Abstract
This paper proposes the hybrid system model identified by a PWARX (piecewise affine autoregressive exogenous) model for modeling human driving behavior. In the proposed model, the mode segmentation is carried out automatically and the optimal number of modes is decided by a novel methodology based on consistent variable selection. In addition, model flexibility is added within the ARX (autoregressive exogenous) partitions in the form of statistical variable selection. The proposed method is able to capture both the decision-making and motion-control facets of the driving behavior. The resulting model is an optimal basal model which is not affected by the choice of data, where the explanatory variables are allowed to vary within each ARX region, thus, allowing a higher-level understanding of the motion-control aspect of the driving behavior, as well as explaining the driver’s decision-making. The proposed model is applied to model the car-following driving task based on real-road driving data, as well as to ROS-CARLA-based car-following simulation and compared to Gipp’s driver model. Obtained results that show better performance both on prediction performance and mimicking actual real-road driving demonstrates and validates the usefulness of the model.
Highlights
A hybrid system identified by a piecewise affine autoregressive exogenous (PWARX) model for modeling human driving behavior is proposed, wherein the mode segmentation is carried out automatically, and the optimal number of modes is decided by a novel methodology based on consistent variable selection (Section 2.4)
The PWARX model was used to capture the dynamical characteristics of each sub-model
This study presented a novel method for deciding the optimal number of behavioral segments from data based on consistent variable selection, thereby making it suitable for application in clustering or segmenting problems, where structural consistency of the identified clusters is of interest
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The automotive industry has seen significant advances in autonomous driving and advanced driving assistance systems (ADAS) in recent times [1], from a technological point of view and from a regulatory point of view [2]. These advances have been spurred on by corresponding advances in sensor technology [3].
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