Standard Deviation Of Lane Position Research Articles

Effect of human–machine interface infotainment systems and automated vehicles on driver distraction

AbstractDriver distraction is intricately linked to human behavior and cognitive ergonomics, as it explores how human engagement with various stimuli influences attention and decision‐making processes while driving. The main purpose of this study is to comprehensively explore whether using Human–Machine Interface infotainment systems in automated vehicles can affect driver distraction. To this end, driver distraction was measured by driving performance features (speed, lane position, and reaction time), behavioral features (fixation time and pupil dilation), physiological features (changes in oxyhemoglobin), and subjective assessment (NASA‐TLX workload). Twenty‐one participants equipped with an eye tracker and functional near‐infrared spectroscopy drove a driving simulator in the current investigation. The results revealed that interacting with the infotainment systems significantly affects the drivers' average speed (F2,40 = 13.60, p < .0001), reaction time (F2,40 = 4.74, p = .0142), fixation time (F2,40 = 88.61, p < .0001), pupil dilation (F2,28 = 3.63, p = .0356), and workload (F2,40 = 14.40, p < .0001). Moreover, driving mode significantly affects drivers' speed deviation (F2,40 = 6.12, p = .0048), standard deviation of lane position (F2,40 = 10.57, p = .0002), fixation time (F2,40 = 36.71, p < .0001), and workload (F2,40 = 28.08, p < .0001). Drawing from the findings of this article and emphasizing human‐centric design principles, researchers and engineers can craft automotive technologies that are intuitive, effective, and safer. This is vital for mitigating driver distraction and guaranteeing the beneficial influence of automated vehicles on both road safety and the overall driving experience.

Assessing the impact of cannabis use on freeway driving performance and practices: A comparative analysis with placebo and alcohol-influenced driving

Objective The objectives of this study were 1) to identify the effects cannabis has on driving performance and individual motor practices when on the freeway compared to placebo and 2) to bring context to the effects of cannabis on driving by comparing effect sizes to those of alcohol. Methods Data for analysis was collected from a study of fifty-three participants with a history of tetrahydrocannabinol (THC) cannabis use who completed three visits in randomized order (placebo (0% THC), 6.18% THC, and 10.5% THC). Data for the alcohol analysis was from a subset of eighteen of these participants with a history of recent alcohol use that completed a fourth alcohol visit that targeted a .05 g/210L breath alcohol content (BrAC) during the drive. Comparisons were made using an analysis of variance approach with the SAS General Linear Models Procedure. Cohen’s d effect sizes were calculated for the cannabis and alcohol conditions relative to placebo for both the full sample and alcohol subset. Results Standard deviation of lane position (SDLP) for cannabis significantly increased compared to placebo and the effect size was comparable to that of alcohol at .05 BrAC. Lane departures for cannabis significantly increased relative to placebo as did the time out of the lane. Cannabis use resulted in an increased amount of time at 10% or more below the speed limit for the 6.18% THC condition. Relative to alcohol, cannabis produced more time at slower speeds and less time at speeds more than 10% above the speed limit. Conclusions Multiple factors of lateral and longitudinal vehicle control on the freeway showed statistical significance. Drivers under the influence of cannabis exhibited higher rates of driving errors but also showed more cautious behaviors such as generally lower speeds on the freeway. Compared with alcohol, effect sizes varied. For longitudinal control, there were larger effect sizes for alcohol with speed effects in opposite directions, but relatively equivalent effect sizes for lateral control and driving errors associated with lane keeping.

Age-related differences in takeover performance: A comparative analysis of older and younger drivers in prolonged partially automated driving

Objective Vehicle automation technologies have the potential to address the mobility needs of older adults. However, age-related cognitive declines may pose new challenges for older drivers when they are required to take back or “takeover” control of their automated vehicle. This study aims to explore the impact of age on takeover performance under partially automated driving conditions and the interaction effect between age and voluntary non-driving-related tasks (NDRTs) on takeover performance. Method A total of 42 older drivers (M = 65.5 years, SD = 4.4) and 40 younger drivers (M = 37.2 years, SD = 4.5) participated in this mixed-design driving simulation experiment (between subjects: age [older drivers vs. younger drivers] and NDRT engagement [road monitoring vs. voluntary NDRTs]; within subjects: hazardous event occurrence time [7.5th min vs. 38.5th min]). Results Older drivers exhibited poorer visual exploration performance (i.e., longer fixation point duration and smaller saccade amplitude), lower use of advanced driving assistance systems (ADAS; e.g., lower percentage of time adaptive cruise control activated [ACCA]) and poorer takeover performance (e.g., longer takeover time, larger maximum resulting acceleration, and larger standard deviation of lane position) compared to younger drivers. Furthermore, older drivers were less likely to experience driving drowsiness (e.g., lower percentage of time the eyes are fully closed and Karolinska Sleepiness Scale levels); however, this advantage did not compensate for the differences in takeover performance with younger drivers. Older drivers had lower NDRT engagement (i.e., lower percentage of fixation time on NDRTs), and NDRTs did not significantly affect their drowsiness but impaired takeover performance (e.g., higher collision rate, longer takeover time, and larger maximum resulting acceleration). Conclusions These findings indicate the necessity of addressing the impaired takeover performance due to cognitive decline in older drivers and discourage them from engaging in inappropriate NDRTs, thereby reducing their crash risk during automated driving.

Road safety: The influence of vibration frequency on driver drowsiness, reaction time, and driving performance

Driver drowsiness is a factor in at least 20% of serious motor vehicle accidents. Although research has shown that Whole-Body Vibration (WBV) can induce drowsiness in drivers, it is unknown whether particular frequencies are more problematic. The present study systematically investigated the influence of WBV frequency on driver drowsiness. Fifteen participants each undertook six 1-h sessions of simulated driving while being subjected to WBV of either 0 Hz (no vibration), 1–4 Hz, 4–8 Hz, 8–16 Hz, 16–32 Hz or 32–64 Hz. Subjective sleepiness, as measured by the Karolinska Sleepiness Scale (KSS), confirmed that drivers felt drowsier when exposed to the two lowest frequency ranges (1–4 Hz and 4–8 Hz). Reaction time, which measures attention and alertness, was significantly impaired by the two lowest frequency ranges. Objective driving performance measures (Standard Deviation of Lane Position (SDLP), Standard Deviation of (SD) Steering Angle, Time in Unsafe Zone) also showed significant degradation due to exposure to the two lowest frequency ranges. Exposure to 1–4 Hz or 4–8 Hz vibration caused attention to become significantly impaired within 15–20 min and driving performance to be significantly impaired by 30–35 min. The other frequency ranges had little or no effect. These findings point to a need to develop equivalent vibration-induced drowsiness contours that can be adopted as transportation safety standards.

Exploring the impact of the passenger's display on driver workload and driving performance

New production vehicles equipped with multiple screens have various functions and technologies at the user's fingertips that create informative and immersive cockpits not only for the driver but also for the passenger. Despite the growing popularity of the co-pilot display, surprisingly little is known about the impact of its engagement on drivers. Engagement, in the context of this paper, is defined as visual, auditory, and mental involvement but no manual involvement. The present study sought to investigate the effects of co-pilot display engagement on drivers' visual workload, cognitive workload, and driving performance and offered a deeper insight into the design factors and task involvement of the co-pilot display. The study involved a driving simulator testing of 25 participants in five automobiles with a co-pilot display. Our analysis found increased driver visual workload and cognitive workload in the conditions of co-pilot display engagement. Participants exhibited more erratic driving behavior (standard deviation of speed and standard deviation of lane position) while the front passenger was watching a video. Furthermore, five linear mixed models were built to further analyze the effects of task type, screen size, screen layout, and task involvement on dependent measures. Potential applications of this research include refinements to the co-pilot display and workload estimation of co-pilot display engagement.

Executive Functioning as a Predictor of Adverse Driving Outcomes in Teen Drivers With ADHD

Objective: The present study examined the association between executive functioning (EF) and risky driving behaviors in teens with ADHD. Method: Teens diagnosed with ADHD (n = 179; Mage = 17.4 years) completed two 15-min drives in a fixed-base driving simulator. EF was assessed using parent- and self-report Behavior Rating Inventory of Executive Functioning (BRIEF-2), a temporal reproduction task, and a Go/No-Go task (GNG). Driving outcomes included known predictors of crashes: count of long (>2 s) off-road glances, standard deviation (SD) of lane position (SDLP), mean speed, and SD speed. Generalized linear mixed models, controlling for intelligence and driving experience, were conducted. Results: Higher rates of GNG commission errors predicted higher rates of long off-road glances. Lower parent-rated EF and increased rates of GNG omission errors predicted SDLP. Higher rates of GNG commission errors also predicted faster average driving speed. Conclusion: Heterogeneity in EF is associated with differences in teen ADHD risky driving behaviors.

The effect of cognitive load on Detection-Response Task (DRT) performance during day- and night-time driving: A driving simulator study with young and older drivers

Over the past 25 years, distracted driving and driver inattention have been one of the main risk factors for road traffic safety. Previous research has shown that increased cognitive load from non-visual activities take our mind off the driving task, by engaging our working memory resources. Studies have shown that engagement in these tasks results in a more concentrated gaze towards the road centre, reducing lateral deviation (e.g., reduced standard deviation of lane position (SDLP) and less high frequency steering reversals). The Detection-Response Task (DRT) is an International Organization for Standardization (ISO) standard used to study the impact of cognitive load, due to secondary task, on performance. This driving simulator study examined the effect of varying levels of cognitive load (using the n-back task) on driving and DRT performance. The effect of age and lighting conditions was examined by comparing the performance of a younger group of drivers (Age Mdn = 22) with that of an older cohort (Age Mdn = 66) during day- and night-time driving conditions. Results showed that young drivers’ DRT performance was better at night-time compared to day-time, when compared to that of older drivers. Young drivers also performed better at the n-back task. Overall, an increase in cognitive load increased response time to the DRT task. In line with previous studies, an increase in cognitive load also reduced SDLP, but only in the absence of the DRT. These results provide new knowledge on drivers’ detection of peripheral stimuli during different lighting conditions, and how these are affected by a cognitively loading, non-visual, working memory task. The implications of these findings on real-world road safety are discussed.

Persistent effects of mobile phone conversation while driving after disconnect: Physiological evidence and driving performance

Cognitive workload has been known as a key factor in traffic accidents, which can be highly increased by talking on the phone while driving. A wide range of studies around the world investigated the effects of mobile phone conversations on driving performance and traffic accidents. But less noticed is the durability of cognitive effects of mobile phone conversations. This study aimed to determine the effects of different types of mobile phone conversations on physiological response and driving performance during and after the conversation. Heart rate, heart rate variability (physiological response), Standard deviation of lane position (SDLP), and the relative distance between two cars (driving performance) of 34 samples (male and female) in the driving simulator were recorded. In this study, three types of conversations (neutral, cognitive, and arousal) were used. Neutral conversation did not pursue specific purpose questions. Cognitive conversations were simple mathematical problem-solving questions and arousal conversations aimed at arousing participant emotions. Each conversation was used as a secondary task in a condition. The study had three conditions; in each condition the participant drove for 15 min. Each condition consisted of 5 min of driving (Background), 5 min of driving and conversation (dual tasks) and 5 min of driving after conversation to trace the effects of the conversation. Vehicle speed was 110 km/h in each of the three conditions using car-following scenario. The results showed that neutral conversations had no significant effects on physiological response. Though, arousal conversations had significant effects on physiological responsiveness and driving performance during conversations, where it was even more significant after disconnection. Therefore, the content of the conversation determines the amount of cognitive load imposed on the driver. Considering the persistence of cognitive effects caused by conversation, the risk of traffic accidents is still high even after disconnection.

Trial of Training to Reduce Driver Inattention in Teens with ADHD

BackgroundTeens with attention deficit–hyperactivity disorder (ADHD) are at increased risk for motor vehicle collisions. A computerized skills-training program to reduce long glances away from the roadway, a contributor to collision risk, may ameliorate driving risks among teens with ADHD.MethodsWe evaluated a computerized skills-training program designed to reduce long glances (lasting ≥2 seconds) away from the roadway in drivers 16 to 19 years of age with ADHD. Participants were randomly assigned in a 1:1 ratio to undergo either enhanced Focused Concentration and Attention Learning, a program that targets reduction in the number of long glances (intervention) or enhanced conventional driver’s education (control). The primary outcomes were the number of long glances away from the roadway and the standard deviation of lane position, a measure of lateral movements away from the center of the lane, during two 15-minute simulated drives at baseline and at 1 month and 6 months after training. Secondary outcomes were the rates of long glances and collisions or near-collisions involving abrupt changes in vehicle momentum (g-force event), as assessed with in-vehicle recordings over the 1-year period after training.ResultsDuring simulated driving after training, participants in the intervention group had a mean of 16.5 long glances per drive at 1 month and 15.7 long glances per drive at 6 months, as compared with 28.0 and 27.0 long glances, respectively, in the control group (incidence rate ratio at 1 month, 0.64; 95% confidence interval [CI], 0.52 to 0.76; P<0.001; incidence rate ratio at 6 months, 0.64; 95% CI, 0.52 to 0.76; P<0.001). The standard deviation of lane position (in feet) was 0.98 SD at 1 month and 0.98 SD at 6 months in the intervention group, as compared with 1.20 SD and 1.20 SD, respectively, in the control group (difference at 1 month, −0.21 SD; 95% CI, −0.29 to −0.13; difference at 6 months, −0.22 SD; 95% CI, −0.31 to −0.13; P<0.001 for interaction for both comparisons). During real-world driving over the year after training, the rate of long glances per g-force event was 18.3% in the intervention group and 23.9% in the control group (relative risk, 0.76; 95% CI, 0.61 to 0.92); the rate of collision or near-collision per g-force event was 3.4% and 5.6%, respectively (relative risk, 0.60, 95% CI, 0.41 to 0.89).ConclusionsIn teens with ADHD, a specially designed computerized simulated-driving program with feedback to reduce long glances away from the roadway reduced the frequency of long glances and lessened variation in lane position as compared with a control program. During real-world driving in the year after training, the rate of collisions and near-collisions was lower in the intervention group. (Funded by the National Institutes of Health; ClinicalTrials.gov number, NCT02848092.)

Driving distraction at night: The impact of cell phone use on driving behaviors among young drivers

Currently, young drivers are more likely than other drivers to use cell phones while driving at night, which has become a major cause of road crashes. However, limited attention has been given to distracted nighttime driving. Therefore, the aim of this study was to explore the interaction effect of cell phone use and time of day (daytime and nighttime) on young drivers’ car-following performance. Forty-three young drivers engaged in a driving simulator experiment with a within-subject design that included three distractions (no distraction, talking and texting on a cell phone) and two times of day. This paper applied non-parametrictests to analyze the data and obtained the following results: (1) the standard deviation of lane position (SDLP) did not significantly differ at either time of day under no distraction, but it was significantly higher at night on straight roads and large-radius curves after introducing distractions. In addition, participants drove faster and gave less headway on small-radius curves under both distractions at night; (2) texting significantly increased the SDLP, while there was less lateral variation during the talking tasks than under no distraction on simple road sections; and (3) compared with the experienced drivers, the novice drivers drove faster during the talking tasks on small-radius curves, but there was no significant difference between groups during the texting tasks. These findings provide both theoretical and practical implications for related policy makers to enhance traffic safety.

Evaluation of driver distraction from in-vehicle information systems: A simulator study of interaction modes and secondary tasks classes on eight production cars

Currently, various modalities for in-vehicle information systems have gained popularity. However, the secondary tasks of an in-vehicle information system (IVIS) may result in driver distraction while driving. This study aimed to evaluate the effects of classified secondary tasks performed in four modalities on driving performance and visual distraction. A driving simulator capable of connecting mass-produced cars to a virtual driving scenario was proposed. Ten secondary tasks were classified based on complexity and difficulty into three classes (basic, medium, and advanced). Thereafter, these tasks were tested using four modalities: knobs and buttons on the center stack, touch screen, steering wheel (SW) buttons, and voice control. For dependent measures, the standard deviation of speed, the standard deviation of lane position, fixation counts, and average fixation duration were collected. Consequently, significant primary effects of modality and task class were found in terms of each dependent measure. The analysis revealed that SW buttons and voice control were more suitable for basic tasks. Moreover, voice control was best for tasks in the medium class. In the advanced class, the voice control and touch screens outperformed the knobs and buttons. However, considering the efficiency and full interaction process, voice control is unsuitable for all advanced secondary tasks. Hence, cross-mode interaction was recommended for advanced class tasks with complicated input and feedback, owing to the trade-off between different interaction modes. The results of this study are significant for selecting the interaction modes and task design of IVIS for enhancing driving safety.

Perceived effects of cannabis: Generalizability of changes in driving performance

Objective The objective of this analysis was to determine the generalizability of the relationship between different samples of a driver’s perceived state after cannabis use and related performance while operating a motor vehicle. Methods Data were collected from 52 subjects in a study examining the effects of cannabis on driving performance. Data were analyzed using the SAS GLM Select procedure, using stepwise selection, with subjective effects, dosing condition (placebo vs. 6.18% delta-9-tetrahydrocannabinol [THC]), and driving context as independent measures. Correlation matrices of measures of driving performance against subjective responses and dosing condition used Pearson’s and Spearman’s test statistics, respectively. Results were compared to a prior study from a sample of 10 subjects. Results Subjective perceptions of acute cannabis impairment remain significant predictors of driving performance and explain individual variability in driving performance degradation as well as the data, beyond that which can be explained by acute use of cannabis alone. However, the significant subjective predictors of driving performance differ between the current and prior studies. To better understand these differences, correlations between subjective effects and performance measures were evaluated, which revealed that most correlations matched directionally (e.g., an increase in “good drug effect” was correlated with an increase in standard deviation of lane position [SDLP]). When there was a mismatch, 1 or more correlations were insignificant. Dosing condition and “stoned” were perfectly consistent; “high” and “sedated” contained 1 mismatch; and “anxious,” “good drug effect” and “restless” contained 3 or more mismatches. Conclusions The results indicate that across both studies, differences in the perceived effects of cannabis are reflected in changes in both lateral and longitudinal control beyond the acute effects of cannabis, which may help explain individual variability in response to acute intoxication. However, the generalizability of these findings is lacking, as shown by inconsistencies in when and where subjective effects were significant. Other factors such as frequency of use, usage type, the evolving profile of a cannabis user, as well as other individual differences should be considered to explain this additional variability.

Comparative analysis of drowsiness and performance in conditionally automated driving and manual driving considering the effect of circadian rhythm

Drowsiness in manual driving (MD) is influenced by circadian rhythms. Conditionally automated driving (CAD) affects drivers’ drowsiness. We conducted a simulator study with 30 participants (every ten subjects in morning group, afternoon group, and evening group) to investigate the effect of circadian rhythm on the changes in drivers’ drowsiness and performance in different driving modes. Each subject was required to complete CAD experiment first and MD experiment later, and experienced 8 risk scenarios in each experiment. The self-reported Karolinska Sleepiness Scale (KSS) was recorded by an investigator every time when the subject drove past the scenario as the drowsiness measurement. The speed, acceleration, time-related metrics, and vehicle lane position were collected as the performance measurements. KSS data were statistically analyzed, and the Spearman’s Rho test was used to confirm the correlation among performance measurements, KSS, and scenarios. The result of the KSS statistical analysis showed that the effect of circadian rhythm on fatigue in MD groups is consistent with the previous studies, but the existence of CAD changes the effect of the circadian rhythm. Compared with the MD, CAD slowed down the drowsiness growth rate in the morning group and promoted the drowsiness growth rate in the evening group. The brake input rate, mean longitude acceleration, max Standard Deviation of Lane Position (SDLP), and the time to pass (TTP) were significantly related to the driver´s drowsiness in both driving modes.

Effects of Speed Reduction Marking Patterns on Simulated Driving Speed and Lane Position

Speed reduction markings have been installed on highways as perceptual countermeasures for speeding. However, little is known about the effects of the shape and interval of road markings on driving speed and lane position. In this paper, a driving simulator experiment and questionnaires were performed to explore the effects of speed reduction marking patterns on driving speed and standard deviation of lane position (SDLP), as well as drivers’ subjective feelings, mental workload, and visual attention. Thirty-nine participants drove on a simulated two-lane rural highway where speed reduction markings with different shapes and intervals were presented at horizontal curves. The pavement markings were associated with reduced throttle values and mean speed in advance of a horizontal curve. The marking shape did not affect participants’ speed choice or SDLP. A cognitive alerting effect of the speed reduction markings was dominant because the participants did not drive more slowly with the markings with converging intervals toward the traveling direction compared to those with a constant interval. A questionnaire on drivers’ attention reflected a potential use of road markings for drivers’ lane-position maintenance. Since less than 18 % of the participants noticed the convergence in marking intervals, speed reduction markings may also induce the perceptual illusion of acceleration.

Address inputting while driving: a comparison of four alternative text input methods on in-vehicle navigation displays usability and driver distraction

Objective Efficient and safe address entry is crucial to in-vehicle navigation systems. Although various text input methods (TIMs) are commercially available, to date, the details of the driver’s interactions with these TIMs in the vehicle are poorly understood. Therefore, the effect of four alternative TIMs conditions on in-vehicle navigation displays usability and driver distraction were directly compared. For reference, the baseline driving task (distraction-free) was also investigated. Methods A city expressway simulator experiment including 25 young drivers was launched. Under each condition, the driving task was lane-keeping with speed ranging between 40 and 60 km/h, and the navigation task was to enter a 14-characters Chinese address name. In the meantime, usability (text entry time, number of errors, and preference) and driver distraction (NASA-TLX, average speed, the standard deviation of lane position, total glance duration, number of glances, average glance duration, and number of glances exceeding 1.6 s) metrics were measured as dependent variables. A sequence of one-way repeated measure analyses of variance (ANOVA) was performed to examine which type of TIMs can maximize in-vehicle navigation displays usability and minimize driver distraction. Results Generally, lateral driving performance deteriorated with the addition of the address inputting task, and the four alternative TIMs might fall into three levels: Speech is optimal, Qwerty followed, Shape-writing and Handwriting ranked last. Specifically, word-based speech remains performed best on all observed metrics for Chinese address names. There was an insignificant difference in text entry time and total glance duration among Qwerty, Shape-writing, and Handwriting. However, Shape-writing and Handwriting are not suitable for young drivers since the nature of uninterruptible causes excessive errors, more considerable lane position variation, longer average glance duration, and more glances exceeding 1.6 s. Conclusions This study provides valuable insights into young drivers’ interactions with four alternative TIMs. Importantly, it is beneficial to the automotive user interfaces design of in-vehicle navigation displays and other sub-functions of in-vehicle information systems (IVISs), such as music playback and text messaging, which positively mitigate driver distraction and prevent traffic injuries.

Shared steering control: How strong and how prompt should the intervention be for a better driving experience?

The lane keeping assistance system, a representative advanced driver assistance system, comprises a shared control that cooperates with the driver to achieve a common goal. The steering experience of the driver may vary significantly depending on the auto-steering control strategy of the system. In this study, we examined the driving experience with various steering control strategies. Nine control strategies (three torque amounts × three deviations in starting control) were established as prototypes. Eighteen drivers participated in the evaluation of each strategy in a highway environment on a driving simulator. A two-way repeated measure ANOVA was used to assess the effects of the system. Both the objective measures (standard deviation of lane position, steering reversal rate, and root mean square of lateral speed) and subjective measures (pleasure and arousal of emotion, trust, disturbance, and satisfaction) were evaluated and analyzed. The results showed that a torque amount of 3 Nm evoked feelings of high disturbance and negative emotional responses. A deviation in starting control (DEV) of 0.80 m yielded unstable lane keeping performances and evoked negative effects on pleasure, trust, and satisfaction. A regression model for the driver satisfaction recommended a torque of 2.32 Nm and a DEV of 0.27 m as the optimal design parameters. This proposed strategy is expected to improve the experience design of lateral semi-autonomous vehicles.

Measuring drivers’ takeover performance in varying levels of automation: Considering the influence of cognitive secondary task

Mixed control by driver and automated system will remain in use for decades until fully automated driving is perfected. Thus, drivers must be able to accurately regain control of vehicles in a timely manner when the automated system sends a takeover request (TOR) at its limitation. Therefore, determining the factors that affect drivers’ takeover quality at varying levels of automated driving is important. Previous studies have shown that visually distracting secondary tasks impair drivers’ takeover performance and increase the subjective workload. However, the influence of purely cognitive distracting secondary tasks on drivers’ takeover performance and how this influence varies at different levels of automation are still unknown. Hence, a 5 (driving modes) × 3 (cognitive secondary tasks) factorial design with the within-subject factors was adopted for this driving simulator experiment. The sample consisted of 21 participants. The participants’ subjective workloads were recorded by the NASA-Task Load Index (NASA-TLX). Results showed that compared to manual driving conditions, the drivers’ subjective workloads were significantly reduced in both partially and highly automated driving conditions, even with a TOR, confirming the benefit of the automated driving system in terms of reducing the driving workload. Moreover, the drivers exhibited a lower takeover behavior quality at high levels of automation than manual driving in terms of increased reaction time, abnormal performance, standard deviation of lane position, lane departure probability, and reduced minimum of time to collision. However, at the highly automated driving condition, the drivers’ longitudinal driving safety and ability to follow instructions improved when performing a highly cognitive secondary task. This phenomenon possibly occurred because automated driving conditions lead to an underload phenomenon, and the execution of highly cognitive tasks transfers drivers into moderate load, which helps with the drivers’ takeover performance.

The role of cognition for identifying unsafe young drivers

Young drivers (individuals between the ages of 15 and 25 years) engage in unsafe driving more than any other age group, accounting for a large portion of youth mortality rates both in Australia and worldwide. Recent research has found a link between cognitive factors such as memory, attention and executive functions and driving performance in younger drivers. The aim of the current study was to explore the unique contribution made by specific cognitive domains to unsafe youth driving, with the intention of using cognitive measures to help identify unsafe drivers. Participants comprised 95 (72% female) undergraduate students aged between 18 and 24 years. Participants completed a demographic questionnaire, a battery of cognitive tests shown to be related to driving performance in older age groups and two simulated drives. Driving performance measures included speeding (percentage of time over the limit, and average speed greater than 10 km/h over the limit), collisions and lane keeping measured via the Standard Deviation of Lane Position (SDLP). Results revealed that visuospatial ability (the visual object space and perception battery) predicted unsafe time spend speeding, whilst visuospatial executive function (Rey Complex Figure Organisation) and psychomotor skills with dominant hand (Grooved Pegboard) were found to predict average speed over the limit. The results returned on those measures, however, did not permit the clinical utility of the measures that would enable categorisation of safe and unsafe drivers above an 80% criterion of clinical significance. Recommendations for future studies are considered in light of limitations with the study such as the dichotomisation and choice of the driving performance measures, the choice of the cognitive tests and the sample of participants. These results provide a starting point for further investigation into the feasibility of cognitive testing for younger drivers.

Alcohol use patterns and their association with sober driver vehicle control in high fidelity driving simulation

Objective To examine the relationship between patterns of alcohol use, as determined by the Alcohol Use Disorders Identification Test, and vehicle control measures in high fidelity driving simulation among adult sober drivers. Methods Baseline data (BAC = 0.00%; N = 108) from a larger study aimed at using high-fidelity driving simulation (National Advanced Driving Simulator) to evaluate the feasibility of vehicle-based sensors to identify alcohol impairment were analyzed. Driving simulation scenarios included driving on urban, interstate, and rural roadways. The independent variable was the pattern of alcohol use measured with the Alcohol Use Disorder Identification Test (AUDIT). Dependent variables included one lateral vehicle control measure (i.e., standard deviation of lane position (SDLP)) and one longitudinal vehicle control measure (i.e., average speed relative to the speed limit) in high fidelity driving simulation. Multivariable linear regression was used to examine the associations between patterns of alcohol use and vehicle control measures. Results Total AUDIT scores ≥8 was positively associated with SDLP. Increased frequency of drinking was associated with decreased SDLP and increased average speed relative to the speed limit. Increased reports of blackouts and alcohol-related injury were associated with increased average speed relative to the speed limit. Driver performance (SDLP, average speed relative to the speed limit) was related to additional factors such as driver experience, age, marital status, and driving context. Conclusions The findings support our hypothesis that the AUDIT score and responses to individual AUDIT questions, among sober drivers, relates to vehicle control measures. Overall, our data highlight two important themes: 1) a need to further integrate alcohol use metrics with high-fidelity driving simulation studies to understand how drinking experience can relate to driver behavior and vehicle control and 2) the opportunity to integrate clinical perspectives with driving simulation research to strengthen clinically oriented alcohol-misuse prevention efforts.

Impact of cannabis and low alcohol concentration on divided attention tasks during driving

Objective To assess divided-attention performance when driving under the influence of cannabis with and without alcohol. Three divided-attention tasks were performed following administration of placebo, cannabis, and/or alcohol. Methods Healthy adult cannabis users participated in 6 sessions, receiving combinations of cannabis (placebo/low-THC/high-THC) and alcohol (placebo/active) in randomized order, separated by washout periods of ≥1 week. At 0.5 hours post-dosing, participants performed simulator drives in the University of Iowa National Advanced Driving Simulator (NADS-1), a full vehicle cab simulator with a 360° horizontal field of view and motion base that provides realistic feedback. Drives contained repeated instances of three tasks: a side-mirror task (reaction to a triangle appearing in the side-mirrors), an artist-search task (select a specified artist from a navigable menu on the vehicle’s console), and a message-reading task (read aloud a message displayed on the console). Blood THC and breath alcohol concentration (BrAC) were interpolated using individual power curves from samples collected approximately 0.17, 0.42, 1.4, and 2.3 hours post-dose. Driving measures during tasks were compared to equal-duration control periods occurring just prior to the task. Performance shifts, task completion, and lane departures were modeled relative to blood THC and BrAC using mixed-effects regression models. Results Each 1 µg/L increase in blood THC concentration predicted increased odds of failing to complete the artist-search task (OR: 1.05, 95% CI: 1.01-1.11, p = 0.046), increased odds of selecting at least one incorrect response (OR: 1.05, 95% CI: 1.00-1.09, p = 0.041), declines in speed during the side-mirror task (0.005 m/s, 95% CI: 0.001-0.009, p = 0.023), and longer lane departure durations during the artist-search task (0.74% of task-period, 95% CI: 0.12-1.36 p = 0.020). BrAC (approximately 0.05%) was not associated with task performance, though each 0.01 g/210 L increase predicted longer departure durations during the side-mirror task (1.41% of task-period, 95% CI: 0.08-2.76, p = 0.040) and increased standard deviation of lane position in the message-reading task (0.61 cm, 95% CI: 0.14-1.08, p = 0.011). Conclusions With increasing medical and legal cannabis use, understanding the impact of acute cannabis use on driving performance, including divided-attention, is essential. These data indicate that impaired divided-attention performance is a safety concern.