Tactile detection response task: Metrics for assessing drivers’ cognitive workload

The Tactile Detection Response Task (TDRT) has been used to assess the cognitive workload of driver distraction with response time and miss rate as metrics of cognitive workload. However, it is not clear which metric is more sensitive and whether sensitivity is maintained for visual tasks. The objective of this study was to assess the sensitivity of the TDRT to changes in cognitive workload and to examine whether the sensitivity depends on task modality. A driving simulator study was conducted with 24 participants. The study included restaurant selection tasks with three presentation modalities (auditory, visual, and hybrid) and two difficulty levels (low and high). The high difficulty level was designed to be more cognitively demanding than the low difficulty level. Mixed-effects models were applied to examine the TDRT metrics and task difficulty level. The model controlled for age group, gender, and included a random effect for participants. The high difficulty level of the auditory tasks significantly increased the likelihood of missing a TDRT stimulus. No statistically significant differences were observed for visual and hybrid tasks. TDRT response time was not significantly associated with the difficulty level, regardless of task modality. In this study, the binary outcome TDRT miss was thus considered a more sensitive metric of cognitive workload than TDRT response time. TDRT response time can still be used to measure cognitive workload when tasks are relatively easy and the TDRT miss rate is close to zero. In addition, the sensitivity of the TDRT miss diminished for tasks that involved a visual component. Researchers who use TDRT to measure the cognitive workload associated with visual tasks should be aware of this limitation.     

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 simulation sickness study on a driving simulator equipped with a vibration platform

Simulator sickness is a well-known side effect of driving simulation which may reduce the passenger well-being and performance due to its various symptoms, from pallor to vomiting. Numerous reducing countermeasures have been previously tested; however, they often have undesirable side effects. The present study investigated the possible effect of seat vibrations on simulator sickness. Three configurations were tested: no vibrations, realistic ones and some that might affect the proprioception. Twenty-nine participants were exposed to the three configurations on a four-minute long automated driving in a simulator equipped with a vibration platform. Simulator sickness was estimated thanks to the Simulator Sickness Questionnaire (SSQ) and to a postural instability measure. Results showed that vibrations help to reduce the sickness. Our findings demonstrate that some specific vibration configurations may have a positive impact on the sickness, thus confirming the usefulness of devices reproducing the road vibrations in addition to creating more immersion for the driver.     

Assessing the driving distraction effect of vehicle HMI displays using data mining techniques

With the rapid development of human–machine interface (HMI) systems in vehicles, driving distraction caused by HMI displays affects road safety. This study presents a data mining technique to model the four driving distraction indicators: speed deviation, lane departure standard deviation, dwell time, and mean glance time. Driving distraction data was collected on a real-car driving simulator. 3 secondary tasks in 13 mass produced cars were tested by 24 drivers. The random forest algorithm outperformed linear regression, extreme gradient boosting, and multi-layer perceptron as the best model, demonstrating good regression performance as well as good interpretability. The result of random forest showed that the importance of target speed is large for all driving distraction indicators. Among the variables of interaction and user interface design, less step and less on-screen distance of finger movement are efficient for lowering lane departure standard deviation and dwell time. The position of right point is another important variable, and should be between 37 and 47 degrees on a typical sample in this study. A larger angle leads to bigger lane departure, while a smaller angle leads to bigger mean glance time. Most variables of HMI display positioning themselves are not important. This study provides one driving distraction assessment method with a variable impact trend analysis for HMI secondary tasks in an early phase of product development.     

A driving simulation study to investigate the information threshold of graphical variable message signs based on visual perception characteristics of drivers

Graphical variable message signs (VMSs) are infrastructure-based advanced traveler information systems widely used to provide drivers with real-time traffic condition information about a road section or area. However, there is a lack of research on the suitable volume of information to be shown on graphical VMSs. In practice, an overload of VMS information commonly exists, especially on China’s highways. Building on our earlier findings obtained through surveys and static cognitive experiments, this study introduces the use of dynamic simulation experiments to assess the influence of the volume of information (i.e., number of roads displayed) on graphical VMSs from the perspective of drivers’ visual perception characteristics. Thirty-two drivers participated in the driving simulation experiment and questionnaires. Five indexes, including legibility speed, legibility distance, legibility time, comprehension accuracy, and driver subjective scoring, were thoroughly analyzed to evaluate their relationships to different volumes of information (i.e., four, five, and six roads shown on a VMS). The results show that the legibility distance notably decreased with increasing volumes of information. The comprehension accuracy decreased significantly when the number of roads shown increased to six. The legibility speed, legibility time, and subjective scoring also deteriorated as the number of roads displayed on the VMS increased. The index scores were evaluated, in combination with the data of the drivers’ subjective scoring, data-based statistical analyses, and comprehensive evaluations using the TOPSIS method, to recommend that five is the recommended maximum number of roads to be shown on a graphical VMS. The results of this study support the goal of providing understandable and effective messages for drivers by addressing issues relating to how much information should be displayed on a VMS. These findings provide a basis for policy development to ensure consistent and practical designs of graphical VMSs on highways.     

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.     

Flow experience influenced by car adjustments

Today’s cars offer a variety of possible setting options which have to be chosen by the driver. In order to examine the psychological effect of automatic adjustments in comparison to manual adjustments of the chassis and drive train while driving, a test vehicle was built up and tested in a naturalistic field operation test (nFOT) through various development stages. 207 BMW employees participated in the study focusing on emotional feedback and flow experience. The results were used to improve the function and to start the test scenario again.The automatic adjustment of the car’s setting (driving mode) due to the detection of the intended driving style of the subjects was preferred to the manual adjustments. Additionally, the feeling of safety increased while the level of distraction decreased. Our findings show that in addition to the positive technical aspects of an automatic adjustment, there is also an increase in driving experience, measured by the flow experience and the feeling of safety.     

Cognitive biases in aggressive drivers: Does illusion of control drive us off the road?

Anger has been shown to be a motivating factor in aggression and it is widely accepted that driving anger may lead to aggressive driving. However, the link between anger and aggressive driving is likely to be mediated by drivers’ pre-existing cognitive biases and the subsequent situational evaluations made. This study investigated the extent to which optimism bias, illusion of control beliefs and driver anger predict self-reported hostile driving behaviours. A total of 220 licensed drivers (106 men; 114 women) completed a self-report questionnaire measuring trait driving anger, optimism bias, illusion of control and driving behaviour. Structural Equation Modelling showed that trait driving anger and illusion of control beliefs account for 37% of the variance in hostile driving behaviour scores. Optimism biases were unrelated to hostile driving behaviours. Thus, driving anger propensities and feelings of control over the situation, but not a general tendency to underestimate the likelihood of adverse outcomes, predict aggressive driving.     

Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence

Adaptive cruise control (ACC), a driver assistance system that controls longitudinal motion, has been introduced in consumer cars in 1995. A next milestone is highly automated driving (HAD), a system that automates both longitudinal and lateral motion. We investigated the effects of ACC and HAD on drivers’ workload and situation awareness through a meta-analysis and narrative review of simulator and on-road studies. Based on a total of 32 studies, the unweighted mean self-reported workload was 43.5% for manual driving, 38.6% for ACC driving, and 22.7% for HAD (0% = minimum, 100 = maximum on the NASA Task Load Index or Rating Scale Mental Effort). Based on 12 studies, the number of tasks completed on an in-vehicle display relative to manual driving (100%) was 112% for ACC and 261% for HAD. Drivers of a highly automated car, and to a lesser extent ACC drivers, are likely to pick up tasks that are unrelated to driving. Both ACC and HAD can result in improved situation awareness compared to manual driving if drivers are motivated or instructed to detect objects in the environment. However, if drivers are engaged in non-driving tasks, situation awareness deteriorates for ACC and HAD compared to manual driving. The results of this review are consistent with the hypothesis that, from a Human Factors perspective, HAD is markedly different from ACC driving, because the driver of a highly automated car has the possibility, for better or worse, to divert attention to secondary tasks, whereas an ACC driver still has to attend to the roadway.