Dual Particle Filtering of Intelligent Driver Model Parameters for Indirect Detection of Driving Anomalies

Abstract

Abnormal driving behavior, such as excessive speed and reckless and aggressive driving, is recognized as causing more than 50% of fatal accidents. The detection of abnormal driving behavior has a wide range of applications and is expected to be used not only to directly suppress abnormal driving behavior but also to be factored into the price of automobile insurance premiums. This paper proposes a new approach to abnormal driving detection that requires neither in-car cameras nor physiological sensors. Instead, this approach makes full use of an intelligent driver model (IDM) and its parameters, where vehicle acceleration is assumed to indicate abnormal driving behavior. In an experiment using a driving simulator, subjects were asked to text on a mobile phone to collect data on their driving behavior with and without distractions. Numerical analysis showed that IDM parameters estimated by dual particle filtering could accurately detect driving abnormalities without the use of direct driver monitoring systems such as on-board cameras or sensors.

Keywords: Driving Anomaly, Anomaly Detection, Intelligent Driver Model, Particle Filter, State Estimation

DOI: 10.54941/ahfe1004584