VANET simulators: an updated review

Weber, Julia Silva; Neves, Miguel; Ferreto, Tiago

doi:10.1186/s13173-021-00113-x

Cited by 66 publications

(26 citation statements)

References 110 publications

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“…The parameters used in the simulations and their corresponding values are presented in Table 4. The proposed method was tested based on the simulation results of Veins 23 .…”

Section: Results Andmentioning

confidence: 99%

An innovative traffic light recognition method using vehicular ad-hoc networks

Al-Ezaly

El-Bakry²,

Abo-Elfetoh³

et al. 2023

Sci Rep

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Car congestion is a pressing issue for everyone on the planet. Car congestion can be caused by accidents, traffic lights, rapid accelerations, deceleration, and hesitation of drivers, as well as a small low-carrying capacity road without bridges. Increasing road width and constructing roundabouts and bridges are solutions to car congestion, but the cost is significant. TLR (traffic light recognition) reduces accidents and traffic congestion caused by traffic lights (TLs). Image processing with convolutional neural network (CNN) lakes dealing with harsh weather. A semi-automatic annotation for traffic light detection employs a global navigation satellite system, raising the cost of automobiles. Data was not collected in harsh conditions, and tracking was not supported. Integrated channel feature tracking (ICFT) combines detection and tracking, but it does not support sharing information with neighbors. This study used vehicular ad-hoc networks (VANETs) for VANET traffic light recognition (VTLR). Information exchange as well as monitoring of the TL status, time remaining before a change, and recommended speeds are supported. Based on testing, it has been determined that VTLR performs better than semi-automatic annotation, image processing with CNN, and ICFT in terms of delay, success ratio, and the number of detections per second.

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“…The parameters used in the simulations and their corresponding values are presented in Table 4. The proposed method was tested based on the simulation results of Veins 23 .…”

Section: Results Andmentioning

confidence: 99%

An innovative traffic light recognition method using vehicular ad-hoc networks

Al-Ezaly

El-Bakry²,

Abo-Elfetoh³

et al. 2023

Sci Rep

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“…Modeling of the communication (e.g., V2V, V2I) in traffic simulation have commonly been done either by changes in the parameters or driving behavior models or by coupling a traffic simulation model with a wireless network simulator [e.g., 111], [ 112], [ 113]. An overview of simulators for vehicular ad-hoc networks (VANETs) is given in [111] and examples of simulation platforms that combines the wireless network and traffic simulators can be found in [112], [113].…”

Section: E Vehicle Connectivitymentioning

confidence: 99%

Modeling Automated Driving in Microscopic Traffic Simulations for Traffic Performance Evaluations: Aspects to Consider and State of the Practice

Farah

Postigo²,

Reddy

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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The gradual deployment of automated vehicles on the existing road network will lead to a long transition period in which vehicles at different driving automation levels and capabilities will share the road with human driven vehicles, resulting into what is known as mixed traffic. Whether our road infrastructure is ready to safely and efficiently accommodate this mixed traffic remains a knowledge gap. Microscopic traffic simulation provides a proactive approach for assessing these implications. However, differences in assumptions regarding modeling automated driving in current simulation studies, and the use of different terminology make it difficult to compare the results of these studies. Therefore, the aim of this study is to specify the aspects to consider for modeling automated driving in microscopic traffic simulations using harmonized concepts, to investigate how both empirical studies and microscopic traffic simulation studies on automated driving have considered the proposed aspects, and to identify the state of the practice and the research needs to further improve the modeling of automated driving. Six important aspects were identified: the role of authorities, the role of users, the vehicle system, the perception of surroundings based on the vehicle's sensors, the vehicle connectivity features, and the role of the infrastructure both physical and digital. The research gaps and research directions in relation to these aspects are identified and proposed, these might bring great benefits for the development of more accurate and realistic modeling of automated driving in microscopic traffic simulations.

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“…In the experiments, an event-based network simulator Netsim is used. Simulation of Urban Mobility (SUMO) bi-directionally coupled with Netsim as described in [25] is used to make the most realistic simulations. SUMO is a microscopic road traffic simulator that allows creating a scenario by converting an existing map or using one of the external tools provided by the SUMO project itself (for example, NETGEN or NETCONVERT) [26].…”

Section: Simulation Setupmentioning

confidence: 99%