Other road users’ adaptations to increase safety in response to older drivers’ behaviour

Changes in physical and cognitive abilities not only challenge the driving ability of older adults, in some situations age-related changes in driving behaviour require other road users to adapt their behaviour to maintain a safe traffic situation. In this study, we aimed to map age-related differences in driving behaviour and assess the impact on other road users. A group younger and a group older adults drove four different routes containing challenging situations (e.g., merging into motorway traffic) in a driving simulator while measures of driving behaviour were collected. Other road users’ deceleration responses to the driver’s behaviour were also collected as a measure of behavioural adaptation. Our results showed similar driving performance between young and older drivers when task complexity was low, but reduced performance in older drivers when tasks requirements increased. Lower driving speed and longer waiting times that were observed in older drivers can be interpreted as compensatory behaviour aimed at creating more time to lower task requirements. Crucially, in a non-time critical situation this compensatory behaviour was found to be successful, however in a time-critical situation (merging onto a motorway) this strategy had negative side effects because other road users had to decelerate in order to keep a safe distance. Our results show the importance of anticipation and adaptation by other road users for the success of older driver’s strategies and traffic safety.     

Should drivers be informed about the equipment of drivers with green light optimal speed advisory (GLOSA)?

Previous research demonstrated that green light optimal speed advisory (GLOSA) affects driving behavior at signalized intersections: On the one hand, drivers assisted with GLOSA show more energy-efficient and eco-friendly driving. Following unequipped vehicles’ drivers (UVDs) also adapt their driving behavior to the assisted one. On the other hand, safety issues can be found in encounters with UVDs who also perceive assisted driving behavior negatively. Therefore, in a multi-driver simulator study (N = 60 participants sorted in groups of n = 2 UVDs), we tested whether informing UVDs about the GLOSA of an assisted driver results in more behavioral adaptation of UVDs to the assisted driving behavior, less safety issues, and less frustration of UVDs. Two UVDs followed a lead vehicle driven by a confederate. The confederate was equipped with GLOSA and knew when traffic lights switched from green to red and, consequently, slowed down when approaching a green traffic light. The degree of information UVDs received was manipulated: The group “no information” did not receive any information. The group “information” knew about the equipment of the assisted confederate with GLOSA and the group “detailed information” received additional information about its functionality and benefit. Results show that UVDs of the group “detailed information” adapted their driving behavior to the assisted driver. However, these UVDs also showed smaller minimum time-to-collision (TTC) values indicating safety issues. Results are discussed and implications made with regard to providing information to UVDs and to further investigate these challenges in the context of autonomous vehicles.     

Intraindividual variability in driving simulator parameters of healthy drivers of different ages

Intraindividual variability is a fundamental behavioural characteristic of aging but has been examined to a very limited extent in driving. This study investigated intraindividual variability in driving simulator measures in healthy drivers of different ages using the coefficient of variation (COV) as a variability measure. Participants were healthy volunteers who were regular drivers, who were divided into a “young” group, a “middle-aged” group, and an “old” group. They drove in two environments (rural, 72 drivers; urban, 60 drivers), under conditions of moderate and high traffic load, without and with distraction (conversation). Significant differences in COV were observed in the rural condition for headway distance and lateral position as a function of traffic load, with high traffic (without and with distraction) resulting in increased COV of headway and decreased COV of lateral position. Significant differences in COV were observed in the urban condition for headway distance only, with high traffic (without and with distraction) resulting in increased COV of headway. No age effects were found for any of the driving conditions. The results indicate that traffic load affected headway distance and lateral position in opposite directions in all three age groups: high traffic resulted in increased variability of headway in both rural and urban conditions but in decreased variability of lateral position in the rural conditions compared to moderate traffic irrespective of distraction. The study indicates that driving conditions affect the intraindividual variability of driving measures in selective ways, which may be linked to the extent of automatization of the driving variables and to adaptive changes to traffic condition challenges.     

Exploring behavioral validity of driving simulator under time pressure driving conditions of professional drivers

Driving simulators have become an important tool in human factors research, given that they are appropriately validated. Therefore, this study aims to explore the behavioral (absolute and relative) validity of a fixed-base driving simulator by analyzing different driving behavior measures such as speed, longitudinal acceleration, lateral acceleration, and brake pedal force. Thirty professional drivers participated in the experiment and the data was collected in real and simulated worlds under No Time Pressure (NTP) and Time Pressure (TP) driving conditions. Initially, comparative analyses were conducted on different driving behavior measures using Wilcoxon-signed rank test to examine absolute validity of the driving simulator. Finally, Generalized Linear Mixed (GLM) models were developed for computing the effective distance between real and simulated worlds by quantifying the parameters and for establishing relative validity. In general, the continuous profiles of driving behavior measures followed similar trends in real and simulated worlds and comparative analyses indicated relative validity of the driving simulator. The GLM models showed significant interaction effect of driving environments (real-world and simulated world) and driving conditions (NTP and TP) where high driving speed, high brake pedal force, and low lateral acceleration were observed in simulated world under TP driving condition than real-world under TP driving condition. Overall, the statistical analyses showed qualitative correspondence (relative validity) of the driving behavior measures in between real and simulated worlds. The findings from the current study showed expediency of the driving simulator and its effectiveness in conducting research on human factors and driver safety.     

Identifying factors for traffic safety support in older drivers

The present study aimed at identifying factors for traffic safety support in older drivers. This was achieved by studying the co-operation between older drivers and their ordinary front seat passenger (co-driver). The knowledge emerging from such study can enhance the understanding of what kind of support an older driver needs and also help facilitate proper design of in-vehicle support systems and intervention training programs for older drivers. A within group field study was carried out, using a mixed-methods evaluation design. Four elderly couples participated in the study. The drivers included in the study requested and received on-going directional support, traffic -and driving strategic help, help with look-out and reminding of current speed limit. It was evident from comparisons between interview data and field data that how the participants themselves described their need of support differed from what support they requested (drivers) and gave (co-drivers) in real driving. Furthermore, data revealed that three out of four drivers were given a score of 2 in the Useful Field Of View test (UFOV). It was evident that there are areas in which older drivers need and request support. The results from the present study could be used in the design process, and in evaluation of, in-vehicle support systems as well as in developing intervention training programs customised for older drivers.     

Does surrounding traffic benefit from an assisted driver with traffic light assistance system?

Traffic light assistance systems enable drivers more energy and time efficient driving behavior at signalized intersections. However, most vehicles will not be equipped with such systems in the next years. These unequipped vehicles’ drivers (UVDs) may benefit from assisted drivers, if they would adapt their behavior. This paper outlines how UVDs (N = 60) interpreted and reacted to a driver with traffic light assistance system. We used a multi-driver simulator with three drivers driving in a car-following scenario. The lead driver was not a participant, but a confederate who was followed by two UVDs. The confederate was apparently equipped either with or without a traffic light assistance system. The traffic light assistance system consisted of two functionalities: a Green Light Optimal Speed Advisory and a start-up assistance system with two different parametrizations. These functionalities aimed at preventing unnecessary changes in speed and reducing the start-up lost time after signal change. The results showed that UVDs benefited from the driving behavior of the confederate with traffic light assistance system. However, the assisted driving behavior was hardly understood and partly rated as aversive by the UVDs. We discuss how to enhance behavioral adaptation of UVDs. We also outline which negative consequences may result from encounters of driver with systems and UVDs. We assume that how UVDs react towards drivers with systems may be one factor contributing to a successful launch of such systems.     

Driver behaviour and traffic accident involvement among professional urban bus drivers in China

The present research aimed to investigate specific behaviors of professional urban bus drivers in China with the revised Driver Behavior Questionnaire (DBQ), and to define the relationships among various driving behaviors (errors, positives, inattention errors, violations), background information (age, years of driving experience, mobility, etc.), self-assessment, and traffic accident. To achieve such goals, the present research designed a four-dimensional DBQ with 20 items for professional urban bus drivers in China. The KMO coefficient of the whole scale was 0.835, and Bartlett’s test was statistically significant (p < 0.000), which demonstrated strong validity of the scale and should be suitable for factor analysis. The four loading factors accounted for 58.991%. In addition, the reliability and effectiveness of the present 20-item scales were measured. The coefficient of internal consistency-Cronbach’s alpha coefficient was 0.881 and the Cronbach’s Alpha Based on Standardized Items was 0.911. This showed that driving behavior scale of professional bus drivers in China was of high reliability and validity. The analysis showed that among the four factors, positive driving behaviors were significantly associated with errors, inattention errors and violations, respectively. Errors, inattention errors and violations correlated positively with each other. This verified that the correlation coefficient of each factor was medium and high, which indicated that the scale had good difference validity. The test content of the total scale was also highly consistent with the test content of each factor, which indicated that the revised scale had good standard related validity. According to the accident prediction model, the variables that significantly affected the occurrence of traffic accidents were daily driving time, positive driving behavior, SE2 (Driving safety), SE3 (Aberrant driver behaviors). The results showed that professional bus drivers often working overtime were most likely to have accidents. The probability of traffic accidents decreased by 53% for every unit of positive driving behavior frequency of professional bus drivers. The more they felt that they had the tendency of aberrant driving behavior, the more likely they were to have traffic accidents. To summary, the present research contributed to validating and improving the DBQ for professional urban drivers in China.     

Identification of driving simulator sessions of depressed drivers: A comparison between aggregated and time-series classification

Depression has been found to significantly increase the probability of risky driving and involvement in traffic collisions. The majority of studies correlating depressive symptoms with driving, pursue to predict the differences in driving behavior if the driver has already been diagnosed. Little evidence can be found, however, on how mental and psychological disorders can be identified from driving data, and usually analyses utilize simple models and aggregated data. This study aims at utilizing microscopic data from a driving simulator to detect sessions belonging to “depressed” drivers by utilizing powerful machine learning classifiers. Driving simulator sessions from 11 older drivers with symptoms of depression and 65 healthy drivers were utilized towards that aim. Random Forests, an ensemble classifier, with proven efficiency among transportation applications, are then trained on highly disaggregated data describing the mean and standard deviation of speed and lateral or longitudinal acceleration of drivers in the simulator. The kinematic data were aggregated in 30-seconds, 1-minute and 5-minute intervals, but the corresponding time-series of the measurements were also taken into account. Furthermore, classifiers were treated with imbalanced learning techniques to address the scarcity of depressed drivers among the healthy. Time-series of mean speed and the standard deviation of longitudinal acceleration even with a duration of 30-seconds have proven to be the best predictors of driving sessions belonging to depressed drivers with a very low rate of false alarms. The results outperform previous approaches, and indicate that naturalistic driving data or deep learning could prove even more efficient in detecting depression.     

It’s about time! Earlier take-over requests in automated driving enable safer responses to conflicts

Automated driving (AD), which takes full responsibility for the driving task in certain conditions, is currently being developed. An important concern in AD is how to design a take-over request (TOR) that mitigates automation effects (e.g., delayed responses to conflict scenarios) that previous literature from simulator experiments has shown can occur. To address this concern, this study aims to investigate and compare driver responses to TORs and a lead-vehicle cut-out scenario under three conditions: (1) after a period of AD with a TOR issued early (18 s time-to-collision), (2) same as (1) except with a TOR issued late (9 s time-to-collision), and (3) baseline, with adaptive cruise control (ACC). This paper also compares the results to those of a previous study using the same conflict scenario but with near-perfect assisted driving system (SAE Level 2). The lead-vehicle cut-out scenario was encountered on a test track after 30 minutes driving with either ACC or AD. In AD the TOR was issued prior to the conflict object was revealed to the participants when the lead vehicle performed the cut-out (at conflict onset). This TOR strategy differed from previous driving-simulator studies that issued the TOR at conflict onset. The participants had to respond by steering and/or braking to avoid a crash. Our findings show that, independent of TOR timing, the drivers required similar amounts of time to 1) direct their first glance to the human–machine interface, 2) look forward, 3) end their secondary task, 4) put their hands on the steering wheel, and 5) deactivate automation. However, when the TOR was issued early rather than late, they started to brake earlier (even before conflict onset). All participants successfully managed to avoid crashing with the object, independent of the condition. AD with an early TOR resulted in the earliest response, while ACC drivers responded slightly earlier than the drivers in AD with the late TOR. Our findings do not support the findings of severe automation effects in previous driving-simulator studies. One reason for the difference is that when a TOR is issued prior to conflict onset, drivers are given the time needed for their preparatory actions (e.g., placing hands on the wheel, deactivating AD) that is not needed when driving with ACC or in manual driving (baseline), before having to respond to the conflict scenario. Thus, at conflict onset the drivers in AD are as ready to act (hands on wheel, eyes forward) as the drivers in the baseline and can perform an avoidance manoeuvre similar as to the baseline drive. Overall, the present study shows that AD does not need to end up in a highly critical situation if the TOR is issued early enough. In fact, AD with an early TOR may be safer than driving with ACC, because in the former drivers are more likely to brake earlier in preparation for the conflict. Finally, a TOR clearly communicates the need for drivers to resume manual control and handle potential events when AD has been deactivated. In our study, once the drivers had taken control, they clearly understood their responsibilities to respond to the conflict, in contrast to a previous study with a similar, near-perfect assisted driving system.     

Self-regulatory driving behaviours amongst older drivers according to cognitive status

Self-regulation of older drivers was explored according to their cognitive status in this pilot study by examining situations commonly avoided by older drivers. In addition, the role of driver insight on self-regulation was examined via passenger reports. Telephone interviews were conducted comprising 49 drivers aged 65 years and above and 40 passengers who acted as informants. Self-regulation was found to be common, with the majority of drivers (71.4%) reporting sometimes or always avoiding one of seven driving situations. However, drivers with cognitive impairment reported self-regulating more often than drivers without cognitive impairment. The largest discrepancy between passenger and driver reports of self-regulation behaviours was found for the drivers with cognitive impairment. These results possibly reflect a decreased awareness of self-regulatory driving behaviours in this subgroup of older drivers and may suggest that other external factors are contributing to self-regulation in older drivers with cognitive impairment. A discussion of these factors is provided with the aim of maintaining mobility and enhancing quality of life in this growing segment of the driver population.     

Using a multi-user driving simulator system to explore the patterns of vehicle fleet rear-end collisions occurrence under different foggy conditions and speed limits

Vehicle fleet rear-end collisions (FRECs) are an extremely fatal type of traffic collisions on freeway and they usually occur in foggy weather. This study aimed to explore the patterns of vehicle fleet rear-end collisions occurrence under different foggy conditions and speed limits on freeway. A multi-user driving simulator system was used to conduct the experiment and the driving behavior data were collected from eight participants. The experimental results showed that as the fog density increased, the length of vehicle fleet decreased significantly, and drivers tended to keep a more stable car-following distance. The fog weather and short vehicle gap prompted drivers to react faster and brake harder in respond to the leading vehicle’s brake. In spite of the compensational behaviors, more FRECs were observed under heavy fog condition. Lowering speed limit can significantly reduce the FRECs under foggy conditions. As the speed limits reduced, drivers’ brake response time and speed variance significantly reduced. The study also found that drivers’ brake response time was negatively correlated with their positions in the fleet. Drivers in the front positions of the fleet had a longer response time than drivers in the back positions and thus were more likely to encounter collisions. The study generated a better understanding of drivers’ behavioral pattern in a vehicle fleet and the patterns of vehicle fleet rear-end collisions occurrence. The findings also shed lights on the design of driver assistance system for complex driving situations such as freeway driving under adverse weather.     

Temporal fluctuations in driving demand: The effect of traffic complexity on subjective measures of workload and driving performance

Traffic density has been shown to be a factor of traffic complexity which influences driver workload. However, little research has systematically varied and examined how traffic density affects workload in dynamic traffic conditions. In this driving simulator study, the effects of two dynamically changing traffic complexity factors (Traffic Flow and Lane Change Presence) on workload were examined. These fluctuations in driving demand were then captured using a continuous subjective rating method and driving performance measures. The results indicate a linear upward trend in driver workload with increasing traffic flow, up to moderate traffic flow levels. The analysis also showed that driver workload increased when a lane change occurred in the drivers’ forward field of view, with further increases in workload when that lane change occurred in close proximity. Both of these main effects were captured via subjective assessment and with driving performance parameters such as speed variation, mean time headway and variation in lateral position. Understanding how these traffic behaviours dynamically influence driver workload is beneficial in estimating and managing driver workload. The present study suggests possible ways of defining the level of workload associated with surrounding traffic complexity, which could help contribute to the design of an adaptive workload estimator.     

Approaching intersections: Gaze behavior of drivers depending on traffic,intersection type,driving maneuver,and secondary task involvement

Urban intersections are hotspots for crashes because they provide a location for several traffic streams and types of road users to cross. A main cause of crashes is the misinformation of drivers as they fail to sense relevant visual information. We aimed to analyze the gaze behavior of car drivers in a variety of intersection scenarios, bringing together the partial findings of previous research, and examine the interdependencies of the contributing factors to provide a database for driver modeling. In a driving simulator study with 59 participants, we varied intersection scenarios regarding drivers’ right of way (yield sign, green traffic light), intersection type (T junction, X intersection), surrounding traffic (none, irrelevant, relevant), and intended driving maneuver (left turn, right turn, going straight). A total of 25 intersection scenarios were presented in a within-subjects design to a control group and a group with a cognitive load task (counting back in numbers of two). Fixations were coded regarding defined areas of interest in the field of view and separated according to three segments of the intersection approach: 75–50 m, 50–25 m, and 25–0 m before entering the intersection. The results show that the effect of surrounding traffic, secondary task engagement, and the intended driving maneuver changed dramatically depending on the right of way of the driver. Surrounding traffic primarily affected gaze behavior in scenarios of ceding the right of way close to the intersection entry. The cognitive load task increased fixations on the road center especially in situations where the driver had the right of way, but less in situations of ceding the right of way. Interactions with the type of intersection were only apparent for different driving maneuvers. This study provides a detailed and comprehensive picture of drivers’ attentional processes when approaching intersections which is relevant for understanding and modeling of driver behavior in urban traffic.     

Investigating the impact of a novel active gap metering signalization strategy on driver behavior at highway merging sections

A safe headway to the lead vehicle is important to reduce conflicts with merging vehicles from highway on-ramps. Previous research has outlined the advantage of gap metering strategies to yield sufficient space to merging vehicles and improve highway capacity during peak hours. However, prevailing gap metering systems fail to indicate the minimum required gap and leave it to the drivers’ judgment to adjust their headway. This paper proposes a new Active Gap Metering (AGM) signalization that helps outer lane drivers to adjust their headway to the lead vehicle when approaching highway ramps with incoming vehicles. This AGM signalization represents a combination of pavement markings and an innovative Variable Message Sign (VMS). The AGM system was tested alone and in combination with additional variable speed limits (VSL) in distinct environments of the Doha Expressway in the State of Qatar using a driving simulator. The driving behavior of 64 drivers was analyzed using repeated-measures ANOVA. The results showed that the AGM effectively influenced the drivers’ behavior on the right stream lane. Drivers did gradually increase the distance to the lead vehicle, which resulted in optimal headways to merging on-ramp vehicles. Most importantly, the minimum time-to-collision (TTC_min) to the merging vehicle was increased by an additional 1–1.5 s as compared to no treatment. The proposed AGM signalization can, therefore, be considered by policymakers to influence drivers’ headways at critical merging sections.     

Assessing the effect of long-automated driving operation,repeated take-over requests,and driver’s characteristics on commercial motor vehicle drivers’ driving behavior and reaction time in highly automated vehicles

Automated Commercial Motor Vehicles (CMVs) have the potential to reduce the occurrence of crashes, enhance traffic flow, and reduce the stress of driving to a larger extent. Since fully automated driving (SAE Level 5) is not yet available, automated driving systems cannot perform all driving tasks under all road conditions. Drivers need to regain the vehicle’s control when the system reaches its maximum operational capabilities. This transition from automated to manual is referred to as Take-Over Request (TOR). Evaluating driver’s performance after TORs and assessing effective parameters have gained much attention in recent years. However, few studies have addressed CMV drivers’ driving behavior after TOR and the effect of long-automated driving and repeated TORs. This paper aims to address this gap and gain behavioral insights into CMV drivers’ driving behavior after TOR and assess the effect of the duration of automated operation before TOR, repeated TORs, and driver characteristics (e.g., age, gender, education, and driving history). To accomplish this, we designed a 40-minutes experiment on a driving simulator and assessed the responses of certified CMV drivers to TORs. Drivers’ reaction time and driving behavior indices (e.g., acceleration, velocity, and headway) are compared to continuous manual driving to measure driving behavior differences. Results showed that CMV drivers’ driving behavior changes significantly after the transition to manual regardless of the number of TORs and the duration of automated driving. Findings suggest that 30 min of automated operation intensifies the effect of TOR on driving behaviors. In addition, repeated TOR improves reaction times to TOR and reduces drivers' maximum and minimum speed after TORs. Driver’s age and driving history showed significant effects on reaction time and some driving behavior indices. The findings of this paper provide valuable information to automotive companies and transportation planners on the nature of driver behavior changes due to the carryover effects of manual driving right after automated driving episodes in highly automated vehicles.     

Effects of partial sleep deprivation: A comparative assessment of young non-professional and professional taxi drivers

AimThis study aims to determine the effects of partial sleep deprivation (PSD) on driving performance of professional and young non-professional drivers.MethodsThe study included fifty participants (20 professional taxi drivers and 30 young non-professional drivers) driving the simulator-cab in three conditions. The first test session (TS1) was conducted after one night of PSD followed by the second test (TS2) after two consecutive nights of PSD. The driving performance metrics in two conditions of PSD (i.e., sleep duration = 4.25 ± 0.5 h) were compared with the baseline drive with no sleep loss. Sleep restriction was monitored using Actiwatch. Drivers subjectively self-reported their alertness using Karolinska Sleepiness Scale (KSS). Driving performance metrics and reaction time to emergency events were collected during each drive.ResultsA preliminary mixed-design ANOVA showed deterioration in driving performance of all drivers in terms of speed (p < 0.1), speed variability (p = 0.06), standard deviation in lateral positions (SDLP) (p < 0.001) and delayed reaction time (p < 0.05). Separate Mixed-Effects Generalized Linear Models for professional and non-professional drivers showed that speed variability, SDLP and reaction time increased from baseline during both the PSD tests, among both the driving groups. The speed variability, SDLP and reaction time of professional drivers differed significantly from other drivers under PSD conditions. Contrary to the existing belief, the professional drivers had significant decrements in driving performance due to PSD.ConclusionA critical and comparative analysis revealed that driving experience/skill of professional drivers does not improve their resistance to deteriorating effects of sleep loss.     

The development and validation of a hazard perception test for Thai drivers

Thailand is a developing country with a high traffic accident fatality rate. However, few attempts have been made to understand crash risks in Thai drivers from a psychological perspective. The purpose of the present study was to develop and validate a latency-based hazard perception test for Thai drivers. The initial test comprised our full item pool of 77 clips containing traffic conflicts captured on video from the driver’s perspective on Thai roads. We evaluated the validity of this test by examining whether performance differed as a function of driving experience in a sample of 135 Thai drivers. We found that experienced drivers (n = 87) performed significantly better than novice drivers (n = 48) after adjusting for individual differences in computer mouse skill, mirroring crash risk differences between these groups. The final 30-item version of the test, which comprised the best items from the initial test, yielded novice/experienced driver differences with or without adjusting for computer mouse skill. These results offer preliminary support for the validity of the latency-based test as a measure of hazard perception ability in Thai drivers.     

Imperfect in-vehicle collision avoidance warning systems can aid distracted drivers

The purpose of this study was to evaluate the efficacy of a type of in-vehicle collision avoidance warning system (IVCAWS) under conditions of driver distraction. Forty-three participants responded to an imperfect warning system while simultaneously driving a simulator and performing a visual/cognitive task. The major concerns were whether drivers would be more inclined to rely on such a system when they are distracted by subsidiary tasks, and if this reliance would be counterproductive. We found that distracted drivers responded, by increasing their temporal headway, to the less reliable system’s alarms, but the warning system at the higher reliability levels led to over reliance and ultimately to maintaining shorter headways. This study has practical implications for the use of warning systems as driving aids for drivers. Although aids may be helpful and, in many cases, the more reliable aid is preferable, in the case of distraction, drivers may misuse the aid.