Abstract
This study aims to investigate the key feature variables and build an accurate decision model for merging behavior during the execution period by using a data-driven method called random forest (RF). To comprehensively explore the feature variables during merging execution period, nineteen candidate variables including speeds, relative speeds, gaps, time-to-collisions (TTCs), and locations are extracted from a dataset including 375 noise-filtered vehicle trajectories. After the variable selection process, an RF model with 9 key feature variables is finally built. Results show that the gap between the merging vehicle and its putative following vehicle and the ration of this gap to the total accepted gap are the two most important feature variables. It is because merging vehicle drivers can easily observe the putative leading vehicles and control the relative speeds and positions to the putative leading vehicles and they tend to leave more space for their putative following vehicles. Relative speed between the merging vehicle and its following vehicle in the auxiliary lane is the only variable related to the vehicles in the auxiliary lane, which means merging vehicles mainly focus on the traffic condition in the adjacent main lane. Evaluation of the performance in comparison with the state-of-the-art method reveals that the proposed method can obtain much more accurate results in both training and testing datasets, which means RF is practical for predicting the merging decision behavior during execution period and has better transferability.
Highlights
As a basic driving task, lane changing has drawn great attention recently
Lane-changing decision assistance is one of the key functions of driving assistance systems. It can help drivers make safer decisions to start a lane change. rough the Vehicular Ad-hoc Network (VANET), vehicles can communicate with the surrounding vehicles and roadside unites [8,9,10]. e lanechanging decision assistance systems can well deal with the situation of discretionary lane-changing by using the data from surrounding vehicles and roadside unites
random forest (RF) is an ensemble classifier composed of a group of decision tree classifiers and gets the prediction result by a simple majority vote. e RF model can improve the prediction accuracy of merging decision as well as help connected and autonomous vehicles (CAVs) make safer decisions during merging process
Summary
As a basic driving task, lane changing has drawn great attention recently. Lane changing behavior was considered to be an important reason for traffic oscillations and accidents [1,2,3,4]. Us, this study tried use a famous machine learning technique, random forest (RF), to model the merging decision behavior during execution period. It can produce more accurate prediction results and excavate the hidden information among the data. The proposed RF method can accurately predict the merging decision during execution period, which can improve the safety and comfort level of driving assistance system if it could be incorporated into lane changing assistance system. Ese contributions can help understand the diverse influences of different variables on the merging decision and shed new insights for driver assistance systems and autonomous driving.
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