This paper investigates the task of predicting drivers’ Brake Reaction Time (BRT) using machine learning methods in the context of driving safety. Data is collected using a driving simulator, and interpretability tools such as variable im- portance and multidimensional partial dependence plots are utilised to interpret the results. The study provides insights into the factors influencing driving safety, with implications for driver training and road safety interventions.

Alessandro Albano , Giuseppe Salvo, Salvatore Russotto (2024). Predictive modeling of drivers’ brake reaction time through machine learning methods. In Proceedings of the SDS 2024 Conference.

Predictive modeling of drivers’ brake reaction time through machine learning methods

Alessandro Albano
;
Giuseppe Salvo
;
Salvatore Russotto
2024-01-01

Abstract

This paper investigates the task of predicting drivers’ Brake Reaction Time (BRT) using machine learning methods in the context of driving safety. Data is collected using a driving simulator, and interpretability tools such as variable im- portance and multidimensional partial dependence plots are utilised to interpret the results. The study provides insights into the factors influencing driving safety, with implications for driver training and road safety interventions.
2024
Settore SECS-S/01 - Statistica
978-88-5509-645-4
Alessandro Albano , Giuseppe Salvo, Salvatore Russotto (2024). Predictive modeling of drivers’ brake reaction time through machine learning methods. In Proceedings of the SDS 2024 Conference.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/639415
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