotomized because of small cell sizes, and we ran separate log-linear regression models to explore whether imposing all four restrictions on teen drivers was associated with either worry intensity (“a lot” versus “somewhat, not very much or not at all”) or perceived influence of parental rules (“a lot” versus “somewhat, not very much or not at all”).ResultsAmong the 456 parent respondents, 80% reported having restrictions for their teen driver regarding use of safety belts, drinking and driving, cell phones, and text messaging while driving. However, among the 188 parents of licensed teens, only 9% reported having a written parent-teen driving agreement, either currently or in the past. Worrying “a lot” was reported less frequently by parents of newly licensed teens (36%) compared with parents of learning teens (61%).Conclusions and Practical ApplicationsParents report having rules and restrictions for their teen drivers, but only a small percentage formalize the rules and restrictions in a written parent-teen driving agreement. Parents worry less about their teen driver’s safety during the newly licensed phase, when crash risk is high as compared to the learning phase. Further research is needed into how to effectively support parents in supervising and monitoring their teen driver.     

Potential effects of lowering the BAC limit on injuries,fatalities, and costs

IntroductionPotential health and cost impacts of lowering the BAC limit for U.S. drivers below .08% were explored through analyses of reductions in crash incidence, injury severity, and costs based on five scenarios with varying assumptions about how the change to a .05% BAC limit might affect alcohol-impaired driving.MethodsDistribution of crashes by injury level and highest driver or non-occupant BAC levels for 2010, together with unit crash costs provided a base for comparison. Scenario 1 assumed all alcohol-impaired driving ceased; scenario 2 assumed all drivers obeyed the law, and scenario 3 assumed decreases in driver BAC levels would be limited to those who had been driving near the legal limit before the change. Scenario 4 was based on changes in driver BAC levels associated with a 08% to .05% BAC limit change in Australia, and scenario 5 was based on changes in alcohol-related crashes associated with the change to the .08% BAC limit in the United States. The number of casualties prevented in each scenario was estimated using relative risks of crash involvement, and changes in societal costs were estimated using the unit costs.ResultsReductions ranging from 71% to 99% in fatalities, injuries, and costs related to alcohol-impaired driving were estimated in scenarios 1 and 2. Scenarios 3–5 produced smaller reductions ranging from 4% to 16% for alcohol-impaired fatalities, injuries, and costs.ConclusionThe wide difference between the outcomes of the two sets of scenarios reflects the sensitivity of BAC policy benefits to driver compliance behavior.Practical applicationThe quantification of the reduction in the number and costs of traffic crash casualties in the set of behavioral scenarios explored in this research can inform policymakers about the extent and limits of benefits achievable by lowering the BAC limits as they consider strategies to reduce alcohol-impaired driving.     

Traffic environment and demographic factors affecting impaired driving and crashes

IntroductionData availability has forced researchers to examine separately the role of alcohol among drivers who crashed and drivers who did not crash. Such a separation fails to account fully for the transition from impaired driving to an alcohol-related crash.MethodIn this study, we analyzed recent data to investigate how traffic-related environments, conditions, and drivers’ demographics shape the likelihood of a driver being either involved in a crash (alcohol impaired or not) or not involved in a crash (alcohol impaired or not). Our data, from a recent case–control study, included a comprehensive sampling of the drivers in nonfatal crashes and a matched set of comparison drivers in two U.S. locations. Multinomial logistic regression was applied to investigate the likelihood that a driver would crash or would not crash, either with a blood alcohol concentration (BAC) = .00 or with a BAC ≥ .05.ConclusionsTo our knowledge, this study is the first to examine how different driver characteristics and environmental factors simultaneously contribute to alcohol use by crash-involved and non-crash-involved drivers. This effort calls attention to the need for research on the simultaneous roles played by all the factors that may contribute to motor vehicle crashes.     

Aggressive driving behaviour in young drivers (aged 16 through 25) involved in fatal crashes

IntroductionWe wished to determine the extent to which number of passengers, driver age, and sex were associated with aggressive driving actions (ADAs) in young drivers involved in a fatal crash.MethodsWe used U.S. fatal-crash data from Fatality Analysis Reporting System (FARS), 1991 –2008. Proxy measures of aggressive driving included ADA presence and speed differential (posted speed limit minus estimated travel speed). We examined the odds of an ADA and speed differential in young drivers (aged 16 to 25) by passenger status.ResultsCompared to driving alone young drivers (aged 16) had increased odds of an ADA between 14% (OR: 1.14; 95% CI: 1.07; 1.22) and 95% (OR: 1.95; 95% CI: 1.40; 2.74) when accompanied by one and five passengers, respectively. Further, carrying a higher number of passengers was a stronger predictor of speeding in younger drivers.ConclusionsThis study supports the use of graduated licensing approaches. Specifically, developing interventions to reduce aggressive driving appear imperative.Impact on IndustryWhile the results of our study support the use of graduated licensing approaches there is room for improvement. Our study indicates that tackling impaired driving is not sufficient to drastically reduce aggressive driving among the youngest drivers. Further research on young drivers is required to understand the influence of peers and the role of gender on driving behavior. Strategies to reduce aggressive driving behaviors among the youngest drivers may not only prevent crashes during their early driving careers but may also translate into a reduced crash risk over their lifetime.     

Associations between driving under the influence or riding with an impaired driver and future substance use among adolescents

Objective: Risky driving behaviors among adolescents, such as riding with a drinking or impaired driver (RWID) or driving while under the influence (DUI) of alcohol or drugs, are significant public health concerns. Few studies have examined associations of RWID and DUI with future substance use and problems after controlling for baseline substance use. Given that the DUI/RWDD event may be a teachable moment to prevent future consequences (e.g., when injured or arrested), it is important to understand how this risk behavior relates to subsequent use and problems. This study therefore examined characteristics of adolescents who reported DUI and RWID and assessed their risk of future alcohol and marijuana use and consequences 6 months later.

Methods: Participants were 668 adolescents aged 12 to 18 (inclusive) recruited at 1 of 4 primary care clinics in Pittsburgh and Los Angeles as part of a larger randomized controlled trial. They completed surveys about their health behaviors at baseline and 6 months after baseline. We examined baseline characteristics of adolescents who reported DUI and RWID and then assessed whether past-year DUI and RWID at baseline were associated with alcohol and marijuana use and consequences 6 months after baseline.

Results: Fifty-eight percent of participants were female, 56% were Hispanic, 23% were Black, 14% were White, 7% were multiethnic or other, and the average age was 16 years (SD?=?1.9). At baseline, participants who reported RWID or DUI were more likely to be older, report past-year use of alcohol and marijuana, and more likely to have an alcohol use disorder or cannabis use disorder versus those who did not report RWID or DUI, respectively. At 6-month follow-up and after controlling for baseline demographics and baseline alcohol use, RWID was associated with more frequent drinking episodes in the past 3 months and greater number of drinks in the past month when they drank heavily. DUI at baseline was associated with more frequent heavy drinking episodes and alcohol and marijuana consequences 6 months later.

Conclusions: RWID and DUI are significantly associated with greater alcohol and marijuana use over time. This study highlights that teens may be at higher risk for problem substance use in the future even if they ride with someone who is impaired. Prevention and intervention efforts for adolescents need to address both driving under the influence and riding with an impaired driver to prevent downstream consequences.     

Crashes and near-crashes on horizontal curves along rural two-lane highways: Analysis of naturalistic driving data

IntroductionPrior research has shown the probability of a crash occurring on horizontal curves to be significantly higher than on similar tangent segments, and a disproportionally higher number of curve-related crashes occurred in rural areas. Challenges arise when analyzing the safety of horizontal curves due to imprecision in integrating information as to the temporal and spatial characteristics of each crash with specific curves.MethodsThe second Strategic Highway Research Program(SHRP 2) conducted a large-scale naturalistic driving study (NDS),which provides a unique opportunity to better understand the contributing factors leading to crash or near-crash events. This study utilizes high-resolution behavioral data from the NDS to identify factors associated with 108 safety critical events (i.e., crashes or near-crashes) on rural two-lane curves. A case-control approach is utilized wherein these events are compared to 216 normal, baseline-driving events. The variables examined in this study include driver demographic characteristics, details of the traffic environment and roadway geometry, as well as driver behaviors such as in-vehicle distractions.ResultsLogistic regression models are estimated to discern those factors affecting the likelihood of a driver being crash-involved. These factors include high-risk behaviors, such as speeding and visual distractions, as well as curve design elements and other roadway characteristics such as pavement surface conditions.ConclusionsThis paper successfully integrated driver behavior, vehicle characteristics, and roadway environments into the same model. Logistic regression model was found to be an effective way to investigate crash risks using naturalistic driving data.Practical ApplicationsThis paper revealed a number of contributing factors to crashes on rural two-lane curves, which has important implications in traffic safety policy and curve geometry design. This paper also discussed limitations and lessons learned from working with the SHRP 2 NDS data. It will benefit future researchers who work with similar type of data.     

A novel driver hazard perception sensitivity model based on drivers’ characteristics: A simulator study

Objective: Considering the high annual number of fatal driving accidents in Iran, any approach for reducing the number or severity of driving accidents is a positive step toward decreasing accident-related losses. Accidents can often be avoided by a timely reaction of the driver. One of the steps before reacting to a hazard is perception. Some driver characteristics may affect road hazard perception. In this research, it was assumed that various driver characteristics, including demographic characteristics and cognitive characteristics, have an impact on driver perception.

Methods: The driving simulator used in this research provides various scenarios; for example, passing a pedestrian or animal across the road or placing fixed objects in a 2-lane separated rural road for 2 groups of experienced and inexperienced drivers under day and night lighting conditions. The go/no-go test was carried out in order to assess drivers’ attention to driving tasks and inhibitory control. A structural equation model (SEM) was used to estimate the relation between driver characteristics and sensitivity to road hazard perception. A new hazard perception index was proposed based on the time intervals in the hazard vulnerability.

Results: The results show that the most effective variables in the analysis of sensitivity to hazard perception are driving experience (in kilometers) during the last 3 years and road lighting conditions. Moreover, hazard perception sensitivity was improved by better inhibitory control, selective attention, and decision making, more carefulness, the average amount of daily sleep, and marital status.

Conclusion: The results of this research may be useful in educating and advertising programs. It also could enhance sensitivity to perception of hazards such as pedestrians, animals, and fixed obstacles among young and novice drivers.     

The antecedents,experience, and coping strategies of driver boredom in young adult males

IntroductionRoad crash statistics are evidence of the severe consequences resulting from human error, especially among young adult males. Drivers perform best and safest when they are adequately engaged in the driving task. Boredom and a lack of engagement in the driving task may cause risk taking and phone use. However, the antecedents to driver boredom, the subjective experience itself, as well as the coping strategies to combat boredom are not well understood. The aim of this study was to investigate these aspects.MethodWe carried out a qualitative study in a simulated, safe, yet highly immersive driving environment. The 24 participants included male drivers aged 18 to 25 susceptible to risky driving and phone use. A phenomenological framework was used to analyze their accounts of the experience of boredom while driving.ResultsResults indicate that situations giving rise to driver boredom include low traffic, slow or constant speed, and routine drives. Feelings comprising the experience were frustration, vigilance, relaxing, autopilot, mind wandering, and discomfort. Coping mechanisms manifest themselves in approach strategies related to the driving task such as speeding, which are often dangerous, and avoidance strategies, which include phone use.ConclusionsWe conclude that driver boredom bears similarities to the experience of boredom at work (unlike boredom at home) due to the situational constraints, where people feel stuck, trapped, or obliged to remain vigilant.Practical applicationsThe findings present an opportunity for the road safety and automotive technology community to address the issue of under-stimulation through safety interventions aimed at increased task engagement. Our work can also aid in investigating driver experiences in partially automated driving, which is likely to induce boredom as well.     

Assessing driver behavior using shrp2 adverse weather data

IntroductionThis study explored how drivers adapt to inclement weather in terms of driving speed, situational awareness, and visibility as road surface conditions change from dry to slippery and visibility decreases. The proposed work mined existing data from the SHRP 2 NDS for drivers who were involved in weather-related crash and near-crash events. Baseline events were also mined to create related metadata necessary for behavioral comparisons. Methods: Researchers attempted, to the greatest extent possible, to match non-adverse-weather driving scenarios that are similar to the crash and near-crash event for each driver. The ideal match scenario would be at a day prior to the crash during non-adverse weather conditions having the same driver, at the same time of day, with the same traffic level on the same road on which the crash or near-crash occurred. Once the matched scenarios have been identified, a detailed analysis will be performed to determine how a driver’s behavior changed from normal driving to inclement-weather driving. Results: Data collected indicated that, irrespective of site location (i.e., state), most crashes and near-crashes occurred in rain, with only about 12% occurring in snowy conditions. Also, the number of near-crashes was almost double the number of crashes showing that many drivers were able to avoid a crash by executing an evasive maneuver such as braking or steering. Conclusions: Most types of near crashes were rear-end and sideswipe avoidance epochs, as the drivers may have had a difficult time merging or trying to change lanes due to low visibility or traffic. Hard braking combined with swerving were the most commonly used evasive maneuvers, occurring when drivers did not adjust their speeds accordingly for specific situations. Practical applications: Results from this study are expected to be utilized to educate and guide drivers toward more confident and strategic driving behavior in adverse weather.     

Using naturalistic driving study data to investigate the impact of driver distraction on driver's brake reaction time in freeway rear-end events in car-following situation

IntroductionThe rear-end crash is one of the most common freeway crash types, and driver distraction is often cited as a leading cause of rear-end crashes. Previous research indicates that driver distraction could have negative effects on driving performance, but the specific association between driver distraction and crash risk is still not fully revealed. This study sought to understand the mechanism by which driver distraction, defined as secondary task distraction, could influence crash risk, as indicated by a driver's reaction time, in freeway car-following situations.MethodA statistical analysis, exploring the causal model structure regarding drivers’ distraction impacts on reaction times, was conducted. Distraction duration, distraction scenario, and secondary task type were chosen as distraction-related factors. Besides, exogenous factors including weather, visual obstruction, lighting condition, traffic density, and intersection presence and endogenous factors including driver age and gender were considered.ResultsThere was an association between driver distraction and reaction time in the sample freeway rear-end events from SHRP 2 NDS database. Distraction duration, the distracted status when a leader braked, and secondary task type were related to reaction time, while all other factors showed no significant effect on reaction time.ConclusionsThe analysis showed that driver distraction duration is the primary direct cause of the increase in reaction time, with other factors having indirect effects mediated by distraction duration. Longer distraction duration, the distracted status when a leader braked, and engaging in auditory-visual-manual secondary task tended to result in longer reaction times.Practical applicationsGiven drivers will be distracted occasionally, countermeasures which shorten distraction duration or avoid distraction presence while a leader vehicle brakes are worth considering. This study helps better understand the mechanism of freeway rear-end events in car-following situations, and provides a methodology that can be adopted to study the association between driver behavior and driving features.     

Improvement of driver active interventions during automated driving by displaying trajectory pointers—A driving simulator study

Abstract

Objective: The handover of vehicle control from automated to manual operation is a critical aspect of interaction between drivers and automated driving systems (ADS). In some cases, it is possible that the ADS may fail to detect an object. In this event, the driver must be aware of the situation and resume control of the vehicle without assistance from the system. Consequently, the driver must fulfill the following 2 main roles while driving: (1) monitor the vehicle trajectory and surrounding traffic environment and (2) actively take over vehicle control if the driver identifies a potential issue along the trajectory. An effective human–machine interface (HMI) is required that enables the driver to fulfill these roles. This article proposes an HMI that constantly indicates the future position of the vehicle.

Methods: This research used the Toyota Dynamic Driving Simulator to evaluate the effect of the proposed HMI and compares the proposed HMI with an HMI that notifies the driver when the vehicle trajectory changes. A total of 48 test subjects were divided into 2 groups of 24: One group used the HMI that constantly indicated the future position of the vehicle and the other group used the HMI that provided information when the vehicle trajectory changed.

The following instructions were given to the test subjects: (1) to not hold the steering wheel and to allow the vehicle to drive itself, (2) to constantly monitor the surrounding traffic environment because the functions of the ADS are limited, and (3) to take over driving if necessary.

The driving simulator experiments were composed of an initial 10-min acclimatization period and a 10-min evaluation period. Approximately 10?min after the start of the evaluation period, a scenario occurred in which the ADS failed to detect an object on the vehicle trajectory, potentially resulting in a collision if the driver did not actively take over control and manually avoid the object.

Results: The collision avoidance rate of the HMI that constantly indicated the future position of the vehicle was higher than that of the HMI that notified the driver of trajectory changes, χ² = 6.38, P < .05. The steering wheel hands-on and steering override timings were also faster with the proposed HMI (t test; P < .05).

Conclusions: This research confirmed that constantly indicating the position of the vehicle several seconds in the future facilitates active driver intervention when an ADS is in operation.     

Older driver fitness-to-drive evaluation using naturalistic driving data

ProblemAs our driving population continues to age, it is becoming increasingly important to find a small set of easily administered fitness metrics that can meaningfully and reliably identify at-risk seniors requiring more in-depth evaluation of their driving skills and weaknesses.MethodSixty driver assessment metrics related to fitness-to-drive were examined for 20 seniors who were followed for a year using the naturalistic driving paradigm. Principal component analysis and negative binomial regression modeling approaches were used to develop parsimonious models relating the most highly predictive of the driver assessment metrics to the safety-related outcomes observed in the naturalistic driving data.ResultsThis study provides important confirmation using naturalistic driving methods of the relationship between contrast sensitivity and crash-related events.Practical applicationsThe results of this study provide crucial information on the continuing journey to identify metrics and protocols that could be applied to determine seniors' fitness to drive.     

Comparison of self-report and objective measures of driving behavior and road safety: A systematic review

IntroductionThis research systematically reviewed the existing literature in regards to studies which have used both self-report and objective measures of driving behavior. The objective of the current review was to evaluate disparities or similarities between self-report and objective measures of driving behavior.MethodsSearches were undertaken in the following electronic databases, PsycINFO, PubMed, and Scopus, for peer-reviewed full-text articles that (1) focused on road safety, and (2) compared both subjective and objective measures of driving performance or driver safety. A total of 22,728 articles were identified, with 19 articles, comprising 20 studies, included as part of the review.ResultsThe research reported herein suggested that for some behaviors (e.g., driving in stressful situations) there were similarities between self-report and objective measures while for other behaviors (e.g., sleepiness and vigilance states) there were differences between these measurement techniques. In addition, findings from some studies suggested that in-vehicle devices may be a valid measurement tool to assess driving exposure in older drivers.ConclusionsFurther research is needed to examine the correspondence between self-report and objective measures of driving behavior. In particular, there is a need to increase the number of studies which compare “like with like” as it is difficult to draw comparisons when there are variations in measurement tools used.Practical applicationsIncorporating a range of objective and self-report measurements tools in research would help to ensure that the methods used offer the most reliable measures of assessing on-road behaviors.     

Characteristics of teens-with-teens fatal crashes in the United States, 2005–2010

BackgroundMore than 40% of fatal crashes of 16- and 17-year-old drivers occur when transporting teenagers. Characteristics of this predominant crash type and prevention possibilities are described, based on data from fatal crashes in the United States during 2005–2010.ResultsFifty-seven percent of 16- and 17-year old drivers in fatal crashes had at least one passenger. Most commonly, all passengers were ages 13–19 (42% of all drivers and 73% of those with passengers). Of fatal crashinvolved drivers with teenage passengers and no passengers of other ages, 56% had one passenger, 24% had two, and 20% had three or more. Most frequently, passengers were the same sex and within one year of the driver. Risk factors involving speeding, alcohol use, late-night driving, lack of a valid license, seat belt non-use, and crash responsibility were more prevalent with teenage passengers than when driving alone, and the prevalence of these factors increased with the number of teenage passengers. Many risk factors were most prevalent with passengers ages 20–29, although few crashes had this occupant configuration. Risk factors were least prevalent with a passenger 30 or older.DiscussionFatal crashes of 16- and 17-year-old drivers with teen passengers are a common crash scenario, despite passenger restrictions in 42 states and the District of Columbia during some or all of the study period. The proportion of these fatal crashes decreased slightly from 46% in 1995 (pre-GDL) to 43% in 2010 and showed no signs of decreasing during the six-year study period (range 41% to 43%).Practical applicationsExisting passenger restrictions are relatively weak and could be strengthened. Fatal crashes involving teen passengers, especially multiple passengers, are more likely to involve alcohol, late-night driving, driver error, and invalid licensure, so stepped-up enforcement of existing laws involving these behaviors might reduce the prevalence of such crashes.     

Driver Behavior During Overtaking Maneuvers from the 100-Car Naturalistic Driving Study

Objective: Lane changes with the intention to overtake the vehicle in front are especially challenging scenarios for forward collision warning (FCW) designs. These overtaking maneuvers can occur at high relative vehicle speeds and often involve no brake and/or turn signal application. Therefore, overtaking presents the potential of erroneously triggering the FCW. A better understanding of driver behavior during lane change events can improve designs of this human–machine interface and increase driver acceptance of FCW. The objective of this study was to aid FCW design by characterizing driver behavior during lane change events using naturalistic driving study data.

Methods: The analysis was based on data from the 100-Car Naturalistic Driving Study, collected by the Virginia Tech Transportation Institute. The 100-Car study contains approximately 1.2 million vehicle miles of driving and 43,000 h of data collected from 108 primary drivers. In order to identify overtaking maneuvers from a large sample of driving data, an algorithm to automatically identify overtaking events was developed. The lead vehicle and minimum time to collision (TTC) at the start of lane change events was identified using radar processing techniques developed in a previous study. The lane change identification algorithm was validated against video analysis, which manually identified 1,425 lane change events from approximately 126 full trips.

Results: Forty-five drivers with valid time series data were selected from the 100-Car study. From the sample of drivers, our algorithm identified 326,238 lane change events. A total of 90,639 lane change events were found to involve a closing lead vehicle. Lane change events were evenly distributed between left side and right side lane changes. The characterization of lane change frequency and minimum TTC was divided into 10 mph speed bins for vehicle travel speeds between 10 and 90 mph. For all lane change events with a closing lead vehicle, the results showed that drivers change lanes most frequently in the 40–50 mph speed range. Minimum TTC was found to increase with travel speed. The variability in minimum TTC between drivers also increased with travel speed.

Conclusions: This study developed and validated an algorithm to detect lane change events in the 100-Car Naturalistic Driving Study and characterized lane change events in the database. The characterization of driver behavior in lane change events showed that driver lane change frequency and minimum TTC vary with travel speed. The characterization of overtaking maneuvers from this study will aid in improving the overall effectiveness of FCW systems by providing active safety system designers with further understanding of driver action in overtaking maneuvers, thereby increasing system warning accuracy, reducing erroneous warnings, and improving driver acceptance.     

Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness

Objective: Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives: to evaluate the (1) suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment and (2) relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time.

Methods: The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5?min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student’s t test.

Results: The ability of HRV indices to discriminate between alert and sleepy drivers does not differ significantly depending on which outlier detection and spectral transformation methods are used. As expected, with increasing sleepiness, the heart rate decreased, whereas heart rate variability overall increased. Furthermore, HRV parameters representing the parasympathetic branch of the autonomous nervous system increased. An unexpected finding was that parameters representing the sympathetic branch of the autonomous nervous system also increased with increasing KSS level. We hypothesize that this increment was due to stress induced by trying to avoid an incident, because the drivers were in real driving situations.

Conclusions: The association of HRV indices to KSS did not depend on the preprocessing strategy. No preprocessing method showed superiority for HRV association to driver sleepiness. This was also true for combinations of methods for frequency domain HRV indices. The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.     

Classification of driving workload affected by highway alignment conditions based on classification and regression tree algorithm

Objective: Guaranteeing a safe and comfortable driving workload can contribute to reducing traffic injuries. In order to provide safe and comfortable threshold values, this study attempted to classify driving workload from the aspects of human factors mainly affected by highway geometric conditions and to determine the thresholds of different workload classifications. This article stated a hypothesis that the values of driver workload change within a certain range.

Methods: Driving workload scales were stated based on a comprehensive literature review. Through comparative analysis of different psychophysiological measures, heart rate variability (HRV) was chosen as the representative measure for quantifying driving workload by field experiments. Seventy-two participants (36 car drivers and 36 large truck drivers) and 6 highways with different geometric designs were selected to conduct field experiments. A wearable wireless dynamic multiparameter physiological detector (KF-2) was employed to detect physiological data that were simultaneously correlated to the speed changes recorded by a Global Positioning System (GPS) (testing time, driving speeds, running track, and distance). Through performing statistical analyses, including the distribution of HRV during the flat, straight segments and P-P plots of modified HRV, a driving workload calculation model was proposed. Integrating driving workload scales with values, the threshold of each scale of driving workload was determined by classification and regression tree (CART) algorithms.

Results: The driving workload calculation model was suitable for driving speeds in the range of 40 to 120 km/h. The experimental data of 72 participants revealed that driving workload had a significant effect on modified HRV, revealing a change in driving speed. When the driving speed was between 100 and 120 km/h, drivers showed an apparent increase in the corresponding modified HRV. The threshold value of the normal driving workload K was between ?0.0011 and 0.056 for a car driver and between ?0.00086 and 0.067 for a truck driver.

Conclusion: Heart rate variability was a direct and effective index for measuring driving workload despite being affected by multiple highway alignment elements. The driving workload model and the thresholds of driving workload classifications can be used to evaluate the quality of highway geometric design. A higher quality of highway geometric design could keep driving workload within a safer and more comfortable range. This study provided insight into reducing traffic injuries from the perspective of disciplinary integration of highway engineering and human factor engineering.