Effect of verbal messages with reminders to communicate driving situations to alter driver behavior in conditional driving automation

In conditionally automated driving, drivers are relieved of steering (hands-off), accelerating, and braking actions as well as of continuous monitoring of driving situations and the system operation status (eyes off). This enables continuously engagement in non-driving-related activities. Managing the allocation of a driver’s attention to the surrounding environment and automation status presents a major challenge in human–machine system design. In this study, we propose a verbal message with a reminder (monitoring request) to divert the driver’s attention from non-driving-related activities to peripheral monitoring under conditionally automated driving. When the system encounters events related to weather, traffic, and road geometry, it provides a verbal message pertaining to the road surroundings (e.g., “It is foggy outside”) to the driver. After three seconds, the system provides a reminder message (i.e., “Did you confirm it?”) to the driver. We explore two questions: (1) how does the message with the reminder affect the driver’s attention allocation, and (2) how does the message with the reminder affect the driver behavior in response to a request to intervene (RTI). With a driving simulator, we designed a repeated measures mixed design with a between-participant factor of “Driving condition” and within-participant factors of “Event type” and “Measurement time”. Three driving conditions were established as follows: no messages, messages without reminders, and messages with reminders. Twenty-seven drivers participated as participants in the driving simulator experiment. Results showed that the reminder message was effective in allocating the participants’ attention to the surrounding environment, and the participants took over the driving task after spending more time understanding the take-over situation in the condition of messages with reminders compared to those in the condition of no messages. We conclude that the proposed reminder message can direct drivers’ attention to the road surroundings during conditionally automated driving. In the future, we plan to design adaptive verbal monitoring requests to adjust the reminder message according to the situation.     

A sleep physiologist's view of the drowsy driver

Drowsy driving is dangerous because of the impairment of driving skills that it causes. Unfortunately, the conceptual basis that underlies much of the multi-disciplinary research on this topic is muddled. The same poorly defined terms, such as fatigue and sleepiness, are used differently by different disciplines and researchers. Some new definitions and concepts are proposed here which may be helpful, as least as a stimulus for discussion by others. Drowsiness, sleepiness and fatigue are distinguished. A new conceptual model of sleepiness is outlined, based on a mutually inhibitory interaction between a putative sleep drive and a wake drive. Sleepiness, defined as sleep propensity, is a function of the relative strengths, not the absolute strengths, of the sleep and wake drives. The measurement of sleepiness requires some new variables such as instantaneous sleep propensity, to be distinguished from either the situational or the average sleep propensity. A subject's instantaneous sleep propensity depends on many variables including his average sleep propensity in daily life, the time of day, the duration of prior wakefulness, the subject's posture, physical and mental activity at the time, and individual differences based on psychophysiological traits. The relationship between dozing at the wheel while driving and crashing the vehicle may not be as straightforward as it appears at first.     

Prediction of effort and eye movement measures from driving scene components

For transitions of control in automated vehicles, driver monitoring systems (DMS) may need to discern task difficulty and driver preparedness. Such DMS require models that relate driving scene components, driver effort, and eye measurements. Across two sessions, 15 participants enacted receiving control within 60 randomly ordered dashcam videos (3-second duration) with variations in visible scene components: road curve angle, road surface area, road users, symbols, infrastructure, and vegetation/trees while their eyes were measured for pupil diameter, fixation duration, and saccade amplitude. The subjective measure of effort and the objective measure of saccade amplitude evidenced the highest correlations (r = 0.34 and r = 0.42, respectively) with the scene component of road curve angle. In person-specific regression analyses combining all visual scene components as predictors, average predictive correlations ranged between 0.49 and 0.58 for subjective effort and between 0.36 and 0.49 for saccade amplitude, depending on cross-validation techniques of generalization and repetition. In conclusion, the present regression equations establish quantifiable relations between visible driving scene components with both subjective effort and objective eye movement measures. In future DMS, such knowledge can help inform road-facing and driver-facing cameras to jointly establish the readiness of would-be drivers ahead of receiving control.     

A driving simulator study to assess driver performance during a car-following maneuver after switching from automated control to manual control

A review of the literature on autonomous vehicles has shown that they offer several benefits, such as reducing traffic congestion and emissions, and improving transport accessibility. Until the highest level of automation is achieved, humans will remain an important integral of the driving cycle, which necessitates to fully understand their role in automated driving. A difficult research topic involves an understanding of whether a period of automated driving is likely to reduce driver fatigue rather than increase the risk of distraction, particularly when drivers are involved in a secondary task while driving. The main aim of this research comprises assessing the effects of an automation period on drivers, in terms of driving performance and safety implications. A specific focus is set on the car-following maneuver. A driving simulator experiment has been designed for this purpose. In particular, each participant was requested to submit to a virtual scenario twice, with level-three driving automation: one drive consisting of Full Manual Control Mode (FM); the other comprising an Automated Control Mode (AM) activated in the midst of the scenario. During the automation mode, the drivers were asked to watch a movie on a tablet inside the vehicle. When the drivers had to take control of the vehicle, two car-following maneuvers were planned, by simulating a slow-moving vehicle in the right lane in the meanwhile a platoon of vehicles in the overtaking lane discouraged the passing maneuver. Various driving performances (speeds, accelerations, etc.) and surrogate safety measures (PET and TTC) were collected and analysed, focusing on car-following maneuvers. The overall results indicated a more dangerous behavior of drivers who were previously subjected to driving automation; the percentage of drivers who did not apply the brakes and headed into the overtaking lane despite the presence of a platoon of fast-moving vehicles with unsafe gaps between them was higher in AM drive than in FM drive. Conversely, for drivers who preferred to brake, it was noted that those who had already experienced automated driving, adopted a more careful behavior during the braking maneuver to avoid a collision. Finally, with regard to drivers who had decided to overtake the braking vehicle, it should be noted that drivers who had already experienced automated driving did not change their behavior whilst overtaking the stopped lead vehicle.     

The influence of road familiarity on distracted driving activities and driving operation using naturalistic driving study data

This paper analyzed the influence of familiarity on the involvement of secondary tasks and driving operation using naturalistic driving study (NDS) data. Distracted driving activities were extracted from face videos captured in 557 trips, including 501 trips on familiar roads and 56 trips on unfamiliar roads. These trips were completed by 155 drivers using their own vehicles during daytime hours under good weather conditions. The data showed the frequency of distracted driving activities and duration time were higher on familiar roads compared to unfamiliar roads. More types of secondary tasks were found on familiar roads. Focusing on objects was the most common distracted driving activity on familiar roads. The average time drivers used to eat or drink was highest (8.67 s) on familiar roads. The time drivers spent checking their cell phone was high on both familiar roads and unfamiliar roads. Since driving operation is directly related to crash risk, this paper also analyzed the difference of driving operation on familiar roads and unfamiliar roads. The speed profiles were generated on well-known versus unfamiliar roads. It was shown that drivers were more likely to be speeding and select a short distance to deceleration near the intersections. The findings indicated that distracted driving phenomenon was more serious on familiar roads.     

Investigating driving instructors: The mediating roles of driving skills in the relationship between organizational safety strategies and driver behaviours

The aim of the present study is to investigate the mediating roles of driving skills in relationship between organizational safety strategies and driver behaviours among driving instructors. Driving skills consist of perceptual-motor skills and safety skills. Driver behaviours are investigated under four factors: violations, errors, lapses, and positive driver behaviours. Participants were 132 driving instructors (108 male and 24 female). In order to measure organizational safety strategies, Organizational Safety Strategies Scale (OSSS) was developed for driving schools. Results of the principal component analyses yielded one-factor solution for OSSS. In order to test the indirect effects of organizational safety strategies on driver behaviours through driving skills, multiple mediation analyses were conducted by entering age and annual mileage as the control variables. As organizational safety strategies were stronger, driving instructors had higher levels of perceptual-motor skills, which resulted in higher violations. On the other hand, as organizational safety strategies were stronger, driving instructors had higher levels of safety skills, which resulted in less violations and lapses. It can be inferred that; organizational stronger safety strategies might have negative influences on road safety through higher perceptual-motor skills; whereas there can be positive influences on road safety through higher safety skills. In addition, both skills are related to organizational safety strategies. Hence, driving schools should consider the asymmetric relationship between perceptual-motor skills and safety skills while improving their safety strategies to decrease violations and lapses. Organizations might also develop interventions to balance the stated skills to increase road safety.     

Adaptations in driver deceleration behaviour with automatic incident detection: A naturalistic driving study

Traffic congestion and crash rates can be reduced by introducing variable speed limits (VSLs) and automatic incident detection (AID) systems. Previous findings based on loop detector measurements have revealed that drivers reduce their speeds while approaching traffic congestion when the AID system is active. Notwithstanding these behavioural effects, most microscopic traffic flow models assessing the impact of VSLs do not describe driver response accurately.This study analyses the main factors that influence driver deceleration behaviour while approaching traffic congestion with and without VSLs. The Dutch VSL database was linked to the driver behaviour data collected in the UDRIVE naturalistic driving study. Driver engagement in secondary tasks and glance behaviour were extracted from the video data. Linear mixed-effects models predicting the characteristics of deceleration events were estimated.The results show that the maximum deceleration is high when approaching a slower leader, when driving at high speeds and short distance headways, and close to the beginning of traffic congestion. The minimum time headway is short when driving at high speeds and changing lanes. Certain drivers showed higher decelerations and shorter time headways than others. Controlled for these main factors, smaller maximum decelerations were found when the VSLs were present and visible, and when the gantries were within close proximity. These factors could be incorporated into microscopic traffic simulations to evaluate the impact of AID systems on traffic congestion more realistically. Further research is needed to clarify the link between engagement in secondary tasks, glance behaviour and deceleration behaviour.     

Three consecutive nights of sleep loss: Effects of morning caffeine consumption on subjective sleepiness/alertness,reaction time and simulated driving performance

Caffeinated products are often consumed as a popular countermeasure to the effects of sleep loss. However, the efficacy of caffeine to exert these effects after consecutive nights of sleep loss is poorly understood. The aim of this study was to investigate the effects of three consecutive nights of restricted sleep and morning caffeine consumption on subjective ratings of sleepiness/alertness, reaction time, and simulated driving performance. Twenty healthy, habitual caffeine consumers (11 females; age: 23.3 ± 5.7 y; BMI: 22.3 ± 3.5 kg⋅m⁻²; caffeine intake: 204 ± 89 mg⋅day⁻¹; Mean ± SD) who had normal sleeping patterns (≥8 h⋅night⁻¹) participated in this double-blind, placebo-controlled, randomised study. Following one night of normal sleep (≥8 h time in bed (TIB)), participants underwent three consecutive nights of restricted sleep (5 h TIB). Participants received caffeine (200 mg; n = 10) or placebo (n = 10) capsules each morning and all participants received caffeine (100 mg) capsules each afternoon. Subjective ratings of alertness, concentration and tiredness were measured before and 1 h after morning capsule administration. Choice Reaction Time (CRT) was examined 1 h after morning capsule administration, with response speed and accuracy as outcome variables. Driving performance was assessed using a 30 min simulated driving task, with lateral (standard deviation of lane position [SDLP]; total number of line crossings [LC]) and longitudinal (standard deviation of speed [SDSP]) measures of vehicle control as outcome variables. Alertness and concentration significantly decreased, and tiredness increased across the three days of sleep loss. Caffeine only marginally alleviated these effects. No differences were observed between treatments or across trial days for response speed and accuracy on the CRT task. Likewise, no significant differences were observed between groups or across trial days for any measures of simulated driving performance. Overall, results from this study indicate that three consecutive days of sleep loss influence subjective ratings of alertness, concentration and tiredness, but does not alter CRT or simulated driving performance. Caffeine may alleviate some of the negative subjective effects imposed by restricted sleep, but the efficacy of caffeine to attenuate performance changes in CRT and driving performance were unable to be observed.     

Towards a new approach to detect sleepiness: Validation of the objective sleepiness scale under simulated driving conditions

The Objective Sleepiness Scale (OSS) was developed to detect and quantify sleepiness on the basis of two direct and reliable sleepiness indicators: EEG and EOG. The present study aims to test whether the OSS can be used to detect sleepiness episodes that impair performance on driving and vigilance tasks accurately and with a good time synchronization. Forty-three healthy volunteers performed monotonous driving sessions on a simulator and the psychomotor vigilance task (PVT) in a normal sleep condition and after partial sleep deprivation. OSS reliability and time synchronization for sleepiness detection were tested on driving (standard deviation of vehicle lateral position and off-road duration) and PVT (reaction time and lapses). Inter-rater reliability of the scale was evaluated by two blinded scorers. Results show that the OSS score indicates higher sleepiness in sleep deprivation conditions (p < 0.001) and with time-on-task. Differences of performance between OSS score calculated with multiple pairwise comparisons, indicate OSS score increase when driving performance (SDLP and off-road duration) decreases (p < 0.001 for comparisons between OSS stages 0 vs 2, 0 vs 3, 1 vs 2 and p < 0.05 for 1 vs 3). Reaction time during PVT is also related to the OSS score (p < 0.05 for OSS values from 0 to 2, 0 to 3, 1 to 2 and 1 to 3). There is no proportional relation between OSS score and performance impairment, but a threshold effect between levels 1 and 2 of the scale is observed. Positive outcomes are also obtained for time synchronization of the OSS assessed on driving performance (p < 0.001 for both SDLP and off-road duration). Finally, inter-rater agreement is found to be considerable. The results allow us to consider using the Objective Sleepiness Scale as a tool for research on sleepiness.     

Eye movements predict driver reaction time to takeover request in automated driving: A real-vehicle study

For automated driving at SAE level 3 or lower, driver performance in responding to takeover requests (TORs) is decisive in providing system safety. A driver state monitoring system that can predict a driver’s performance in a TOR event will facilitate a safer control transition from vehicle to driver. This experimental study investigated whether driver eye-movement measured before a TOR can predict driving performance in a subsequent TOR event. We recruited participants (N = 36) to obtain realistic results in a real-vehicle study. In the experiment, drivers rode in an automated vehicle on a test track for about 32 min, and a critical TOR event occurred at the end of the drive. Eye movements were measured by a camera-based driver monitoring system, and five measures were extracted from the last 2-min epoch prior to the TOR event. The correlations between each eye-movement measure and driver reaction time were examined, and a multiple regression model was built using a stepwise procedure. The results showed that longer reaction time could be significantly predicted by a smaller number of large saccades, a greater number of medium saccades, and lower saccadic velocity. The implications of these relationships are consistent with previous studies. The present real-vehicle study can provide insights to the automotive industry in the search for a safer and more flexible interface between the automated vehicle and the driver.     

Proposal of a driver profile classification in relation to risk level in overtaking maneuvers

Traffic crashes are a worldwide problem, and records have indicated frontal collisions have resulted in the most significant number of fatalities. Such a type of crash is frequently caused by improper overtaking of vehicles, which highlights the interference of human factors. Therefore, investigations on driver's risk perception are necessary. This study proposes a classification of driver's risk level through a decision tree using the Classification and Regression Tree (CART) algorithm from data collected from the overtaking maneuvers in a driving simulator. The model obtained by CART algorithm indicated young male drivers are more likely to take risks in overtaking maneuvers. The results were correlated with governmental records and similar studies. In addition, the results showed the potential of the tool for used as a risk level classifier, as well as the validation of the driving simulator in studies associated with human factor behaviours, accident analysis and investigation.     

Exploration of the effects of task-related fatigue on eye-motion features and its value in improving driver fatigue-related technology

Mental fatigue has been lacked attention in developing eye-tracking fatigue detection system for drivers. However, it has great influence on eye movement which could account for the poor validity of current fatigue detectors only focusing on sleep-related fatigue. This work sought to investigate the influence of two types of task-related mental fatigue on eye movement by examining 8 saccade-based, 3 blink-based, and 1 pupil-based metrics. We propose that two types of task-related fatigue caused by cognitive overload and prolonged underload will induce different physiological responses to eye-motion features. Twenty participants completed a vigilance task before and after a 1-h driving with a secondary task in a virtual simulation environment, while forty participants, divided equally into two groups, finished the same task before and after a 1-h and 1.5-h monotonous driving. T-test was applied to analyse the eye-motion, subjective and vigilance data during vigilance task. We found that overload driving made drivers vigilance ability decrease. The eye metrics showed different changes in underload and overload scenario. The blink duration, the mean velocity of saccade and saccade duration increased after 1-h overload driving, while the pupil diameter decreased. However, none of those changes were observed in 1.5-h underload driving, but saccade duration had a significant increase. The fatigue response to heavy demands over short periods of driving is different from the lighter demands over long periods in terms of eye-motion metrics. Considering mental fatigue in designing an eye-tracking fatigue detection system could possibly improve its accuracy.     

Effect of chevron design on driver behaviour when encountering and passing through a dangerous curve

Horizontal curves are locations on the road network with a high road accident risk. In order to provide drivers with timely and proper information about the upcoming curve, road authorities often use chevron signs. Although the main design of chevrons is similar in most countries (one colour for the background and another for the arrow), the combination of colours differs. The aim of this simulator study is to investigate how different colour combinations affect drivers when they encounter and drive through horizontal curves on rural roads at daytime. Overall, each of the tested chevrons reduced the driving speed (between 25 and 29 km/h), although not to the speed limit level (60 km/h). However, for curves marked with chevrons with fluorescent or white background the driving speed was the lowest at all measuring points, regardless of the curve direction. The observation of lateral movement shows that there are no significant differences in the way the vehicle is positioned when approaching and driving through curves marked with different chevrons. Based on the obtained results, practical recommendations and potential future research activities are presented.     

Psychometric properties of driver self-image inventory for Chinese drivers and its associations with personality and driving style

This study aimed to adapt the Driver Self-image Inventory (DSII, Taubman-Ben-Ari, 2008) to Chinese drivers and examine its relationship with personality traits and driving style. Six hundred forty drivers aged 18–55 years agreed to participate in this study. Measurements included the DSII, a personality scale and a validated Chinese version of the Multidimensional Driving Style Inventory (MDSI). The results of exploratory factor analysis (n = 302) and confirmatory factor analysis (n = 305) yielded a three-factor scale with satisfactory reliability. Significant gender differences were found on the DSII factors, with male drivers scoring higher on the impulsive driver factor and lower on the cautious driver factor than female drivers. The validity of the DSII was supported by significant associations between the DSII factors and personality traits, driving style and number of traffic violations and accidents in the previous 12 months. Moreover, drivers with traffic accidents scored significantly lower on the cautious driver factor and higher on the impulsive driver factor than those without traffic accidents. These findings indicate that the reliability and validity of the Chinese version of the DSII are acceptable.     

Using topic modeling to develop multi-level descriptions of naturalistic driving data from drivers with and without sleep apnea

One challenge in using naturalistic driving data is producing a holistic analysis of these highly variable datasets. Typical analyses focus on isolated events, such as large g-force accelerations indicating a possible near-crash. Examining isolated events is ill-suited for identifying patterns in continuous activities such as maintaining vehicle control. We present an alternative approach that converts driving data into a text representation and uses topic modeling to identify patterns across the dataset. This approach enables the discovery of non-linear patterns, reduces the dimensionality of the data, and captures subtle variations in driver behavior. In this study topic models were used to concisely described patterns in trips from drivers with and without untreated obstructive sleep apnea (OSA). The analysis included 5000 trips (50 trips from 100 drivers; 66 drivers with OSA; 34 comparison drivers). Trips were treated as documents, and speed and acceleration data from the trips were converted to “driving words.” The identified patterns, called topics, were determined based on regularities in the co-occurrence of the driving words within the trips. This representation was used in random forest models to predict the driver condition (i.e., OSA or comparison) for each trip. Models with 10, 15 and 20 topics had better accuracy in predicting the driver condition, with a maximum AUC of 0.73 for a model with 20 topics. Trips from drivers with OSA were more likely to be defined by topics for smaller lateral accelerations at low speeds. The results demonstrate topic modeling as a useful tool for extracting meaningful information from naturalistic driving datasets.     

The effect of fatigue driving on car following behavior

Existing fatigued driving analysis methods mainly focus on lateral driving performance by using the measurements related to the steering wheel or lane position. There is a lack of research on longitudinal car following behavior. In this study, 40 professional drivers are invited to participate in field expressway driving experiment, lasting at least for 6 h. During the test, their performance is measured in terms of their self-reported fatigued driving level according to the Karolinska Sleepiness Scale (KSS), the PERcentage of eye CLOSures (PERCLOS) and the Time Headway (THW). Then the effects of the fatigued driving level on car following behavior are evaluated. The results indicate that the fatigue level (for both KSS and PERCLOS) has significantly impact on THW parameters, including the mean, standard deviation and minimum THW. An increase in KSS and PERCLOS leads to a lower mean and minimum THW. Meanwhile, the standard deviation of THW increases with the increase of KSS and PERCLOS. In conclusion, this study found that a higher fatigue level leads to the driver keeping a smaller THW when following another vehicle and choosing shorter THW to make lane change. More deviation of car following performance was also found with the increase of fatigue level. Therefore, the findings of this study can be used to explain fatigue as one of the major reasons for rear-end collisions. Also, the research findings demonstrate the impact of fatigue on driving behavior in terms of car following performance, which can be used as a measurement for monitoring fatigued drivers.     

Accident involvement among Iranian lorry drivers: Direct and indirect effects of background variables and aberrant driving behaviour

Road accident rates among Iranian lorry drivers are considerably high and, according to empirical evidence, aberrant driving behaviours, summed to certain demographic, psycho-social and work-related factors, may explain their accident involvement. Consequently, the main aim of the study was to examine the direct and indirect effects of background variables (i.e. annual mileage, lorry driving experience, demographic and socioeconomic factors) on accident involvement mediated through aberrant driving behaviour among Iranian lorry drivers. A cross-sectional questionnaire survey was conducted in 2012 among 914 lorry drivers in 10 selected provinces in Iran. The 27-item Driver Behaviour Questionnaire (DBQ) was used to measure aberrant driving behaviour. Results from valid observations (n = 785) confirmed a four-factor solution (including ordinary violations, aggressive violations, errors, and lapses) of the DBQ. Errors, ordinary violations and aggressive violations were positively associated with accident involvement. However, lapses were not significantly associated with accident involvement. The results of structural equation modeling (SEM) further showed that, in addition to direct effects of background variables on accident involvement, several variables had indirect effects mediated by three-DBQ factors; ordinary violations, aggressive violations, and errors. Higher age, having more lorry driving experience, having higher educational attainment, and married drivers were indirectly related to less accident involvement. Annual driving mileage and the resting rate of drivers was both directly and indirectly related to accident involvement. Higher income and car ownership were directly related to fewer accidents. Interventions could aim to decrease ordinary violations, aggressive violations and errors among younger, less educated and single lorry drivers. Initiatives targeted to increase the scheduled resting frequency of lorry drivers may also hold promise.     

Driving anger in Germany: Validation of the driving anger scale for German drivers

In 1994, Deffenbacher et al. published the Driving Anger Scale (DAS), a tool for assessing a driver’s propensity to experience anger in road traffic. Since then, much research has used this scale to measure the driving anger experienced in various countries around the world. This study examines the scale’s validity for German drivers. It also relates their experiences of anger while driving to their experiences and expressions of anger in general, as well as to certain demographic variables. In addition, it compares German drivers’ experiences of driving anger to those reported by drivers from other countries. We distributed a German version of the DAS and the State-Trait Anger Expression Inventory (STAXI) to a sample of 1136 German drivers. Results showed that a 22-items version of the DAS with six factors produced good fit indices for German drivers. Furthermore, data analysis revealed small to moderate significant relationships between German drivers’ driving anger experiences and their experiences and expressions of anger in general, underlining the idea that driving anger is a personality characteristic that is related to one's general experience and expression of anger. Finally, German drivers' driving anger experiences differed from those of drivers from other countries in that German drivers reported less driving anger than drivers from Spain and New Zealand, comparable levels to those from Turkey, Malaysia, and the United States, and more driving anger than drivers from France, Australia, China, and the United Kingdom. In addition, discourteous driver actions and hostile gestures consistently triggered highest driving anger ratings whereas police presence was rated lowest. Given these results, we conclude that the DAS can be applied to German drivers in its modified version.     

Driver glance behavior towards displayed images on in-vehicle information systems under real driving conditions

Intelligent vehicle technologies like driver assistance systems and in-vehicle information systems, enhance convenience of the driving experience for drivers and passengers. At the same time, these systems may increase driver distraction and workload. Guidelines developed for this purpose include principles, methods, and assessments which are widely agreed upon, with some being singled out for a particular recommendation or requirement. Especially the display of graphical or photographic images are generally assumed to distract the driver from safely operating the vehicle and should be blocked during driving under all circumstances (so called per se lock outs). This study investigates the effect of displaying graphical and photographical images during driving on driveŕs glance behavior during real-world driving. Findings presented in this paper provide empirical evidence for the unobtrusiveness of these stimuli: Participants didn’t exhibit longer glance durations towards in-vehicle information systems, nor a deterioration of driver distraction parameters such as total eyes off road time and long glance proportion when being compared to driving without displaying any photographic images.