Prediction of Collision Avoidance Ability of Two-wheeled Vehicle Riders Using Driving Behaviors and Emotional States

Lee, Joohyeong; Kishino, Saya; Suzuki, Keisuke

doi:10.20485/jsaeijae.12.2_32

Cited by 6 publications

(5 citation statements)

References 29 publications

(29 reference statements)

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“…Lee et al [19] assess driving behaviors using a dynamic riding simulator and develop a model for collision avoidance. They quantify driving behaviors based on lateral control, head motion, and mental state.…”

Section: Featuresmentioning

confidence: 99%

“…[18] Low-cost driver profiling ML algorithm investigation Simulation limitations, data validation Lee, J. [19] Assessing driving behaviors Utilization of sensor data Small trial, context validation needed C. C. Chang [20] Collision avoidance using dynamics Real-time object detection Single camera setup, broader validation Moses and Parvathi [21] ML-based traffic flow prediction ML process, stages breakdown Limited to US data, no new methodology Lyu et al [22] Improved collision alerts Novel model, simulation validation Specific scenario, broader context needed Watanabe et al [23] Identifying dangerous roadplaces Sensory data, ML application Real-world validation, generalization Silva et al [24] Various ML methods for collision Prediction overview, evaluation Data precision, lack of real-time data AbouElassad et al [25] Framework for analyzing behavior…”

Section: Algorithms Accuracy Measurementmentioning

confidence: 99%

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Advancements in Machine Learning and Data Mining Techniques for Collision Prediction and Hazard Detection in Internet of Vehicles

Manchala,

Kishore

2024

PJBAS

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The Internet of Vehicles (IoV) has transfigured transportation with connected vehicles, smart infrastructure, and self-driving cars. Road collisions and accidents are still a problem for road safety. This review of the literature discusses the prediction of IoV accidents and collisions as well as the detection of hazards using data mining, deep learning, and machine learning techniques. It describes the most recent developments to these methods and how they enhanced IoV safety. The article starts off by going over data collection, data quality, and the ever-changing nature of IoV traffic scenarios. What follows is a detailed breakdown of the ML, DL, and DM methods used in IoV safety applications. Convolutional neural networks, artificial neural networks, recurrent neural networks, support vector machines, and decision trees. As examples of real-world applications and case studies, intelligent accident prediction models, driver attention forecasting, traffic congestion forecasting, spatiotemporal analysis in autonomous vehicles, scene-graph embedding, and V2P collision risk alerts are discussed. The goal of this review is to give readers a comprehensive overview of the cutting-edge methods enhancing IoV accident prediction, collision avoidance, and hazard detection.

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Section: Featuresmentioning

confidence: 99%

Section: Algorithms Accuracy Measurementmentioning

confidence: 99%

Advancements in Machine Learning and Data Mining Techniques for Collision Prediction and Hazard Detection in Internet of Vehicles

Manchala,

Kishore

2024

PJBAS

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“…Their method performed remarkably well on three different datasets. A model of driving behavior was developed by Lee et al [55] with the goal of predicting how riders of two-wheeled vehicles would avoid hazardous collisions. Their theoretical framework provides a predictive model for motorcyclists who participate in risky behavior to avoid collisions.…”

Section: Table 2: Summary Of the Reviewed Articles Pertaining To Coll...mentioning

confidence: 99%

A Comprehensive Review of the Literature on Machine Learning-Based Road Safety Prediction Techniques for Internet of Vehicles (IoV)-Enabled Vehicles

Munish Kumar Tiwari

2023

tjjpt

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The goal of this comprehensive assessment of the literature is to ascertain the state-of-the-art in machine learning (ML)-based vehicle safety measure prediction, obstruction estimate, and road traffic analysis the vehicle-related internet (IoV). In particular, we concentrate on confirming the necessity and extent of federated studying in this area. A decentralized machine learning method called federated learning enables numerous edge devices to train a shared model together while storing the data locally. We looked through numerous scholarly databases and a few chosen peer-reviewed papers on the subject. The paper outlines the benefits and future prospects of federated learning in road traffic analysis and vehicle safety while highlighting the current drawbacks and restrictions of the conventional centralized ML systems. In addition, we examined the state of the art studies in federated learning for road traffic analysis at the time and noted any knowledge gaps as well as potential future study areas. The results of this paper show the extent to which federated learning is required in the subject of road traffic analysis and vehicle safety, as well as how it can be used to get around some of the drawbacks of centralized machine learning techniques.

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“…Their approach demonstrated exceptional performance on three distinct datasets. Lee et al [56] formulated a driving behaviour model aimed at forecasting the hazardous collision avoidance of riders of two-wheeled vehicles. Their theoretical framework offers a predictive model for the avoidance of collisions by riders who engage in risky behaviour.…”

Section: ) Machine Learning Based Collision Scope Predictionmentioning

confidence: 99%

Machine Learning-Based Road Safety Prediction Strategies for Internet of Vehicles (IoV) Enabled Vehicles: A Systematic Literature Review

Reddy,

Muralidhar

2023

IEEE Access

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This systematic literature review aims to investigate the current state-of-the-art in machine learning (ML) based road traffic analysis, hindrance estimation, and predicting vehicle safety measures for the Internet of Vehicles (IoV). Specifically, we focus on the verification of the scope and need of federated learning in this field. Federated learning is a decentralized ML technique that allows multiple edge devices to collaboratively train a shared model while keeping the data locally. We searched various academic databases and selected peer-reviewed publications related to the topic. The review highlights the existing challenges and limitations of the traditional centralized ML approaches and presents the advantages and potential benefits of federated learning in road traffic analysis and vehicle safety. Furthermore, we analyzed the current state-of-the-art research in federated learning for road traffic analysis and identified the research gaps and future research directions. The findings of this review demonstrate the scope and need of federated learning in the field of road traffic analysis and vehicle safety, as well as the potential of federated learning to overcome the limitations of the centralized ML approaches.INDEX TERMS Internet of Vehicles, driving behavior analysis, machine learning, collision prediction, traffic accidents, deep learning, and risk assessment.

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Prediction of Collision Avoidance Ability of Two-wheeled Vehicle Riders Using Driving Behaviors and Emotional States

Cited by 6 publications

References 29 publications

Advancements in Machine Learning and Data Mining Techniques for Collision Prediction and Hazard Detection in Internet of Vehicles

Advancements in Machine Learning and Data Mining Techniques for Collision Prediction and Hazard Detection in Internet of Vehicles

A Comprehensive Review of the Literature on Machine Learning-Based Road Safety Prediction Techniques for Internet of Vehicles (IoV)-Enabled Vehicles

Machine Learning-Based Road Safety Prediction Strategies for Internet of Vehicles (IoV) Enabled Vehicles: A Systematic Literature Review

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