Toward personalized driving assistance: exploring individual differences in the effect of driving support at stop sign intersections

Personalized driving assistance systems for older drivers are promising tools for decreasing the cognitive load and promoting safe driving. However, the types of assistance that are effective for various drivers with specific characteristics are unclear. Our goal is to develop personalized driving assistance systems that adaptively provide support on the basis of driver characteristics. This pilot study focuses on driving support at stop sign intersections, and the interactions between types of support and driver characteristics on driving behavior are explored. First, we collected driving behavior data and driver characteristics information from 38 participants ( MAge = 59.3,\ SDAge = 15.1,\ RangeAge=19-83 ). They performed driving sessions on a simulator with driving support that differed in terms of its content and the amount of information provided. To examine how effective support differs depending on driver characteristics, we built regression models to explain driving behaviors considering the interactions between driver characteristics and support types. The interaction terms improved the adjusted R² by 0.08 to 0.32, indicating that driver characteristics were related to the differences in the support effect. Additionally, we employed a machine learning algorithm to uncover findings about effective support. In particular, drivers with cognitive decline tended to exhibit safe driving behaviors when provided with instructive support. In contrast, neurotic drivers seemed to have trouble processing the information provided by the driving support system. These findings reveal the importance of accounting for a wide range of driver characteristics for the personalization of support.