Deep LSTM with Dynamic Time Warping Processing Framework: A Novel Advanced Algorithm with Biosensor System for an Efficient Car-Driver Recognition

Rundo, Francesco

doi:10.3390/electronics9040616

Cited by 8 publications

(8 citation statements)

References 33 publications

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“…The overall accuracy (both training and test) of the described method stands at around 98.75%, thus, confirming the robustness of the pipeline proposed in this contribution. The previous pipeline [15] tested in this recruited dataset with four different identity to be recognized showed a performance of 97.12% significantly lower than the method proposed here. Although it is a matter of a few percentage points of difference between the two pipelines, in the case of automotive applications, the accuracy must be as high as possible.…”

Section: Resultsmentioning

confidence: 60%

“…Therefore, the PPG signal can effectively be considered as a subject's "physiological imprint" or "cardiac imprint". The PPG waveform can be used as a fingerprint or bio-marker of a car driver; this implies that the features extracted from PPG signal can be properly used to recognize the subject from which the PPG is sampled [15]. In the Figure 1 the implemented PPG bio-sensor is reported.…”

Section: Methodsmentioning

confidence: 99%

“…In particular, both LEDs are based on InGaN technology (in SMD package) emitting at 873 nm, they also have an area of 2.3 × 1.5mm 2 viewing angle of 120 and typical power emission of a few mW in the standard operation range. More details in [15][16][17]. In Fig.…”

Section: Methodsmentioning

confidence: 99%

“…The method performs well but it suffers from the limitations of similar methods that use devices external to the car, therefore problems of invasiveness and compatibility with the automotive systems of the car. In [15] the author proposed an interesting car driver identity recognition based on the usage of combined approach which includes machine learning and dynamic time warping methodology. Through the analysis of such physiological signals of the car driver (collected from specific bio-sensor embedded on the car systems) the author was able to recognize the driver identity with high accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Through the analysis of such physiological signals of the car driver (collected from specific bio-sensor embedded on the car systems) the author was able to recognize the driver identity with high accuracy. The pipeline herein proposed is an improvement of the one described in [15]. Specifically, the authors propose an approach for the car driver identity recognition based on the analysis of the "physiological imprinting" of the subject [16].…”

Section: Related Workmentioning

confidence: 99%

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Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification

Rundo

Trenta

Leotta

et al. 2021

Lecture Notes in Computer Science

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The latest generation cars are often equipped with advanced driver assistance systems usually known as ADAS (Advanced Driver Assistance Systems). These systems are able to assist the car driver leveraging several levels of automation. It is therefore essential to adapt the ADAS technology to the car driver's identity in order to personalize the provided assistance services. For these reasons, such car driver profiling algorithms have been developed by scientific community. The algorithm herein proposed is able to recognize the driver's identity with an accuracy close to 99% thanks to ad'hoc specific analysis of the driver's PhotoPlethysmoGraphic (PPG) signal. In order to rightly identify the driver profile, the proposed approach uses a 1D Dilated Temporal Convolutional Neural Network architecture to learn the features of the collected driver's PPG signal. The proposed deep architecture is able to correlate the specific PPG features with subject identity enabling the car ADAS services associated to the recognized identity. Extensive validation and testing of the developed pipeline confirmed its reliability and effectiveness.

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Section: Resultsmentioning

confidence: 60%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification

Rundo

Trenta

Leotta

et al. 2021

Lecture Notes in Computer Science

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A Deep Bidirectional LSTM-GRU Network Model for Automated Ciphertext Classification

et al. 2022

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Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) are a class of Recurrent Neural Networks (RNN) suitable for sequential data processing. Bidirectional LSTM (BLSTM) enables a better understanding of context by learning the future time steps in a bidirectional manner. Moreover, GRU deploys reset and update gates in the hidden layer, which is computationally more efficient than a conventional LSTM. This paper proposes an efficient network model based on deep BLSTM-GRU for ciphertext classification aiming to mark the category to which the ciphertext belongs. The proposed model performance was evaluated using well-known evaluation metrics on two publicly available datasets encrypted with various classical cipher methods and performance was compared against onedimensional convolutional neural network (1D-CNN) and various other deep learning-based approaches. The experimental results showed that the BLSTM-GRU cell unit network model achieved a high classification accuracy of up to 95.8%. To the best of our knowledge, this is the first time an RNN-based model has been applied for the ciphertext classification.INDEX TERMS Recurrent neural networks, bidirectional long short-term memory, gated recurrent unit, ciphertext classification, 1D-convolutional neural networks.

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Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review

Negash

Yang

2023

IEEE Access

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Driver behavior models have been used as input to self-coaching, accident prevention studies, and developing driver-assisting systems. In recent years, driver behavior recognition has revolutionized autonomous vehicles (AVs) and traffic management studies. This comprehensive survey provides an up-todate review of the different driver behavior models and modeling approaches. In heterogeneous streets where humans and autonomous vehicles operate simultaneously, predicting the intent and action of human drivers is crucial for AVs with the help of wireless communication and artificial intelligence (AI) technologies. Therefore, the review also summarizes the applications of driver behavior modeling (DBM) for effective behavior recognition and human-like AV driving. Moreover, the review also covers the application of DBM in capturing behaviors of complex dynamic driving tasks. In this review, we solely cover car-following (CF) and lane-changing (LC) maneuvers.INDEX TERMS Driver behavior modeling (DBM), autonomous vehicles, behavior recognition, human-like, car-following, lane-changing, perception, decision-making, data-driven, vehicle control. I. INTRODUCTIONIn 2018, the US Department of Transportation reported 6,734,000 motor vehicle-related accidents. 1 Among these accidents, there were 2.71 million injuries and 36,560 fatalities. These deaths and injuries also increased in the years 2019 and 2020. Earlier in 2015, specifically in the US, 80 − 94% of crashes were due to driver-related errors, and 33% were due to the wrong prediction of other drivers [1]. The statistics of accidents caused by human driver-related errors were also reported in [2] and [3].The accidents and their behind reasons will lead us back to human drivers with rich and diverse behaviors. According to Michon [4], a human as a problem solver can be a road user, a transportation consumer, a social agent, and a psycho-biological organism. The issues to be solved are controlling a vehicle, trip-making, communication, and satisfying the basic driving necessity. A human driver can have unique traits towards vehicle controlThe associate editor coordinating the review of this manuscript and approving it for publication was Divanilson Rodrigo Campelo .

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Deep LSTM with Dynamic Time Warping Processing Framework: A Novel Advanced Algorithm with Biosensor System for an Efficient Car-Driver Recognition

Cited by 8 publications

References 33 publications

Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification

Advanced Temporal Dilated Convolutional Neural Network for a Robust Car Driver Identification

A Deep Bidirectional LSTM-GRU Network Model for Automated Ciphertext Classification

Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review

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