A V2P Collision Risk Warning Method based on LSTM in IOV

Pan, Ruoyu; Jie, Lihua; Zhang, Xinyue; Shengli, Pang; Wang, Honggang; Wei, Zhaoying

doi:10.1155/2022/7507573

Cited by 8 publications

(4 citation statements)

References 36 publications

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“…Pan et al [37] develop a LoRa-based V2P communication system for early collision warning. The proposed V2P approach demonstrates effective collision risk alert strategy, contributing to pedestrian safety.…”

Section: Optimizationmentioning

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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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: Optimizationmentioning

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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“…The suggested approach makes use of geocoding and named entity recognition to address problems with informal language and misspellings. Last but not least, Pan et al [67] proposed an early warning system and communication network architecture for Vehicle to Pedestrian (V2P) using LoRa technology. When pedestrian trajectories are unclear, the system uses LSTM and ANN to forecast the area of risk for a collision between a pedestrian and a vehicle.…”

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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“…Lastly, Pan et al [68] presented a communication network architecture and early warning system for Vehicle-to-Pedestrian (V2P) using LoRa technology. The system uses LSTM and ANN to predict the area of risk for pedestrian-vehicle collision in situations where pedestrian trajectories are uncertain.…”

Section: ) Machine Learning Based Collision Scope Predictionmentioning

confidence: 99%

“…Extracting data related to traffic accidents from Twitter Twitter has the potential to furnish information regarding traffic accidents, with commercial and industrial areas of the city being impacted by Twitter Pan et al [68] Communication network architecture using LoRa technology and LSTM/ANN…”

Section: Named Entity Recognition and Geocodingmentioning

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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A V2P Collision Risk Warning Method based on LSTM in IOV

Cited by 8 publications

References 36 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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