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.     

Transition patterns of driving style from a traditional driving environment to a connected vehicle environment: A case of an extra-long tunnel road

To provide a better understanding of individual driver’s driving style classification in a traditional and a CV environment, spatiotemporal characteristics of vehicle trajectories on a road tunnel were extracted through a driving simulator-based experiment. Speed, acceleration, and rate of acceleration changes are selected as clustering indexes. The dynamic time warping and k-means clustering were adopted to classify participants into different risk level groups. To assess the driver behavior benefits in a CV environment, an indicator BI (behavior indicator, BI) was defined based on the standard deviation of speed, the standard deviation of acceleration, and the standard deviation of the rate of acceleration change. Then, the index BI of each driver was calculated. Furthermore, this paper explored driving style classification, not in terms of traditional driving environment, but rather the transition patterns from a traditional driving environment to a CV environment. The results revealed that inside a long tunnel, 80 % of drivers benefited from a CV environment. Moreover, drivers might need training before using a CV system, especially female drivers who have low driving mileage. In addition, the results showed that the driving style of 69 % of the drivers’ transferred from a high risk-level to a low risk-level when driving in a CV environment. The study results can be expected to improve driving training education programs and also to provide a valuable reference for developing individual in-vehicle human-machine interface projects and other proactive safety countermeasures.     

Effects of environmental,vehicle and human factors on comfort in partially automated driving: A scenario-based study

Although it is key to improving acceptability, there is sparse scientific literature on the experience of humans as passengers in partially automated cars. The present study therefore investigated the influence of road type, weather conditions, traffic congestion level, vehicle speed, and human factors (e.g., trust in automated cars) on passenger comfort in an automated car classified as Level 3 according to the Society of Automotive Engineers (SAE). Participants were exposed to scenarios in which a character is driven by an SAE Level 3 automated car in different combinations of conditions (e.g., highway × heavy rain × very congested traffic × vehicle following prescribed speed). They were asked to rate their perceived comfort as if they were the protagonist. Results showed that comfort was negatively affected by driving in downtown (vs. highway), heavy rain, and congested traffic. Interaction analyses showed that reducing the speed of the vehicle improved comfort in these two last conditions, considered either individually or in combination. Cluster analysis revealed four profiles: trusting in automation, averse to speed reduction, risk averse, and mistrusting automation. These profiles were all influenced differently by the driving conditions, and corresponded to varying levels of trust in automated cars. This study suggests that optimizing comfort in automated cars should take account of both driving conditions and human profiles.     

An online hazard perception training course reduces heavy braking,speeding, and over-revving rates during everyday driving

In essence, driver training involves learning the skills required to drive safely and avoid dangerous events. However, in traditional on-road driver instruction, drivers virtually never accrue experience of the most significant types of events that they are learning to avoid: crashes. One means of providing this experience safely is to present novice drivers with video clips of real crashes, as part of structured learning exercises. A six-week automated online hazard perception training course for drivers, incorporating evidence-based training methods and over a hundred crash clips, was previously found to improve novice drivers’ hazard perception skill, which is known to be an important attribute for avoiding crashes. However, since hazard perception was measured using computer-based methods, the possibility remained that the training effect might not transfer to actual driving. We report a randomized control trial in which novice drivers were recruited to assess everyday driving behaviour objectively, using g-force triggered dashcams and GPS trackers installed in their vehicles. On-road data were collected for a one-month baseline period, and for a further two months after half of the sample completed the hazard perception training course. Drivers who completed the course significantly reduced their rate of heavy-braking events, their speeding behaviour, and their rate of over-revving events. These findings support the proposal that a relatively inexpensive and highly scalable hazard perception training intervention can improve on-road driving behaviour, with the clear potential to impact real-world driver safety.     

Effect of multimodal takeover request issued through A-pillar LED light,earcon, speech message,and haptic seat in conditionally automated driving

The driver of a conditionally automated vehicle equivalent to level 3 of the SAE is obligated to accept a takeover request (TOR) issued by the vehicle. Considerable research has been conducted on the TOR, especially in terms of the effectiveness of multimodal methods. Therefore, in this study, the effectiveness of various multimodalities was compared and analyzed. Thirty-six volunteers were recruited to compare the effects of the multimodalities, and vehicle and physiological data were obtained using a driving simulator. Eight combinations of TOR warnings, including those implemented through LED lights on the A-pillar, earcon, speech message, or vibrations in the back support and seat pan, were analyzed to clarify the corresponding effects. When the LED lights were implemented on the A-pillar, the driver reaction was faster (p = 0.022) and steering deviation was larger (p = 0.024) than those in the case in which no LED lights were implemented. The speech message resulted in a larger steering deviation than that in the case of the earcon (p = 0.044). When vibrations were provided through the haptic seat, the reaction time (p < 0.001) was faster, and the steering deviation (p = 0.001) was larger in the presence of vibrations in the haptic seat than no vibration. An interaction effect was noted between the visual and auditory modalities; notably, the earcon resulted in a small steering deviation and skin conductance response amplitude (SCR amplitude) when implemented with LED lights on the A-pillar, whereas the speech message led to a small steering deviation and SCR amplitude without the LED lights. In the design of a multimodal warning to be used to issue a TOR, the effects of each individual modality and corresponding interaction effects must be considered. These effects must be evaluated through application to various takeover situations.     

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.     

Driving self-regulation and risky driving outcomes

Self-regulation has been associated with risky driving outcomes in the past but there are no available measures to assess driving-specific self-regulatory capacity. The present study assessed the association of a newly developed driving self-regulation measure with driving violations, errors, and lapses. Overall, 330 UK drivers completed measures of risky driving outcomes, driving anger, trait impulsivity, sensation seeking, normlessness, domain-general trait self-regulation plus a new unidimensional measure of Driving Self-Regulation Questionnaire (DSRQ-16). Bivariate correlation analysis indicated that the DSRQ-16 showed expected associations with both driving-related outcomes and factors, as well as with impulsivity traits and general self-regulation. Bootstrapped hierarchical linear regression models showed that the DSRQ-16 was significantly associated with driving violations, errors, and lapses after controlling for the effects of other relevant predictors. This is the first study to demonstrate the association of driving-specific self-regulation with risky driving behaviour, driving anger, impulsivity and related personality traits. Driving-specific self-regulation may present a novel target for road safety interventions, as well as a theoretically relevant component of models of risky driving behaviour.     

Driving around bends with or without shoulders: The influence of bend direction

Paved shoulders have long been used to create “forgiving” roads where drivers can maintain control of their vehicles even when as they drift out of the lane. While the safety benefits of shoulders have been well documented, their effects on driver behavior around curves have scarcely been examined. The purpose of this paper is to fill this gap by assessing whether the addition of shoulders affects driver behavior differently as a function of bend direction. Driver behavior in a driving simulator was analyzed on left and right curves of two-lane rural roads in the presence and absence of 0.75-m and 1.25-m shoulders. The results demonstrated significant changes in drivers’ lateral control when shoulders were provided. In the absence of oncoming traffic, the shoulders caused participants to deviate more toward the inner lane edge at curve entry, at the apex and at the innermost position on right bends but not left ones. In the presence of oncoming traffic, this also occurred at the apex and the innermost position, leading participants to spend more time off the lane on right curves. Participants did not slow down in either traffic condition to compensate for steering farther inside, thereby increasing the risk of lane departure on right curves equipped with shoulders. These findings highlight the direction-specific influence of shoulders on a driver’s steering control when driving around bends. They provide arguments supporting the idea that drivers view paved shoulders as a new field of safe travel on right curves. Recommendations are made to encourage drivers to keep their vehicle within the lane on right bends and to prevent potential interference with cyclists when a shoulder is present.     

Driving at night with a cataract: Risk homeostasis?

Driving with a cataract can be dangerous, especially at night when road lighting and automotive lighting produce glare. Disability glare alters visual performance, while discomfort glare contributes to restricted mobility, with drivers avoiding driving at night. The present study was focused on the visual effects of an early cataract, and aimed at comparing three driving performance indexes at night, under glare conditions, with and without a simulated cataract. Two indexes directly referred to road safety, while a measure could be related to behavioral adaptation.Using a driving simulator, twenty-six participants were asked to drive in simulated night-time conditions, under controlled photometric conditions where the adaptation luminance and the glare level were consistent with a two-lane rural road at night with oncoming traffic. The visual effects of a bilateral cataract were simulated using goggles which were in the range of an early cataract in terms of light scattering, light transmission, visual acuity and contrast sensitivity loss. Participants were asked to avoid virtual pedestrians on the road, both with and without a simulated cataract. Three performance indexes were considered: the rate of pedestrian crashes, the distance to the pedestrian when the participant avoided the crash, and the mean speed, which allowed to control for a possible behavioral adaptation to the reduced visual performance. For a better understanding of the visual functions responsible for the degraded driving behavior, contrast sensitivity and time-to-collision performance were also measured in glare conditions.While simulated cataract resulted in slightly slower speeds, poorer driving performance was observed with the goggles than without, with more pedestrians being hit and shorter stopping distances. Time-to-collision estimates at 90 km/h were found to be predictive of stopping distances with a simulated cataract, while contrast sensitivity in glare conditions at 13 cycles per degree was found to be associated with the occurrence of a crash with cataract.The decrease in speed with a simulated cataract was real but ineffective in terms of driving safety, which suggests that the behavioral adaptation to the degraded visual performance was insufficient. The precise impact of a cataract on driving abilities remains to be further studied, to provide scientific knowledge to help practitioners determine the moment when the individuals should forego driving.     

Cognitive function and young drivers: The relationship between driving,attitudes, personality and cognition

Young drivers (aged 17–25 years) are the highest risk age group for driving crashes and are over-represented in car crash statistics in Australia. A relationship between cognitive functioning and driving in older drivers (60 years and older) has been consistently supported in previous literature, however, this relationship has been neglected in research regarding younger drivers. The role of cognitive functioning in young people’s driving was investigated both independently and within a current model of younger peoples driving performance. With young drivers as participants, driving behaviour, attitudes, personality and cognitive functioning were tested and driving performance was operationalised through two measures on a driving simulator, speeding and lane deviations. Cognitive functioning was found to contribute to driving behaviour, along with driving attitudes and personality traits, in accounting for young people’s driving performance. The young drivers who performed better on cognitive functioning tasks engaged in less speeding behaviour and less lane deviation on the driving simulator than those who performed worse on these tasks. This result was found independent of the role of driving behaviour, driving attitudes and personality traits, accounting for unique variance in driving ability.