Vulnerable Road Users Detection Using V2X Communications

Anaya, J.J.; Talavera, Edgar; Gimenez, David; Gómez, Nuria; Jiménez, Felipe; Naranjo, José Eugenio

doi:10.1109/itsc.2015.26

Cited by 49 publications

(33 citation statements)

References 6 publications

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“…Once fully deployed, it can prevent thousands of vehicles from the threat of car accidents [56]. According to the 2020 framework, the European Commission plans to use V2X communication as a mandatory device for new vehicles in the short term [57]. The introduction of car-to-vehicle communication can exchange their own driving and road information, and achieve safer and more efficient automatic driving through mutual cooperation.…”

Section: A Research On Traffic Conflict Based On V2v Communicationmentioning

confidence: 99%

A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

Huang

et al. 2020

IEEE Access

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The traffic situation at an urban intersection is complicated, due to the numerous internal conflicts and all kinds of traffic accidents. With the objective of improving road safety, the automotive industry is moving toward intelligent vehicles. The major challenges are accurately perceiving the road traffic environment, detecting the potential traffic conflicts, and proposing the alternative driving strategies. This paper summarizes the existing researches on traffic conflicts from the perspective of intelligent vehicles. The intelligent vehicles can perceive the surrounding environment, extract road condition information, and detect obstacles for avoiding collisions or mitigating accidents. It expounds that the perception technology of intelligent vehicles can be divided into three main categories, namely, the perception technology, the communication technology, and the fusion of perception-communication technology. At the same time, the existing technical problems are analyzed. Finally, future development trends in this field are discussed. INDEX TERMS Intelligent vehicles, traffic conflicts, V2X communications, environment perception. I. INTRODUCTION

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Section: A Research On Traffic Conflict Based On V2v Communicationmentioning

confidence: 99%

A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

Huang

et al. 2020

IEEE Access

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“…Even when in groups, they may travel in a line following each other. Multiple efforts have been made to design V2P crash prevention systems for cyclists [17,22,27,35,36,38,39,40].…”

Section: Classificationmentioning

confidence: 99%

Vehicle-to-Pedestrian Communication for Vulnerable Road Users: Survey, Design Considerations, and Challenges

Sewalkar

Seitz

2019

Sensors

109

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In the last few years, increasing attention has been provided to research Vehicle-to-Pedestrian (V2P) communication systems. These V2P systems serve different purposes (safety or convenience) and cater to different Vulnerable Road User (VRU) groups. Also, these V2P systems employ different communication technologies, and use different mechanisms to interact with the users. An effective V2P system also needs to consider varying characteristics of different VRUs. These various elements may be considered as design parameters of the V2P system. In this paper, we discuss such elements and propose a design framework for the V2P system based on them. We also provide an extensive survey of existing V2P efforts for safety and convenience applications and their design considerations. We perform a case study that compares the different approaches of V2P safety system for different VRU groups under different pre-crash scenarios. Finally, we discuss a few technological challenges in integration of VRUs into V2X systems.

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“…In addition, a 3D photonic mixer camera was developed to provide pedestrian identification using a sensor device to meet a unique criteria for pedestrian safety in [22]. Anaya et al [5] have used V2V communications to develop a novel Advanced Driver Assistance Program to prevent collisions between motorcyclists and bikes.…”

Section: Related Workmentioning

confidence: 99%

“…Taking advantage of the new technology and available big datasets can create a fully functional, instant-time, precise, and secure transport facilities [2,4]. While C-ITS is now taking attention globally, the academic community focused primarily on motorized road users such as motor vehicles, expressing less interest for non-motorized road users [5]. Major planning challenges faced by deploying AV is intermodal traffic control, where AV regulations and programming should be structured to value human life by reducing the likelihood of crashes and protecting non-motorized road users [6,7].…”

Section: Introductionmentioning

confidence: 99%

Vulnerable Road Users Classification of Smartphone Sensors Data Using Deep Transfer Learning

Elhenawy¹,

Ashqar²,

Masoud³

et al. 2020

Preprint

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As the Autonomous Vehicle (AV) industry is rapidly advancing, classification of non-motorized (vulnerable) road users (VRUs) becomes essential to ensure their safety and to smooth operation of road applications. The typical practice of non-motorized road users’ classification usually takes numerous training time and ignores the temporal evolution and behavior of the signal. In this research effort, we attempt to detect VRUs with high accuracy be proposing a novel framework that includes using Deep Transfer Learning, which saves training time and cost, to classify images constructed from Recurrence Quantification Analysis (RQA) that reflect the temporal dynamics and behavior of the signal. Recurrence Plots (RPs) were constructed from low-power smartphone sensors without using GPS data. The resulted RPs were used as inputs for different pre-trained Convolutional Neural Network (CNN) classifiers including constructing 227×227 images to be used for AlexNet and SqueezeNet; and constructing 224×224 images to be used for VGG16 and VGG19. Results show that the classification accuracy of Convolutional Neural Network Transfer Learning (CNN-TL) reaches 98.70%, 98.62%, 98.71%, and 98.71% for AlexNet, SqueezeNet, VGG16, and VGG19, respectively. The results of the proposed framework outperform other results in the literature (to the best of our knowledge) and show that using CNN-TL is promising for VRUs classification. Because of its relative straightforwardness, ability to be generalized and transferred, and potential high accuracy, we anticipate that this framework might be able to solve various problems related to signal classification.

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Vulnerable Road Users Detection Using V2X Communications

Cited by 49 publications

References 6 publications

A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

Vehicle-to-Pedestrian Communication for Vulnerable Road Users: Survey, Design Considerations, and Challenges

Vulnerable Road Users Classification of Smartphone Sensors Data Using Deep Transfer Learning

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