Assessing the effectiveness of driver training interventions in improving safe engagement with vehicle automation...

This study investigates how targeted training interventions can improve safe driver interaction with vehicle automation (VA) systems, focusing on Adaptive Cruise Control (ACC) and Lane Keeping Assist (LKA), both safety-critical advanced driver assistance systems (ADAS). While VA features can enhance roadway safety, they do not function reliably under all conditions. Drivers' limited awareness of system boundaries can cause overreliance or missed interventions, raising crash risk. Effective training reduces misuse and enhances road safety by promoting correct knowledge and application.

A review of multiple automakers' owners' manuals revealed inconsistencies in describing ACC and LKA functions. Three training formats were compared: (1) owners' manual (OM), (2) knowledge-based (KB) with summarized operational guidelines and visual aids, and (3) skill-based hands-on practice in a driving simulator (SIM). Thirty-six participants with no prior VA experience were randomly assigned to one group. Safety-relevant outcomes - system comprehension (quiz scores) and real-world engagement (frequency and duration of activations) - were analyzed using mixed-effects and negative binomial models.

KB training produced the greatest improvements in comprehension of system limitations, as well as safer engagement patterns. Compared with OM participants, KB participants achieved significantly higher quiz scores and engaged LKA and ACC more often (1.4 and 1.45 times, respectively); they also demonstrated greater awareness of scenarios requiring manual control, indicating reduced risk of inappropriate reliance. Older drivers exhibited longer activations overall, highlighting age-related differences in reliance and potential safety implications.

Short, targeted training can significantly improve safe and effective VA system use, particularly for senior drivers. These results highlight training as a proactive safety intervention to reduce human-automation mismatch and enhance system reliability in real-world driving.

The findings support standardized, accessible, age-tailored training to align driver behavior with VA safety standards, therefore preventing accidents and ensuring safer automation integration for all.