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Author

Yu Wang

Bio: Yu Wang is an academic researcher from Hefei University of Technology. The author has contributed to research in topics: Vehicular ad hoc network & IEEE 802.11p. The author has an hindex of 2, co-authored 3 publications receiving 6 citations.

Papers
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Proceedings ArticleDOI
01 Mar 2017
TL;DR: Two novel vehicle collision warning algorithms are presented: rear-end collision warning and intersection collision warning, which are implemented in the developed prototype of VANET communication terminals and are effective and efficient.
Abstract: Vehicular Ad-Hoc Network (VANET) has attracted many attentions in recent years. In order to avoid vehicle collision, most of scholars put efforts towards the design of collision warning algorithms, which mainly rely on the Basic Safety Messages (BSMs) of Wireless Access in Vehicular Environment (WAVE) protocol. For an embedded application, the computational load of the algorithm, roads layout and the real-time data processing must be taken into consideration seriously. Therefore, we present two novel vehicle collision warning algorithms: rear-end collision warning and intersection collision warning, which are implemented in our developed prototype of VANET communication terminals. The main features of the algorithms presented in this paper are simplicity and effectiveness. The fundamental method of the two algorithms is to construct reasonable traffic models for collision detection. Simulations and experiments show that the proposed algorithms are effective and efficient.

4 citations

Proceedings ArticleDOI
01 Mar 2017
TL;DR: The proposed approach reduces synchronization error to that of less than 0.3 ms, which meets the accuracy requirement of VANET specification and can greatly reduce the frequency of handover performed by the high speed OBUs.
Abstract: Vehicular Ad hoc Network (VANET) is still a very attractive technology by which the construction of an Intelligent Transportation System (ITS) will be realized. The communication devices of VANET, i.e., OBUs and RSUs, are only allowed to transmit data in an assigned channel time and the channel switch will take place in about every 50 ms according to the standards of IEEE 802.11p and IEEE 1609.4. On the other hand, in the case of a large scale VANET, e.g., in the scenario of a traffic rush hour, available transmission time interval would be a few of milliseconds or even much less. Therefore, it is essential to achieve a precise time synchronization among the embedded devices. In this paper, we put forward a new specific time synchronization method among VANET devices. In our method, OBU can synchronize to other OBU or RSU initiatively. In the case that there is no center node in BSS (Basic Service Set), the proposed approach reduces synchronization error to that of less than 0.3 ms, which meets the accuracy requirement of VANET specification. In addition, we achieved time synchronization among RSUs. This can greatly reduce the frequency of handover performed by the high speed OBUs.

3 citations

Proceedings ArticleDOI
Yu Wang1, Zhizhong Ding1, Fei Li1, Xue Xia1, Zhentao Li1 
01 Mar 2017
TL;DR: The design and its implementation of such a program which includes the basic Intelligent Transportation System (ITS) functionality, especially the two safety sub-applications: Emergency Electronic Brake Lights and Intersection Collision Warning are presented.
Abstract: The Wireless Access in Vehicular Environment (WAVE) protocol is capable of providing safety, seamless and effective communication for the Vehicular Ad-hoc Network (VANET). Although some companies have launched the products of PHY and MAC layer modules, there is no mature system-level WAVE product. Obviously, a well-performed application layer program design complying with the WAVE protocol is essential to the low-latency and low-overhead VANET. In this paper, we present the design and its implementation of such a program which includes the basic Intelligent Transportation System (ITS) functionality, especially the two safety sub-applications: Emergency Electronic Brake Lights and Intersection Collision Warning. In order to run the program in a traffic scenario, we also develop a traffic simulator. The conducted tests show that the developed application provides efficient communication and performs well.

2 citations


Cited by
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Journal ArticleDOI
TL;DR: The current research status of safety warnings under connected vehicle environments to some extent is summarized, which can provide references for future safety warning research in terms of framework, methods and technologies, etc.
Abstract: According to many studies, motor vehicle collisions lead to many deaths and disabilities, and bring about huge economic losses to societies and individuals around the world. If risky scenarios could be recognized and the driver could be timely warned, the frequency of traffic accidents could be reduced effectively, and traffic safety could be improved. This article summarizes the current state of the research on safety warnings under connected vehicle environments-from risk recognition algorithms, collision avoidance systems, dangerous event identification and notification methods, real-time safety warning system and effects of various types of warning information notifications. At the end of this article, the conclusions of this work are presented, and possible future directions for safety warning research under connected vehicle environments are discussed. This article represents the current research status of safety warnings under connected vehicle environments to some extent, which can provide references for future safety warning research in terms of framework, methods and technologies, etc.

16 citations

Journal ArticleDOI
TL;DR: In this paper , a comprehensive survey of intersection management methods for heterogeneous connected vehicles (CVs) is presented, where the authors consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles.
Abstract: Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy.

14 citations

Journal ArticleDOI
TL;DR: The key role of real-time traffic signal control technology in managing congestion at road junctions within smart cities is explored and the benefits of synchronizing the traffic signals on various busy routes for the smooth flow of traffic at intersections are examined.
Abstract: The effective control and management of traffic at intersections is a challenging issue in the transportation system. Various traffic signal management systems have been developed to improve the real-time traffic flow at junctions, but none of them have resulted in a smooth and continuous traffic flow for dealing with congestion at road intersections. Notwithstanding, the procedure of synchronizing traffic signals at nearby intersections is complicated due to numerous borders. In traditional systems, the direction of movement of vehicles, the variation in automobile traffic over time, accidents, the passing of emergency vehicles, and pedestrian crossings are not considered. Therefore, synchronizing the signals over the specific route cannot be addressed. This article explores the key role of real-time traffic signal control (TSC) technology in managing congestion at road junctions within smart cities. In addition, this article provides an insightful discussion on several traffic light synchronization research papers to highlight the practicability of networking of traffic signals of an area. It examines the benefits of synchronizing the traffic signals on various busy routes for the smooth flow of traffic at intersections.

12 citations

Journal ArticleDOI
TL;DR: An intelligent channel access algorithm empowered by cooperative Reinforcement Learning (RL), in which vehicles coordinate the channel access in a fully-decentralized manner is proposed, which satisfies the low latency requirement of VANET safety applications as well as both short-term and long-term communication fairness.
Abstract: Vehicular Ad-hoc Network (VANET) is an emerging technique dedicated to wireless vehicular communication to improve transportation safety by exchanging driving information between vehicles. For safety purposes, vehicles periodically broadcast a safety packet via Vehicle-to-Vehicle (V2V) communication. Accordingly, VANET safety applications demand a reliable exchange of the safety packet with high Packet Delivery Ratio (PDR), acceptable latency, and communication fairness. However, the communication performance significantly degrades due to numerous packet collisions when a large number of vehicles simultaneously access limited channel resources for the safety broadcast. In particular, the problem grows more severe in congested VANETs absent infrastructures since vehicles must control channel access using a self-adaptive scheme without external assistance. Thus, a robust and decentralized channel access protocol for VANETs is required to achieve road safety. In this paper, we propose an intelligent channel access algorithm empowered by cooperative Reinforcement Learning (RL), in which vehicles coordinate the channel access in a fully-decentralized manner. We also consider a proper interaction scheme between vehicles for enhancing the V2V safety broadcast in infrastructure-less congested VANETs. We provide evaluation results with extensive simulations according to various levels of traffic congestion. Simulations confirm the superior performance of the algorithm: the algorithm has a 20% increase in PDR compared to the latest RL-based channel access scheme. Furthermore, the algorithm satisfies the low latency requirement of VANET safety applications as well as both short-term and long-term communication fairness.

11 citations

Book ChapterDOI
TL;DR: In this article , the authors proposed a machine learning-based VANET implementation using simulated data that is collected and based on implementation is done through a random forest classifier, which can provide a splendid set of tools for handling data.
Abstract: A vehicular ad hoc network (VANET) can help in reducing accidents by sending safety messages to the vehicles. High mobility and high dynamics of vehicles give rise to many challenges in VANET. Machine learning is a technique of artificial intelligence that can provide a splendid set of tools for handling data. A concise introduction of the significant concepts of machine learning and VANET. Our main concern is to implement VANET using different machine learning techniques. The proposed scheme uses simulated data that is collected and based on implementation is done through a random forest classifier.

2 citations