Driver Behavior Analysis via Two-Stream Deep Convolutional Neural Network

Chen, Ju-Chin; Lee, Chien-Yi; Huang, Po-Hao; Lin, Cheng-Rong

doi:10.3390/app10061908

Cited by 28 publications

(17 citation statements)

References 43 publications

(95 reference statements)

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Section: Comparison With Other Methodsmentioning

confidence: 99%

“…We furthermore compared the proposed approach in the public benchmark AUC with three state-of-the-art approaches, including [18], [41], and [4]. The authors of [18] performed a re-split of the AUC dataset, letting the methods train on a set of drivers and tested on never seen drivers. As the proposed framework was trained on our dataset, we randomly selected a set of 2000 images, corrected the corresponding annotations, and then carried out the evaluation.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…This motivates the need for robust driver body detection. The authors of [18] have proposed a driver behavior analysis system using a two-stream convolutional neural network (CNN) model. A two-dimensional (2D) ConvNet was used to construct the spatial and temporal ConvNet streams, which was pre-trained using the ImageNet dataset.…”

Section: B Detection Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Robust Deep Learning-Based Driver Distraction Detection and Classification

et al. 2021

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Driver distraction is a major cause of road accidents. Distracting activities while driving include text messaging and talking on the phone. In this paper, we propose a robust driver distraction detection system that extracts the driver's state from the recordings of an onboard camera using Deep Learning. We consider ten driving activities, which consist of one normal driving and nine distracted driving behaviors. Nine drivers were included in the experiments, and each one was asked to perform the ten activities in naturalistic and simulated driving situations. The main feature of the proposed solution is the extraction of the driver's body parts, using deep learning-based segmentation, before performing the distraction detection and classification task. Experimental results show that the segmentation module significantly improves the classification performance. The average accuracy of the proposed solution exceeds 96% on our dataset and 95% on the public AUC dataset.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Comparison With Other Methodsmentioning

confidence: 99%

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Robust Deep Learning-Based Driver Distraction Detection and Classification

et al. 2021

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“…The first paper [1] proposes a driver behavior analysis system using one spatial stream ConvNet to extract the spatial features and one temporal stream ConvNet to capture the driver's motion information. The two-dimensional (2D) ConvNet is used to construct the spatial and temporal ConvNet streams, and they were pre-trained by the large-scale ImageNet.…”

Section: Contributionsmentioning

confidence: 99%

Actionable Pattern-Driven Analytics and Prediction

Lin

Chen

2021

Applied Sciences

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“…A model for incorporating human factors into an ADAS is proposed in [ 12 ]. Other safety-related applications where driving style awareness is of interest include driving fatigue detection [ 13 ] and distraction detection [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Holistic Vehicle Instrumentation for Assessing Driver Driving Styles

Garrosa

Olmeda

Toro

et al. 2021

Sensors

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Nowadays, autonomous vehicles are increasing, and the driving scenario that includes both autonomous and human-driven vehicles is a fact. Knowing the driving styles of drivers in the process of automating vehicles is interest in order to make driving as natural as possible. To this end, this article presents a first approach to the design of a controller for the braking system capable of imitating the different manoeuvres that any driver performs while driving. With this aim, different experimental tests have been carried out with a vehicle instrumented with sensors capable of providing real-time information related to the braking system. The experimental tests consist of reproducing a series of braking manoeuvres at different speeds on a flat floor track following a straight path. The tests distinguish between three types of braking manoeuvre: maintained, progressive and emergency braking, which cover all the driving circumstances in which the braking system may intervene. This article presents an innovative approach to characterise braking types thanks to the methodology of analysing the data obtained by sensors during experimental tests. The characterisation of braking types makes it possible to dynamically classify three driving styles: cautious, normal and aggressive. The proposed classifications allow it possible to identify the driving styles on the basis of the pressure in the hydraulic brake circuit, the force exerted by the driver on the brake pedal, the longitudinal deceleration and the braking power, knowing in all cases the speed of the vehicle. The experiments are limited by the fact that there are no other vehicles, obstacles, etc. in the vehicle’s environment, but in this article the focus is exclusively on characterising a driver with methods that use the vehicle’s dynamic responses measured by on-board sensors. The results of this study can be used to define the driving style of an autonomous vehicle.

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Driver Behavior Analysis via Two-Stream Deep Convolutional Neural Network

Cited by 28 publications

References 43 publications

Robust Deep Learning-Based Driver Distraction Detection and Classification

Robust Deep Learning-Based Driver Distraction Detection and Classification

Actionable Pattern-Driven Analytics and Prediction

Holistic Vehicle Instrumentation for Assessing Driver Driving Styles

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