ADMT: Advanced Driver’s Movement Tracking System Using Spatio-Temporal Interest Points and Maneuver Anticipation Using Deep Neural Networks

Gite, Shilpa; Pradhan, Biswajeet; Alamri, Abdullah; Kotecha, Ketan

doi:10.1109/access.2021.3096032

Cited by 7 publications

(7 citation statements)

References 42 publications

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“…An Advanced Driver Movement Tracking (ADMT) system was created using recurrent neural network (RCNN) and CNN, a contemporary deep learning technique [12]. Comparatively speaking, that system outperformed SVM, FFNN, and F-RNN-EL.…”

Section: Related Workmentioning

confidence: 99%

“…Previous work directed towards driver drowsiness detection had used SVM [29,32], ML classified algorithms [37], CNN [8-11, 19, 30, 36, 39], Image processing [27], lightweight CNNs [20], and [28]), pre-trained CNN [2], LSTM model [7], ML algorithms (HOG) and (NB) [4], CNN and face alignment [41], and CNN combined with RCNN [12]. While the highest performance achieved in the previous work on the Brain4cars dataset [22] was 96.05 % using CNN combined with RCNN [12]. Other datasets are also available.…”

Section: Introductionmentioning

confidence: 99%

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A CNN-LSTM-based Deep Learning Approach for Driver Drowsiness Prediction

Gomaa

Mahmoud²,

Sarhan³

2022

Journal of Engineering Research

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The development of neural networks and machine learning techniques has recently been the cornerstone for many applications of artificial intelligence. These applications are now found in practically all aspects of our daily life. Predicting drowsiness is one of the most particularly valuable of artificial intelligence for reducing the rate of traffic accidents. According to earlier studies, drowsy driving is at responsible for 25 to 50% of all traffic accidents, which account for 1,200 deaths and 76,000 injuries annually. The goal of this research is to diminish car accidents caused by drowsy drivers. This research tests a number of popular deep learning-based models and presents a novel deep learning-based model for predicting driver drowsiness using a combination of convolutional neural networks (CNN) and Long-Short-Term Memory (LSTM) to achieve results that are superior to those of state-of-the-art methods. Utilizing convolutional layers, CNN has excellent feature extraction abilities, whereas LSTM can learn sequential dependencies. The National Tsing Hua University (NTHU) driver drowsiness dataset is used to test the model and compare it to several other current models as well as state-of-the-art models. The proposed model outperformed state-of-the-art models, with results up to 98.30% for training accuracy and 97.31% for validation accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A CNN-LSTM-based Deep Learning Approach for Driver Drowsiness Prediction

Gomaa

Mahmoud²,

Sarhan³

2022

Journal of Engineering Research

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“…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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“…Recently reported novel works for human action anticipation using the deep learning model in (Gite et al, 2019(Gite et al, , 2021Koppula and Saxena, 2013;Muhammad et al, 2020;Pirri et al, 2019). Anticipation methods will allow to build of proactive assistive systems and enhance the interactions between a driver and the context().…”

Section: Activity Anticipationmentioning

confidence: 99%

“…Advanced prediction of the activity of the driver can help ADAS systems at great instance rather than using multiple heavy sets of wearable to prevent accidents while driving (Jafaripournimchahi et al , 2020). Deep neural networks have driven the recent developments in anticipating driver’s activities. Activity anticipation: Recently reported novel works for human action anticipation using the deep learning model in (Gite et al , 2019, 2021; Koppula and Saxena, 2013; Muhammad et al , 2020; Pirri et al , 2019). Anticipation methods will allow to build of proactive assistive systems and enhance the interactions between a driver and the context().…”

Section: Advanced Techniques For Assistive Drivingmentioning

confidence: 99%

Context–aware assistive driving: an overview of techniques for mitigating the risks of driver in real-time driving environment

Gite

Kotecha

Ghinea

2021

IJPCC

Self Cite

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Purpose This study aims to analyze driver risks in the driving environment. A complete analysis of context aware assistive driving techniques. Context awareness in assistive driving by probabilistic modeling techniques. Advanced techniques using Spatio-temporal techniques, computer vision and deep learning techniques. Design/methodology/approach Autonomous vehicles have been aimed to increase driver safety by introducing vehicle control from the driver to Advanced Driver Assistance Systems (ADAS). The core objective of these systems is to cut down on road accidents by helping the user in various ways. Early anticipation of a particular action would give a prior benefit to the driver to successfully handle the dangers on the road. In this paper, the advancements that have taken place in the use of multi-modal machine learning for assistive driving systems are surveyed. The aim is to help elucidate the recent progress and techniques in the field while also identifying the scope for further research and improvement. The authors take an overview of context-aware driver assistance systems that alert drivers in case of maneuvers by taking advantage of multi-modal human processing to better safety and drivability. Findings There has been a huge improvement and investment in ADAS being a key concept for road safety. In such applications, data is processed and information is extracted from multiple data sources, thus requiring training of machine learning algorithms in a multi-modal style. The domain is fast gaining traction owing to its applications across multiple disciplines with crucial gains. Research limitations/implications The research is focused on deep learning and computer vision-based techniques to generate a context for assistive driving and it would definitely adopt by the ADAS manufacturers. Social implications As context-aware assistive driving would work in real-time and it would save the lives of many drivers, pedestrians. Originality/value This paper provides an understanding of context-aware deep learning frameworks for assistive driving. The research is mainly focused on deep learning and computer vision-based techniques to generate a context for assistive driving. It incorporates the latest state-of-the-art techniques using suitable driving context and the driver is alerted. Many automobile manufacturing companies and researchers would refer to this study for their enhancements.

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ADMT: Advanced Driver’s Movement Tracking System Using Spatio-Temporal Interest Points and Maneuver Anticipation Using Deep Neural Networks

Cited by 7 publications

References 42 publications

A CNN-LSTM-based Deep Learning Approach for Driver Drowsiness Prediction

A CNN-LSTM-based Deep Learning Approach for Driver Drowsiness Prediction

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

Context–aware assistive driving: an overview of techniques for mitigating the risks of driver in real-time driving environment

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