Behind the Wheel: Unraveling the Impact of Experience Over Age Over the First 18 Months of Licensure.

OBJECTIVE: This study examined how driving attention develops with age and driving. METHODS: In this observational longitudinal study, 190 adolescents (53% female, 73% Black) were enrolled across four groups: 16- and 18-year olds with and without driving experience. They underwent driving simulation with eye-tracking technology seven times over 18 months. By using a combination of factorial and longitudinal designs, the study examined the individual and combined effects of age and driving experience on driving attention over time. RESULTS: Licensed participants had higher odds of glancing at safety-critical events initially (OR = 15.01, 95% CI: 1.36-165.53), but these odds decreased at higher driving speeds (b = -0.17, p<.01). Average glance length decreased over time (b = -0.26, p=.01), but less so in licensed participants (b=0.14, p=.01). Several visual behaviors were influenced by environmental and driving factors. CONCLUSIONS: Motor vehicle crashes (MVCs) are burdensome and costly to society. This study focused on the role of inattention in MVCs, particularly during the risky period of adolescence. Findings indicated that driving experience, as determined by licensure, had a considerable impact on visual behavior in both the short term (within two weeks of obtaining a license) and over the first 18 months of independent driving. Overall, these findings suggest that licensed adolescents are more likely to identify potential hazards on the road and navigate safely. To ensure effective guidance, pediatric psychologists and other professionals should consider the unique circumstances, needs, and concerns of individual patients.

Developmental trajectories of driving attention in adolescents: Preliminary findings from REACT.

OBJECTIVE: To characterize the trajectory of driving attention as a function of age and driving experience. Hypotheses. The rate of change in driving attention will be greater for 16- compared to 18-year-olds and those acquiring driving experience (vs. non-drivers). Age and driving experience will interact, with the effect of driving experience being stronger among 16- compared to 18-year-olds. METHODS: In this longitudinal study, 190 adolescents were enrolled into 4 groups: (1) 16-year-olds and (2) 18-year-olds recruited within 2 weeks of obtaining a full driver's license; (3) 16-year-olds and (4) 18-year-olds with no driving experience (no permit/license, no intention to obtain either over study period). At seven time points over 18 months, participants drove in a high-fidelity driving simulator integrated with eye tracking. Participants completed three experimental drives with three safety critical events and varying cognitive load conditions. Driving attention was measured by vertical and horizontal eye movements, number of glances, and glance length. A multilevel model using SAS PROC MIXED (SAS 9.4) will estimate the baseline intercept and slope of driving attention over time, with baseline age, driving experience, and their interaction serving as predictors of intercept and slope. RESULTS: Preliminary analyses suggest driving attention changes over time as a function of age, driving experience, and across cognitive load conditions. CONCLUSIONS: Inattention is the primary contributor to motor vehicle crashes. It is critical to gain a clear understanding of how driving attention changes during adolescence, the riskiest developmental period for drivers. Results will reveal how driving impacts attention development through practice, providing a target for intervention.

Data Reduction Solution for Driving Simulator.

This paper describes the methods that have been developed and implemented to process research participant data generated by a high fidelity driving simulator that has been integrated with eye tracking equipment. The driving simulator is used for experimental studies to understand driving behavior. Solutions are implemented to programmatically process the output of the simulator and transform the raw data from these research experiments to an analysis ready format. The algorithm is tested across the data for numerous participants with varying scenarios within the experiments and is further curated to meet the requirements and standards of the research studies that require the use of driving simulator to generate data.