Aggressive Driving Detection Using Deep Learning-based Time Series Classification

Moukafih, Youness; Hafidi, Hakim; Ghogho, Mounir

doi:10.1109/inista.2019.8778416

Cited by 46 publications

(35 citation statements)

References 18 publications

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“…In [14], [29], [37], the authors reported that the most common artificial intelligence algorithms utilized to classify driving behaviors are Fuzzy Logic (FL), Random Forests (RF), k-Nearest Neighbor (kNN), the Support Vector Machine (SVM), and LSTM. However, the authors in [13], [24] experimentally proved that the computational models based on LSTM are the most suitable for driving behavior analysis. The LTSM architecture was also utilized in [25] to develop a driving action prediction system to predict the driver's action a few seconds in advance.…”

Section: ) Classification Modelsmentioning

confidence: 99%

“…These methodologies can be categorized based on (1) the features that can be extracted and derived from the collected data (e.g., acceleration, deceleration, and brake) [6], [11]; (2) the computational models for classifying driving behaviors [19]; (3) driving behavior outputs (e.g., normal, drowsy, or aggressive) [20], [21]; and (4) the performance metrics that evaluate these models [22], [23]. Some of these researches have experimentally proven that the computational models based on a Long Short-Term Memory (LSTM) recurrent neural network (RNN) architecture are the most suitable for driving behavior analysis [13], [24], [25]. However, few of these studies have experimentally evaluated the accuracy of driving behavior classification models based on various time-series window sizes and sampling rates.…”

Section: Introductionmentioning

confidence: 99%

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Driving Behavior Classification Based on Oversampled Signals of Smartphone Embedded Sensors Using an Optimized Stacked-LSTM Neural Networks

Khodairy

Abosamra

2021

IEEE Access

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Driving behavior classification is an essential real-world requirement in different contexts. In traffic safety, avoiding traffic accidents by taking corrective actions against aggressive behaviors is necessary to protect drivers. Similarly, in the automotive insurance industry, distinguishing between driving behaviors is essential to adopt usage-based insurance (UBI) policies. Also, in the ridesharing industry, monitoring and evaluating driving behaviors is critical for risk assessment and service improvement. This research presents a deep learning-based solution for driving behavior classification using an optimized Stacked-LSTM model based on the signals of smartphone embedded sensors generating two different classification models: threeclass and binary. Three-class classification distinguishes between normal, drowsy, and aggressive behaviors to support advanced driver-assistance systems (ADAS). Binary classification differentiates between aggressive and non-aggressive behaviors to support commercial applications, such as ridesharing services and automotive insurance services based on UBI. Our time-series classification models have been evaluated on the public UAH-DriveSet dataset. Using the proper number and type of features, the optimum factor of upsampling for the raw signals, and the optimum time-series window size, our proposed Stacked-LSTM model made a breakthrough in the F1-score when applied to the aforementioned dataset. The achieved scores are 99.49% and 99.34% for the Three-class and binary classification models, respectively. Comparisons with state-of-the-art models, our three-class classification model surpassed the highest published F1-score of 91% by 8.49% when applied to the aforementioned dataset.

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Section: ) Classification Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Driving Behavior Classification Based on Oversampled Signals of Smartphone Embedded Sensors Using an Optimized Stacked-LSTM Neural Networks

Khodairy

Abosamra

2021

IEEE Access

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“…The focus of this subsection is the review of the latest research attempts in detecting human driver aggressiveness using deep learning algorithms. The comparative analysis of driver aggressiveness is presented in Table 3. Y. Moukafih et al [108] have proposed a driver aggressive behavior detection scheme using two deep learning algorithms LTSM (Long Short Term Memory) and FCN (Fully Convolutional Network). For the classification of driver's behavior, testing and validation of the proposed technique, a public dataset "UAH-DriveSet" is utilized.…”

Section: Comparative Study Of Driver Aggressiveness Detection Techmentioning

confidence: 99%

Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

2020

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Human drivers have different driving styles, experiences, and emotions due to unique driving characteristics, exhibiting their own driving behaviors and habits. Various research efforts have approached the problem of detecting abnormal human driver behavior with the aid of capturing and analyzing the face of driver and vehicle dynamics via image and video processing but the traditional methods are not capable of capturing complex temporal features of driving behaviors. However, with the advent of deep learning algorithms, a significant amount of research has also been conducted to predict and analyze driver's behavior or action related information using neural network algorithms. In this paper, we contribute to first classify and discuss Human Driver Inattentive Driving Behavior (HIDB) into two major categories, Driver Distraction (DD), Driver Fatigue (DF), or Drowsiness (DFD). Then we discuss the causes and effects of another human risky driving behavior called Aggressive Driving behavior (ADB). Aggressive driving Behavior (ADB) is a broad group of dangerous and aggressive driving styles that lead to severe accidents. Human abnormal driving behaviors DD, DFD, and ADB are affected by various factors including driver experience/inexperience of driving, age, and gender or illness. The study of the effects of these factors that may lead to deterioration in the driving skills and performance of a human driver is out of the scope of this paper. After describing the background of deep learning and its algorithms, we present an in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction, Fatigue/Drowsiness, and Aggressiveness of a human driver. We attempt to achieve a comprehensive understanding of HIADB detection by presenting a detailed comparative analysis of all the recent techniques. Moreover, we highlight the fundamental requirements. Finally, we present and discuss some significant and essential open research challenges as future directions.

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“…Safety is another objective in CAS context-aware drive behavior applications. [104] and [105] define driving behavior as normal and aggressive, but their works differ through the used classification algorithms. In [104] are evaluated the following algorithms: SVM, Radial Basis Function Network (RBF), Logistic Regression, Bayesian Network, Decision Tree, k-NN and Naïve Bayes.…”

Section: Urban Planningmentioning

confidence: 99%

“…By comparison, the SVM algorithm achieved an accuracy of 93.25%. Whereas, in [105] are analyzed Random Forest (RF), Random Feature Selection (RFS), and Long Short Term Memory Fully Convolutional Network (LTSM-FCN). The LTSM-FCN method reached 95.88% performance to differentiate between aggressive or normal driving behavior, characterized by six driving events such as speed, acceleration, orientation, car position relative to lane center, time of impact to ahead vehicle and road width.…”

Section: Urban Planningmentioning

confidence: 99%

Context Aware Control Systems: An Engineering Applications Perspective

et al. 2020

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Cyber-physical systems revolve around context awareness, empowering objective-oriented services, products and operations based on real data. Self-aware and self-control systems are core elements in the Industry 4.0 framework towards self-sustainable adaptive manufacturing and personalized services. This development is witnessed by the context-aware pervasive assistance to users and machines in decisions making process for optimizing product performance and economic yield. While integration of the virtual and the physical world entails smart sensors communication and complex data analytics, it relies on artificial intelligence tools to manage process operations. The objective of the article is to create awareness that systems & control community must address theoretical and practical aspects from a larger perspective. Context aware control is emerging as a natural solution to maximize the use of available sensing instrumentation and the relatively low cost data logging, i.e. an important source for extracting information, interpreting and using context information and adapt its functionality to the current context of use. This article presents a concise overview of applications where context aware systems and control methodologies are relevant in the seven societal challenges acknowledged by European policy-makers: Digital Society;

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Aggressive Driving Detection Using Deep Learning-based Time Series Classification

Cited by 46 publications

References 18 publications

Driving Behavior Classification Based on Oversampled Signals of Smartphone Embedded Sensors Using an Optimized Stacked-LSTM Neural Networks

Driving Behavior Classification Based on Oversampled Signals of Smartphone Embedded Sensors Using an Optimized Stacked-LSTM Neural Networks

Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

Context Aware Control Systems: An Engineering Applications Perspective

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