Analysis and Modeling of Real World Car-Following Driving in Downtown Area Based on Variable Structured Piecewise Linear Model

Nwadiuto, Jude; Chin, Hyuntai; Okuda, Hiroyuki; Suzuki, Tatsuya

doi:10.1109/itsc.2019.8916962

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…The car-following model can be categorized into physical-based models and learning-based models [13]. The physical-based models are used to present the behavior of drivers with unknown parameter values These parameters have special physical meaning and can be estimated based on the obsertation data, such as Kalman filter [14], Particle filtering [15], variable structured piece wise linear (VS-PWL) model [16], and Bayesian methods [17]. In this way, it is easier to model certain and linear behavior.…”

Section: Introductionmentioning

confidence: 99%

Hybrid order characteristics in car-following behavior

Tu¹,

Du²

2020

IJEECS

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<span>This paper addresses the discovery of an interesting property in car-following processes, which was not reported in the existing literatures. A hybrid order behavior is supported by both experimental data and theoretical simulations. To demonstrate this behavior, the first order and the second order car-following behaviors are defined. Then, by comparing the first and the second order car-following behaviors in the existing analystic models and the real traffic context, this paper finds that a significant amount of the second order car-following processes in real traffic context do not match the existing models and structural mismatches are observed. The popularity and significance of such cases suggest the existence of unmodelled dynamics in the existing methods, that is, the car following behavior should be determined by more factors than the immediate proceeding vehicle. Therefore, the existing car-following models must be improved to accommodate these factors. This forms one of the main values of this paper. This paper then defines the hybrid order car-following behavior and prompts to associate this behavior with the concerned unmodelled dynamics (mismatches between the actual traffic data and the simulation from models). The neural network is employed to model such dynamics. The idea of the proposed hybrid order behavior matches the fact that the car-following behavior is determined by multiple vehicles driving in front of the subject car instead of only the immediate proceeding one. This is valuable because it provides guidance on the improvement of existing car-following models. The neural network model validates that the consideration of multiple vehicles improves the accuracy of car-following modelling.</span>

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Section: Introductionmentioning

confidence: 99%

Hybrid order characteristics in car-following behavior

Tu¹,

Du²

2020

IJEECS

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Variable Selection and Modeling of Drivers’ Decision in Overtaking Behavior Based on Logistic Regression Model With Gazing Information

Nwadiuto

Yoshino²,

Okuda

et al. 2021

IEEE Access

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This paper investigates the decision-making characteristics of the driver in the overtaking on the highway road. For the research purpose, a novel method was proposed by introducing a logistic regression model accompanied by the statistical test technique, which does not require prior knowledge about the explanatory variables. This study hypothesizes that the driver's gazing behavior is crucial for the decision-making process in driving and hence, the line-of-sight information was introduced to estimate driver's gazing behavior in the model of driver's decision specifically for reproducing the overtaking driving behavior accurately. Consequently, the proposed model realized a high describability on the decision of the driver when performing the overtaking driving task, which is one of the significant advancements of the present study with respect to the past similar studies. This study integrates the perspectives of intelligent vehicle design and cognitive science by revealing which factor the driver pays attention to in a changeable driving environment due to various observable factors. In experiments based on the driving simulator with six human subjects, the overtaking behavior was successfully estimated by specifying a set of variables to reconstruct the driver's behavior and then the proposed model provided a minimum set of necessary variables accompanied with key coefficients. In conclusion, the proposed approach based on a simple logistic regression model demonstrated driving behaviors with an accurate estimation of the driver's intention without the need for prior knowledge, and it may contribute to higher describability for various driving actions in a dynamic environment.

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Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model

2021

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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.

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Analysis and Modeling of Real World Car-Following Driving in Downtown Area Based on Variable Structured Piecewise Linear Model

Cited by 3 publications

References 18 publications

Hybrid order characteristics in car-following behavior

Hybrid order characteristics in car-following behavior

Variable Selection and Modeling of Drivers’ Decision in Overtaking Behavior Based on Logistic Regression Model With Gazing Information

Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model

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