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Author

Zhentao Li

Bio: Zhentao Li 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 1, co-authored 2 publications receiving 3 citations.

Papers
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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 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

Proceedings ArticleDOI
16 Oct 2020
TL;DR: This paper proposes a prediction method by probabilistic modelling that combines two aspects lane change evidence, and shows that the algorithm performs well in predicting accuracy and reducing false alarms.
Abstract: According to the previous research, lane-changes are a major cause of serious traffic accidents. Thus, it is essential to build an efficient prediction algorithm for vehicles lane change on Advanced Driving Assistance System (ADAS) to ensure a safe and comfort driving for host vehicle. At present, many methods for lane change prediction have been proposed. However, most of them require a lot of data training to have a good prediction performance. Considering the practical applicability of the prediction algorithm in ADAS, this paper proposes a prediction method by probabilistic modelling. This method combines two aspects lane change evidence. On the one hand, model the lane change probability caused by the target vehicle's driving context. On the other hand, model the lane change probability reflected by vehicle posture in the road. The evaluation of the whole algorithms was done by using simulation data and real lane change data. The results show that the algorithm performs well in predicting accuracy and reducing false alarms.

1 citations

Journal ArticleDOI
TL;DR: Simulation results obtained show that Sync-free RadChat is able to mitigate interference without degrading the radar performance, and relies on using the relative time for radar coordination, eliminating the dependency on the absolute time provided by GPS.
Abstract: Automotive radar interference mitigation is expected to be inherent in all future ADAS and AD vehicles. Joint radar communications is a candidate technology for removing this interference by coordinating radar sensing through communication. Coordination of radars requires strict time synchronization among vehicles, and our formerly proposed protocol (RadChat) achieves this by a precise absolute time, provided by GPS clocks of vehicles. However, interference might appear if synchronization among vehicles is lost in case GPS is spoofed, satellites are blocked over short intervals, or GPS is restarted/updated. Here we present a synchronization-free version of RadChat (Sync-free RadChat), which relies on using the relative time for radar coordination, eliminating the dependency on the absolute time provided by GPS. Simulation results obtained for various use cases show that Sync-free RadChat is able to mitigate interference without degrading the radar performance.

1 citations