Generating contextual description from driving behavioral data

Bando, Takashi; Takenaka, Kazuhito; Nagasaka, Shogo; Taniguchi, Tadahiro

doi:10.1109/ivs.2014.6856476

Cited by 11 publications

(2 citation statements)

References 18 publications

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“…In recent years, semantic analysis of driving behaviors using driving behavior primitives has become a hot topic due to its high efficiency [8][9][10]. Driving behavior primitives are the smallest data segments with clear physical meanings.…”

Section: Introductionmentioning

confidence: 99%

Driving Behavior Primitive Classification Using CNN-Based Fusion Models

Cui,

Li,

Zheng

et al. 2024

IEEE Access

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Driving behavior primitives play a crucial role in semantic explanation of driving behaviors. Although much work has been done on exacting driving behavior primitives from naturalistic driving data, few studies was published on primitive classification. Driving behavior primitives are typically described by multi-dimensional variables with varying durations, which leads to the inefficiency of the traditional classification methods. There hence, a CNN-based fusion model for primitive classification is proposed in this paper. Primitive feature matrix is constructed using statistical methods for the four basic and the four constructed variables, which serves as the input. A 1D-CNN is employed to extract global information of the total eight variables in the feature matrix, while a 2D-CNN is used to extract the local information. The 1D-CNN and the 2D-CNN are fused in parallel using a new fusion method to combine different types of information, and two models, namely the FC-before fusion model and the FC-after fusion model, are acquired. Compared with the classical methods, the empirical results demonstrate that CNN-based fusion model can recognize driving behavior primitives more accurately. Specifically, the FC-after fusion model achieves an accuracy of 91.12% and a macro F1-score of 90.88%, while the accuracy and macro F1-score of the FCbefore fusion model are 93.47% and 92.57%, respectively. INDEX TERMSDriving behavior semantics, Driving behavior primitive classification, Deep learning, CNN-based fusion model.

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Section: Introductionmentioning

confidence: 99%

Driving Behavior Primitive Classification Using CNN-Based Fusion Models

Cui,

Li,

Zheng

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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“…In most studies about driving behavior analysis, the feature vectors that are regarded as input data of the analysis method were selected manually from the measured sensor data [ 7 , 8 , 9 ] and designed manually [ 10 ]. For example, Taniguchi et al selected the velocity, steering angle, brake pressure, accelerator position, and designed the temporal difference between the velocity and steering angle as input data of the HMM for driving behavior prediction [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Defect-Repairable Latent Feature Extraction of Driving Behavior via a Deep Sparse Autoencoder

Liu

Taniguchi

Takenaka

et al. 2018

Sensors

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Data representing driving behavior, as measured by various sensors installed in a vehicle, are collected as multi-dimensional sensor time-series data. These data often include redundant information, e.g., both the speed of wheels and the engine speed represent the velocity of the vehicle. Redundant information can be expected to complicate the data analysis, e.g., more factors need to be analyzed; even varying the levels of redundancy can influence the results of the analysis. We assume that the measured multi-dimensional sensor time-series data of driving behavior are generated from low-dimensional data shared by the many types of one-dimensional data of which multi-dimensional time-series data are composed. Meanwhile, sensor time-series data may be defective because of sensor failure. Therefore, another important function is to reduce the negative effect of defective data when extracting low-dimensional time-series data. This study proposes a defect-repairable feature extraction method based on a deep sparse autoencoder (DSAE) to extract low-dimensional time-series data. In the experiments, we show that DSAE provides high-performance latent feature extraction for driving behavior, even for defective sensor time-series data. In addition, we show that the negative effect of defects on the driving behavior segmentation task could be reduced using the latent features extracted by DSAE.

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