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

Abstract

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.

XGBoost-DNN Mixed Model for Predicting Driver’s Estimation on the Relative Motion States during Lane-Changing Decisions: A Real Driving Study on the Highway

“…Without loss of generality, in order to verify the applicability of the prediction model based on the XGBoost-DNN mixed algorithm for predicting the driver's lane change speed and distance estimation, this paper compared its performance with traditional machine algorithm models such as Logistic Regression (LR) [43], Random Forest (RF) [44], Support Vector Machine (SVM) [45,46], K-Nearest Neighbor (KNN) [47], Deep Neural Networks (DNN) [34,35], Gradient Boosting Decision Tree (GBDT) [48], and XGBoost models [49].…”

Emotion Recognition and Multi-class Classification in Music with MFCC and Machine Learning

“…Table 1 [30]- [33] displays the logistic regression model's performance metrics for a multi-class classification task. These metrics indicate the model's efficacy in predicting each class based on precision, recall, and f1-score [34].…”

An Improved Accuracy of Multiclass Random Forest Classifier with Continuous Attribute Transformation Using Random Percentile Generation

“…Another application of the LR method was used to investigate the decision-making characteristics of drivers in overtaking on the highway. This regression model approach shows driving behavior with accurate estimates without the need for prior knowledge and contributes to various driving actions in dynamic environments [18]. The method was also used as a base model on the weakly supervised object localization problem using weighted regions due to its good performance in multi-instance settings [19].…”