Abstract
Concerning autonomous driving, lane‐changing (LC) is essential, particularly within complicated dynamic settings. It is a challenging task to model LC since driving behavior is complicated and uncertain. The present study adopts a dual‐layer feed‐forward backpropagation neural network involving sigmoid hidden neurons and linear output neurons for evaluating intrinsic LC complexity. Furthermore, the estimation and validation of the model were performed by large‐scale trajectory data. Empirical LC data were obtained from the Next Generation Simulation (NGSIM) project for training and testing the neural network‐based LC model. The findings revealed that the introduced model could make precise LC predictions of vehicles under small trajectory errors and satisfactory accuracy. The present work assessed LC beginning/endpoints and velocity estimates by analyzing the vehicles around. It was observed that the neural network model yielded almost the same predictions as the observational LC trajectories as well as following vehicle trajectories on the original and target lanes. Furthermore, for LC behavior characteristic validation, the neural network‐produced LC gap distributions underwent comparisons to real‐life data, demonstrating the characteristics of LC gap distributions not to differ from the real‐life LC behavior substantially. Eventually, the introduced neural network‐based LC model was compared to a support vector regression‐based LC model. It was found that the trajectory predictions of both models were adequately consistent with the observational data and could capture both lateral and longitudinal vehicle movements. In turn, this demonstrates that the neural network and support vector regression models had satisfactory performance. Also, the proposed models were evaluated using new inputs such as speed, gap, and position of the subject vehicle. The analysis findings indicated that the performance of the proposed NN and SVR models was higher than the model with new inputs.
Highlights
It is expected that an interconnected environment contributes to the solving of a large number of transportation problems in association with mobility, efficiency, environmental impacts, and safety
Other models could be used for lane changing, but a review of data-driven LC researches indicates that neural network (NN) have been the most exciting instruments
long short-term memory (LSTM) takes more longer to train than NN, and LSTM is easy to overfit. us, the SVR and NN approaches can be said to have good performance. e NNbased and SVR-based LC models were compared. e same number of trajectories was employed for making comparisons
Summary
It is expected that an interconnected environment contributes to the solving of a large number of transportation problems in association with mobility, efficiency, environmental impacts, and safety. One can classify LC into LC decisions (LCD) and LC implementation (LCI) In the former, drivers have mental motivation for changing lanes based on the around traffic, while the latter refers to a physical procedure, in which vehicles move from a lane to a target one [21]. Earlier works proved that data-driven models outperformed conventional analytical ones in several characteristics, e.g., trajectory accuracy and traffic flow characteristic replication [35,36,37,38,39]. Such studies adopted the neural network (NN) approach in several variants. Data-driven and analytical models mostly take into account LCD or LCI separately. is may not lead to complete LC process reproduction consisting of LCI and LCD and their influence on the traffic behavior
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