Understanding the impacts of negative advanced driving assistance system warnings on hazardous materials truck drivers’ responses using interpretable machine learning

Abstract

In recent years, Artificial Intelligence (AI) has significantly enhanced road safety, with Explainable Artificial Intelligence (XAI) providing essential transparency and trust. Our research utilizes AI to improve Advanced Driving Assistance Systems (ADAS) by investigating the gap in Forward Collision Warning (FCW): the impact of previous negative warnings (false and nuisance warnings) on drivers’ response times to subsequent accurate FCWs. By integrating XAI methods, we offer insights into the factors affecting driver behavior and system trust. Utilizing extensive dataset that encompasses various driving scenarios and driver behaviors, we constructed a gradient-boosting machine model to forecast driver response times. To explain the underlying mechanics of the model, the Shapley Additive Explanations (SHAP) framework was employed, enabling a comprehensive interpretation of feature importance and inter-feature interactions. Key findings reveal that increased speeds heighten driver responsiveness due to amplified alertness, whereas slower speeds lead to delayed reactions. The influence of previous negative warnings, significantly extends response times to accurate warnings. Additionally, older drivers require longer response times. The relationship between the driving period and previous warning judgment profoundly affects subsequent driver responsiveness, indicating trust dynamics with FCW systems. By using interpretable machine learning, we provide insights into ADAS functionality, suggesting pathways for FCW responsiveness and contributing to the field of XAI applications. In the validation experiment, our approach improved driver response times, reducing the average time from 2.1 s to 1.6 s. The proportion of ignored warnings decreased from 12% to 6%, and the driver acceptance rate increased from 59% to 71%.