New LSTM Deep Learning Algorithm for Driving Behavior Classification

Kadri, Nesrine; Ellouze, Ameni; Ksantini, Mohamed; Turki, Sameh Hbaieb

doi:10.1080/01969722.2022.2059133

Cited by 20 publications

(4 citation statements)

References 42 publications

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“…Given that the driving actions are not independent in pure distribution but rather temporally connected, this is similar to driving patterns. Therefore, the LSTM, a variation of RNN, showed an accurate classification rate for driving behaviors when applying the data from the IMU and GPS [49][50][51]. In fact, the RNN forecasts anything in the BEVrelated indicators.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 93%

“…Multi-dimensional time-series data are challenging to apply to the majority of statistical models, including hidden Markov model (HMM) [9][10][11][12][13][14], Gaussian mixture model (GMM) [15][16][17][18][19], support vector machine (SVM) [20][21][22][23][24][25][26][27], Naive Bayes (NB) [28][29][30], fuzzy logic (FL) [31][32][33][34][35][36], and k-nearest neighbor (KNN) [20,[37][38][39][40]. The deep learning based models, including convolutional neural network (CNN) [41][42][43][44][45][46][47][48], recurrent neural network (RNN) [49][50][51][52][53][54][55]…”

Section: Introductionmentioning

confidence: 99%

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Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

et al. 2023

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In the foreseeable future, electric vehicles (EVs) will play a key role in the decarbonization of transport systems. Replacing vehicles powered by internal combustion engines (ICEs) with electric ones reduces the amount of CO2 being released into the atmosphere on a daily basis. The Achilles heel of electrical transportation lies in the car battery management system (BMS) that brings challenges to lithium-ion (Li-ion) battery optimization in finding the trade-off between driving and battery health in both the long- and short-term use. In order to optimize the state-of-health (SOH) of the EV battery, this study focuses on a review of the common Li-ion battery aging process and behavior detection methods. To implement the driving behavior approaches, a study of the public dataset produced by real-world EVs is also provided. This research clarifies the specific battery aging process and factors brought on by EVs. According to the battery aging factors, the unclear meaning of driving behavior is also clarified in an understandable manner. This work concludes by highlighting some challenges to be researched in the future to encourage the industry in this area.

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Section: Recurrent Neural Network (Rnn)mentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

et al. 2023

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“…In order to reduce the number of accidents and, thus, improve road safety, Nesrine Kadri et al designed a new recurrent neural network structure based on stacked long short-term memory (LSTM) for classifying driving behaviors. By applying the Dempster-Shafer (DS) belief function theory, the uncertainty of data was successfully overcome, thus, significantly improving the accurate classification results of driving states [20]. Amina Turki et al proposed a hybrid real-time system based on the eye-closing ratio and mouthopening ratio, which has two processes: offline and online.…”

Section: Driving Style and Traffic Flow Theorymentioning

confidence: 99%

Data Validity Analysis Based on Reinforcement Learning for Mixed Types of Anomalies Coexistence in Intelligent Connected Vehicle (ICV)

Gao,

Hu,

Wang

et al. 2024

Electronics

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Compared with traditional anomaly analysis, intelligent connected vehicle (ICV) data validity analysis is faced with a variety of data anomalies, including sensor anomalies, driving behavior anomalies, malicious tampering, and so on, which eventually leads to anomalies in the data. How to integrate the vehicle moving characteristics, driving style, and traffic flow conditions to provide an effective data detection method has become a new problem in the field of intelligent networked vehicles. Based on ICV data, a particle swarm optimization data validity detection algorithm (TE-PSO-SVM) was proposed by combining driving style and traffic flow theory to realize the effective detection of driving data. In addition, aiming at the problem of mixed types of anomalies in complex scenes, a model pool is constructed, and a model selection algorithm based on reinforcement learning (RLBMS) is proposed. Experiments on the real data set HighD show that RLBMS has a better detection effect in complex scenes of mixed types of anomalies.

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“…Scholars have conducted detailed analysis and evaluation of the EG correction system based on DL algorithms. DL algorithms have strong learning and generalization capabilities and can effectively solve many problems in natural language processing [1][2]. In the study of the design and implementation of EG correction system based on DL, some researchers have proposed a design scheme for an EG correction system based on DL algorithms.…”

Section: Introductionmentioning

confidence: 99%

Design of English Grammar Error Correction System Based on Deep Learning Algorithms

Cao

2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

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With the popularity of the Internet, English has become a globally used language, and English grammar(EG) errors have become a universal problem worldwide. EG errors not only affect daily communication but also cause reading and writing mistakes. Traditional grammar analysis methods mainly look for EG errors through static analysis, but this method can only detect some fixed types of grammar errors and cannot provide real-time EG correction. Based on this, this paper proposes an EG correction system based on deep learning(DL) algorithms. Through this system, it is hoped to provide more accurate and real-time EG error detection services. According to the experimental results of the system, it can be concluded that the grammar error recognition accuracy of the sequence model is the highest, reaching 82.2%, but its recall rate is slightly lower, only 42.3%. The accuracy of the grammar correction system based on DL can be further improved. The design, implementation, and testing processes of the system provide valuable references for the practical application of DL technologies in natural language processing tasks, such as grammar error detection and correction.

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New LSTM Deep Learning Algorithm for Driving Behavior Classification

Cited by 20 publications

References 42 publications

Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

Data Validity Analysis Based on Reinforcement Learning for Mixed Types of Anomalies Coexistence in Intelligent Connected Vehicle (ICV)

Design of English Grammar Error Correction System Based on Deep Learning Algorithms

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