Siamese Temporal Convolutional Networks for Driver Identification Using Driver Steering Behavior Analysis

Azadani, Mozhgan Nasr; Boukerche, Azzedine

doi:10.1109/tits.2022.3151264

Cited by 18 publications

(7 citation statements)

References 50 publications

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“…Some researchers also built CNN models to extract vehicle state data features to predict lane changing and following driving behaviors. For example, Azadani and Boukerche [26] designed a continuous-time convolution network system architecture model to identify lanechanging behaviors, with a recognition accuracy of 95.3%. Xie et al [75] constructed a CNN driving behavior recognition model to recognize lane change, braking, and other driving behaviors with an accuracy of 87.67%, which is better than KNN and FR.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…Meanwhile, it can be organically integrated with the classifier to realize end-to-end data learning and significantly improve the recognition accuracy. Some researchers try to adopt deep neural network (DNN) [20,21], convolutional neural network (CNN) [22][23][24][25][26][27][28][29], and recurrent neural network (RNN) [30][31][32][33][34][35][36][37] to construct a driving behavior recognition model, which has achieved good results. In recent years, with the widespread application of on-board sensors and CAN bus technology in cars, driving behavior data in the natural driving process have been collected and stored, which provides massive data samples for the construction of deep learning model, and the recognition methods of driving behavior are gradually evolving to deep learning model.…”

Section: Introductionmentioning

confidence: 99%

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A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

Zhao

Zhong

Zhi-jun

et al. 2022

Journal of Advanced Transportation

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The frequent traffic accidents lead to a large number of casualties and large related financial losses every year; this serious state is owed to several factors; among those, driving behavior is one of the most imperative subjects to discuss. Driving behaviors mainly include behavior characteristics such as car-following, lane change, and risky driving behavior such as distraction, fatigue, or aggressive driving, which are of great help to various tasks in traffic engineering. An accurate and reliable method of driving behavior recognition is of great significance and guidance for vehicle driving safety. In this paper, the vehicle multisensor information, vehicle CAN bus data acquisition system, and typical feature extraction methods are summarized at first. And then, several driving behavior recognition models based on machine learning and deep learning are reviewed. Through a detailed analysis of the features of random forests, support vector machines, convolutional neural networks, and recurrent neural networks used to build driving behavior recognition models, the following findings are obtained: the driving behavior model constructed by traditional machine learning model is relatively mature but it is greatly affected by feature extraction, data scale, and model structure, which affects the accuracy of the final driving behavior recognition. Deep learning model based on a neural network has achieved high accuracy in identifying driving behavior, and it may gradually become the mainstream of constructing the driving behavior model with the development of big data, artificial intelligence technology, and computer hardware. Finally, this paper points out some content that needs to be further explored, to provide reference and inspiration for scholars in this field to continue to study the driving behavior recognition model in depth.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

Zhao

Zhong

Zhi-jun

et al. 2022

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…It is necessary to go through the different applications and implications involved in the analysis and processing of motor activity or diver behavior data, in order to create driver identification systems that are less invasive and less susceptible to failures derived from uncontrolled conditions, taking advantage of the features provided by some sensors such as vehicle speed, Revolutions Per Minute (RMP) engine load, and even steering wheel variation, where is it used driving behavior analysis [ 6 ]. Moreover, Global Positioning System (GPS) is commonly deployed in car navigation systems and can also be used to learn an individual’s driving pattern, which makes it applicable in real-time applications with the least overhead costs for different resources [ 7 ], and it provides accurate position and velocity information when there is a direct line of sight to four or more satellites [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms

Espino-Salinas

Luna-García

Celaya-Padilla

et al. 2023

Sensors

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Driver identification refers to the process whose primary purpose is identifying the person behind the steering wheel using collected information about the driver him/herself. The constant monitoring of drivers through sensors generates great benefits in advanced driver assistance systems (ADAS), to learn more about the behavior of road users. Currently, there are many research works that address the subject in search of creating intelligent models that help to identify vehicle users in an efficient and objective way. However, the different methodologies proposed to create these models are based on data generated from sensors that include different vehicle brands on routes established in real environments, which, although they provide very important information for different purposes, in the case of driver identification, there may be a certain degree of bias due to the different situations in which the route environment may change. The proposed method seeks to intelligently and objectively select the most outstanding statistical features from motor activity generated in the main elements of the vehicle with genetic algorithms for driver identification, this process being newer than those established by the state-of-the-art. The results obtained from the proposal were an accuracy of 90.74% to identify two drivers and 62% for four, using a Random Forest Classifier (RFC). With this, it can be concluded that a comprehensive selection of features can greatly optimize the identification of drivers.

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“…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%

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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Siamese Temporal Convolutional Networks for Driver Identification Using Driver Steering Behavior Analysis

Cited by 18 publications

References 50 publications

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms

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

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