Evaluation and Visualization of Driver Inattention Rating From Facial Features

Dua, Isha; Nambi, Akshay; Jawahar, C. V.; Padmanabhan, Venkata N.

doi:10.1109/tbiom.2019.2962132

Cited by 14 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It does not evaluate the risk perception k-NN classifier [31] Analyze the movement of neighboring vehicles while shifting the driver's sight Use a longer interval without analyzing gaze Advanced driver assistance systems (ADASs) [32] It defines the "braking" behavior as the driver's body movement It is difficult to identify specific drivers and predict their behavior braking Convolutional neural network (CNN) [33] Founded on the analysis of time and space characteristics of the -driving state Adjacent levels of attention rating are not clear Neural network algorithm [5] To be more observant and concurrently conscious of numerous vehicles positions in order to react swiftly The problem of sensing abnormal driver behavior with the support of face netting and analyzation is tough Feedforward neural network (FFNN) [6] Identification task to identify evaluations with different characteristics It is difficult to collect more real-world data sets and identify deeper driver behavior.…”

Section: Techniquementioning

confidence: 99%

Deriving Driver Behavioral Pattern Analysis and Performance Using Neural Network Approaches

Malik¹,

Nandal²,

Dalal³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

It has been observed that driver behavior has a direct and considerable impact upon factors like fuel consumption, environmentally harmful emissions, and public safety, making it a key consideration of further research in order to monitor and control such related hazards. This has fueled our decision to conduct a study in order to arrive at an efficient way of analyzing the various parameters of driver behavior and find ways and means of positively impacting such behavior. It has been ascertained that such behavioral patterns can significantly impact the analysis of traffic-related conditions and outcomes. In such cases, the specific vehicular behavior can be detected and related data mined in order to analyze the spatial or temporal patterns of movement patterns as well as to position/track the prominent trends. This analysis seeks to determine the efficacy of such an exercise and whether the various parameters employed can help efficiently determine the various criteria for defining the driver's style. To that end, the analysis of a driver's behavioral pattern and performance utilizes a computer modeled application for generating a set of classifications based on the autonomous driving data as well as indicators that are characteristic of driver aggression. In order to draw such insights from the driver's behavior, the application is modeled using various categories of data, for instance, the steering wheel's angle, braking conditions, acceleration conditions, the vehicle speed, etc. Unlike the previously developed mechanisms for analyzing the system-based driver behavioral patterns, which were not very efficacious, this endeavor assimilates the contemporary breakthroughs in real-world scenario analysis approaches and driver behavior classification methods. Based on the system capabilities and desired outcomes, distinct strategies can be employed in order to detect the target driver's behavior. In this specific case, neural network algorithms were utilized in order to conduct an intensive study to determine and analyze the prevailing driver behavior and driving styles. This proposed approach evaluated multiple factors that were determinants in identifying specific driver behavior and driving styles. The results of this experiment that utilized Python, indicated that the driver model in question was successful in achieving a 90% accuracy in terms of logistic regression.

show abstract

Section: Techniquementioning

confidence: 99%

Deriving Driver Behavioral Pattern Analysis and Performance Using Neural Network Approaches

Malik¹,

Nandal²,

Dalal³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…[6][7][8][9] There are two categories of gaze estimation methods, one only utilizes the head pose [10][11][12][13][14] and the other utilizes driver's head pose plus eye pose. [15][16][17][18][19][20][21][22] A system 10 that can continuously estimate driver's head movement, tracks facial features and estimates head posture based on the geometric relationship between 1 School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, China them. Localized Gradient Orientation histograms and Support Vector Regressors were combined, 11 which introduced a heading tracking model built upon 3D head motion estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Yang et al 18 proposed OLS (orthogonal least squares) model to describe the relationship between the facial feature that including eye pose plus head pose and gaze on the driver's view. A system presented by Dua et al, 19 AUTORATE, which monitored driver's attention by combining head orientation and eye gaze. However, the contribution of the allocation ratio of eye pose and head pose under different gaze strategies to the maximum correct rate of gaze classification is currently unclear.…”

Section: Introductionmentioning

confidence: 99%

Gaze dynamics with spatiotemporal guided feature descriptor for prediction of driver’s maneuver behavior

Yan

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

At different levels of driving automation, driver’s gaze maintains great indispensable importance on semantic perception of the surround. In this work, we model gaze dynamics and clarify its relationship with driver’s maneuver behaviors from personalized driving style. Firstly, this paper proposes an Occlusion-immune Face Detector (OFD) for facial landmark detection, which can adaptively solve the facial occlusion introduced by the body and glasses frame in the real-world driving scenarios. Meanwhile, an Eye-head Coordination Model is brought up to bridge the error gap in gaze direction through determining eye pose and head pose fused pattern. Then, a vectorized spatiotemporal guidance feature (STGF) descriptor combining gaze accumulation and gaze transition frequency is proposed to construct gaze dynamics within a time window. Finally, we predict driver’s maneuver behavior through STGF descriptor considering different driving styles to clarify the relationship between gaze dynamics and driving maneuver task. Natural driving data are sampled in a dedicated instrumented vehicle testbed, on which 15 drivers with three kind of driving styles participated. Experimental results show that the prediction model achieves the best performance, estimating driver’s behavior an average of 1 s ahead of actual behavior with 83.6% accuracy considering driving style, compared with other approaches.

show abstract

Person identification using autoencoder-CNN approach with multitask-based EEG biometric

Bandana Das,

Kumar Ram,

Sathya Babu

et al. 2024

Multimed Tools Appl

View full text Add to dashboard Cite

Evaluation and Visualization of Driver Inattention Rating From Facial Features

Cited by 14 publications

References 33 publications

Deriving Driver Behavioral Pattern Analysis and Performance Using Neural Network Approaches

Deriving Driver Behavioral Pattern Analysis and Performance Using Neural Network Approaches

Gaze dynamics with spatiotemporal guided feature descriptor for prediction of driver’s maneuver behavior

Person identification using autoencoder-CNN approach with multitask-based EEG biometric

Contact Info

Product

Resources

About