Driving Style-Based Conditional Variational Autoencoder for Prediction of Ego Vehicle Trajectory

Kim, Dongchan; Shon, Hyukju; Kweon, Nahyun; Choi, Sangdun; Yang, Chanuk; Huh, Kunsoo

doi:10.1109/access.2021.3138502

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…In response, hybrid models combining traditional machine learning algorithms with deep learning have emerged. Zhang [22].…”

Section: Literature Reviewmentioning

confidence: 99%

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Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP

Cheng,

Gao,

Jia

2023

Applied Sciences

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To enhance the lane-changing safety of autonomous vehicles, it is crucial to accurately identify the driving styles of human drivers in scenarios involving the coexistence of autonomous and human-driven vehicles, aiming to avoid encountering vehicles exhibiting hazardous driving patterns. In this study, based on the real traffic flow data from the Next Generation Simulation (NGSIM) dataset in the United States, 301 lane-changing vehicles that meet the criteria are selected. Six evaluation parameters are chosen, and principal component analysis (PCA) is employed for dimensionality reduction in the data. The K-means algorithm is then utilized to cluster the driving styles, classifying them into three categories. Finally, ant colony optimization (ACO) of a backpropagation (BP) neural network model was constructed, utilizing the dimensionality reduction results as inputs and the clustering results as outputs for the purpose of driving style recognition. Simulation experiments are conducted using MATLAB Version 9.10 (R2021a) for comparative analysis. The results indicate that the constructed ACO-BP model achieved an overall recognition accuracy of 96.7%, significantly higher than the recognition accuracies of the BP, artificial neural network (ANN), and gradient boosting machine (GBM) models. The ACO-BP model also exhibited the fastest recognition speed among the four models. Moreover, the ACO-BP model shows varied improvements in recognition accuracy for each of the three driving styles, with an increase of 13.7%, 4.4%, and 4.3%, respectively, compared to the BP model. The simulation results validate the high accuracy, real-time capability, and classification effectiveness of this model in driving style recognition, providing new insights for this field.

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“…In response, hybrid models combining traditional machine learning algorithms with deep learning have emerged. Zhang [22].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In Equations ( 20)- (22), n denotes the total number of samples; x i denotes the i sample j's true value; and y i signifies the predicted value of the model for the same sample i. The smaller the value of MSE, RMSE, and MAE, the more reasonable the corresponding hidden layer's node count.…”

Section: Model Parameter Settingmentioning

confidence: 99%

Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP

Cheng,

Gao,

Jia

2023

Applied Sciences

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“…Unsupervised learning [2][3][4][5][6] and semi-supervised learning [7][8][9] methods require a smaller amount of data but face challenges in obtaining reliable sample features within limited data. In situations where data are sufficiently abundant, researchers opt for supervised learning for driver style recognition [10][11][12][13][14][15][16]. This approach achieves high accuracy but demands high requirements for both the quantity and quality of training data.…”

Section: Introductionmentioning

confidence: 99%

Driving Style Recognition Method Based on Risk Field and Masked Learning Techniques

Jin,

Zhu,

Liu

et al. 2024

Mathematics

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With the increasing demand for road traffic safety assessment, global concerns about road safety have been rising. This is particularly evident with the widespread adoption of V2X (Vehicle-to-Everything) technology, where people are more intensively focused on how to leverage advanced technological means to effectively address challenges in traffic safety. Through the research of driving style recognition technology, accurate assessment of driving behavior and the provision of personalized safety prompts and warnings have become crucial for preventing traffic accidents. This paper proposes a risk field construction technique based on environmental data collected by in-vehicle sensors. This paper introduces a driving style recognition algorithm utilizing risk field visualization and mask learning technologies. The research results indicate that, compared to traditional classical models, the improved algorithm performs excellently in terms of accuracy, stability, and robustness, enhancing the accuracy of driving style recognition and enabling a more effective evaluation of road safety.

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“…However, the occupant's driving experience may become worse with this change [5]- [7]. Furthermore, since individual driving styles and operating habits are characterized by a wide range of diversity and dynamic changes [8], [9], it is difficult for a universally designed TTC to meet the driving needs of all individuals [10], [11]. Therefore, the development of humanlike TTC method is of great research significance and value.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking Theory

Tang,

Yan,

Wang

2023

IEEE Access

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By improving the ability of Trajectory Tracking Control (TTC) algorithms to mimic the manipulation behaviors of real drivers, which is of great significance in improving the personalized driving experience of autonomous vehicles. In this paper, we propose a TTC method that combines the Adaptive Control of Thought-Rational (ACT-R) cognitive theory framework with the Preview Tracking (PT) theory. Firstly, by analyzing and describing the ACT-R cognitive framework and the PT theory, a TTC framework that combines the two theories is proposed. Secondly, a virtual driving simulator was built to collect driving data from 30 drivers and construct a driving memory database. Thirdly, a central production system for driver trajectory tracking is designed, which consists of: three control modes, production rules, a timing generator, and filtering methods for driving memory segments. Fourthly, the TTC method based on PT theory is designed to adapt to different control modes. Finally, statistical and comparative analyses of the human-like trajectory tracking results of the proposed method were carried out through co-simulation experiments, and it was verified that the human-like performance of the proposed new method had a high degree of similarity with the manipulation behaviors and the vehicle motion state of the real driver. The real-vehicle experiments are carried out, which verifies the consistency of the proposed method with the results of the simulation experiments.INDEX TERMS Trajectory tracking control; human-like driving; autonomous vehicles; ACT-R cognitive framework; preview tracking theory.

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Driving Style-Based Conditional Variational Autoencoder for Prediction of Ego Vehicle Trajectory

Cited by 8 publications

References 19 publications

Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP

Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP

Driving Style Recognition Method Based on Risk Field and Masked Learning Techniques

Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking Theory

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