Consistency of neuropsychological and driving simulator assessment after neurological impairment

Can executive functions of the brain predict official driving test success?

Human factors, including the level of cognitive functioning, are the most influential factors in road traffic crashes. Among cognitive abilities, executive functions (EFs) of the brain play a pivotal role in driving performance and outcomes, including crash numbers. The current study was aimed to explore, for the first time, the ability of EF tests to predict success on the official driving tests in applicants of driving license in Iran. We administered a relevant set of commonly used EF tests, including a computerized Stroop test and six tests from the Delis-Kaplan Executive Function System (D-KEFS) and the Behavioral Assessment of the Dysexecutive Syndrome (BADS), to 87 healthy new drivers applying for a driver's license (Mage = 25.9 years, SD = 8.2; 43 female). We also administered a series of demographic and psychological questionnaires. The data regarding the participants' official driving tests were extracted from the official records. To determine the relations of the EF tests with success on the driving tests, several correlation and regression analyses were conducted. Most of the EF measures had significant correlations with the road test success, while having no significant relations with the theory test success. Moreover, in our regression analyses, The EF measures predicted success on the official driving road test, but not success on the driving theory test, even after controlling for the effects of previous unlicensed driving experience and stress symptoms. The results demonstrate the predictability of the driving road test success from executive functioning.

Effectiveness of a time to fixate for fitness to drive evaluation in neurological patients

We present a method to automatically calculate time to fixate (TTF) from the eye-tracker data in subjects with neurological impairment using a driving simulator. TTF presents the time interval for a person to notice the stimulus from its first occurrence. Precisely, we measured the time since the children started to cross the street until the drivers directed their look to the children. From 108 neurological patients recruited for the study, the analysis of TTF was performed in 56 patients to assess fit-, unfit-, and conditionally-fit-to-drive patients. The results showed that the proposed method based on the YOLO (you only look once) object detector is efficient for computing TTFs from the eye-tracker data. We obtained discriminative results for fit-to-drive patients by application of Tukey's honest significant difference post hoc test (p < 0.01), while no difference was observed between conditionally-fit and unfit-to-drive groups (p = 0.542). Moreover, we show that time-to-collision (TTC), initial gaze distance (IGD) from pedestrians, and speed at the hazard onset did not influence the result, while the only significant interaction is among fitness, IGD, and TTC on TTF. Obtained TTFs are also compared with the perception response times (PRT) calculated independently from eye-tracker data and YOLO. Although we reached statistically significant results that speak in favor of possible method application for assessment of fitness to drive, we provide detailed directions for future driving simulation-based evaluation and propose processing workflow to secure reliable TTF calculation and its possible application in for example psychology and neuroscience.