Early Prediction of Driver's Action Using Deep Neural Networks

Gite, Shilpa; Agrawal, Himanshu

doi:10.4018/ijirr.2019040102

Cited by 15 publications

(18 citation statements)

References 20 publications

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“…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. The LSTM model achieved an F1-score of 92.12% compared to SVM, Hidden Markov Model (HMM), Feed Forward Neural Networks (FFNN), Fusion-RNN-Exp-Loss (F-RNN-EL) models that achieved an F1-score of 65.40%, 75.72%, 85.76%, and 87.56%, respectively.…”

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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“…In reality, all transportation infrastructure and driving rules are designed with human-driven vehicles in mind [82]. Some previous studies [37], [41], and [96] [47]. Additionally, the process of driver behavior cloning can be used to predict behavior in any driving situation [109].…”

Section: Adaptive Mobilitymentioning

confidence: 99%

Behavior Prediction of Traffic Actors for Intelligent Vehicle Using Artificial Intelligence Techniques: A Review

Kolekar

Gite²,

Pradhan

et al. 2021

IEEE Access

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Intelligent vehicle technology has made tremendous progress due to Artificial Intelligence (AI) techniques.Accurate behavior prediction of surrounding traffic actors is essential for the safe and secure navigation of the intelligent vehicle. Minor misbehavior of these vehicles on the busy roads may lead to an accident. Due to this, there is a need for vehicle behavior research work in today's era. This research article reviews traffic actors' behavior prediction techniques for intelligent vehicles to perceive, infer, and anticipate other vehicles' intentions and future actions. It identifies the key strategies and methods for AI, emerging trends, datasets, and ongoing research issues in these fields. As per the authors' knowledge, this is the first systematic literature review dedicated to the vehicle behavior study examining existing academic literature published by peer review venues between 2011 and 2021. A systematic review was undertaken to examine these papers, and five primary research questions have been addressed. The findings show that using sophisticated input representation that includes traffic rules and road geometry, artificial intelligence-based solutions applied to behavior prediction of traffic actors for intelligent vehicles have shown promising success, particularly in complex driving scenarios. Finally, the paper summarizes the most widely used approaches in behavior prediction of traffic actors for intelligent vehicles, which the authors believe serves as a foundation for future research in behavior prediction of surrounding traffic actors for secure and accurate intelligent vehicle navigation. INDEX TERMS intelligent driving; deep learning; intelligent vehicles; vehicle behavior prediction; pedestrian behavior prediction. Inclusion criteriaResearch articles must be original research articles instead of review/survey articles. Research articles that are published between2011 and 2021.Research articles that answer at least one research question. Research articles must contain search keywords either in the title, abstract, or full text. The proposed solution in the research article must be evaluated. The solution developed must be aimed to solve problems on behavior prediction of traffic actors. Exclusion criteriaNon-English Research articles Duplicate research articles Research articles with non-availability of full text Research articles that are not relevant to pedestrians and vehicle behavior prediction concerning intelligent vehicle III. RESULTSThis section summarizes the findings of systematic analysis. It answers the research questions posed above based on the results of this review process.

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“…They used an in-car video assistant system to present the driver's occluded view when the driver's view is occluded by truck. An effort for driver body tracking and activity analysis, posture recognition, and action predication, have been studied in [346], [347] respectively.…”

Section: A Scholarly Cvasmentioning

confidence: 99%

Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues

2020

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The proliferation of multimedia devices over the Internet of Things (IoT) generates an unprecedented amount of data. Consequently, the world has stepped into the era of big data. Recently, on the rise of distributed computing technologies, video big data analytics in the cloud has attracted the attention of researchers and practitioners. The current technology and market trends demand an efficient framework for video big data analytics. However, the current work is too limited to provide a complete survey of recent research work on video big data analytics in the cloud, including the management and analysis of a large amount of video data, the challenges, opportunities, and promising research directions. To serve this purpose, we present this study, which conducts a broad overview of the state-of-the-art literature on video big data analytics in the cloud. It also aims to bridge the gap among large-scale video analytics challenges, big data solutions, and cloud computing. In this study, we clarify the basic nomenclatures that govern the video analytics domain and the characteristics of video big data while establishing its relationship with cloud computing. We propose a service-oriented layered reference architecture for intelligent video big data analytics in the cloud. Then, a comprehensive and keen review has been conducted to examine cutting-edge research trends in video big data analytics. Finally, we identify and articulate several open research issues and challenges, which have been raised by the deployment of big data technologies in the cloud for video big data analytics. To the best of our knowledge, this is the first study that presents the generalized view of the video big data analytics in the cloud. This paper provides the research studies and technologies advancing the video analyses in the era of big data and cloud computing. INDEX TERMS big data, intelligent video analytics, cloud-based video analytics system, video analytics survey, deep learning, distributed computing, intermediate results orchestration, cloud computing.

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Early Prediction of Driver's Action Using Deep Neural Networks

Cited by 15 publications

References 20 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

Behavior Prediction of Traffic Actors for Intelligent Vehicle Using Artificial Intelligence Techniques: A Review

Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues

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