Development of on-board unit in vehicular ad-hoc network for highways

Yang, Qiong; Wang, Lin; Xia, Weiwei; Wu, Yi; Shen, Lianfeng

doi:10.1109/iccve.2014.7297589

Cited by 16 publications

(7 citation statements)

References 7 publications

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“…VANETs are a network of intelligent interconnected vehicles and are composed of an Onboard Unit (OBU) and a stationary access point, termed roadside units (RSUs) [76]. OBU is a device fitted to each vehicle, which basically includes memory, a processing unit, a GPS receiver, and an antenna for short-range Internet connection [77].…”

Section: Full Automationmentioning

confidence: 99%

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Shokravi

Bakhary

et al. 2020

Sensors

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Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques.

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Section: Full Automationmentioning

confidence: 99%

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Shokravi

Bakhary

et al. 2020

Sensors

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“…The components of OBU typically consists of the GPS unit, human-machine interface unit, a wireless communication unit, and the central control unit. The central control unit is the main unit, consisting of data transceiver, serial port for information processing, memory, and decision and judgement making [28].…”

Section: A Physical Layermentioning

confidence: 99%

“…GPS signals received from the satellite are interpreted and filtered by the GPS unit in the OBU. The recorded data are then taken to calculate the location, speed and the rate of change of speed of vehicles [28]. e) Traffic Light Controller (TLC): A TLC in a CV environment acts as a device that dictates instructions and provides a set of rules for drivers to rely on, for the main purpose of avoiding collisions, giving directions, and giving warnings.…”

Section: A Physical Layermentioning

confidence: 99%

“…V2V communications in connected vehicle environment have contributed to providing important reassurance on the improvement of operational safety in vehicles such as collision warning, as vehicles can communicate actively within the area through message exchange, primarily for accident prevention and warnings [28], [39]. V2I is also an essential part of the CV communication for the road-side units (RSU) to provide continuous connection and communication in the network [40].…”

Section: B Network Layermentioning

confidence: 99%

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Conceptual Model for Connected Vehicles Safety and Security using Big Data Analytics

Razali¹,

Shamsaimon²,

Wook³

et al. 2020

IJACSA

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The capability of Connected Vehicles (CVs) connecting to a nearby vehicle, surrounding infrastructure and cyberspace presents a high risk in the aspect of safety and security of the CV and others. Data volume generated from the sensors and infrastructure in CVs environment are enormous. Thus, CVs implementations require a real-time big data processing and analytics to detect any anomaly in the CVs's environment which are physical layer, network layer and application layer. CVs are exposed to various vulnerabilities associated with exploitations or malfunctions of the components in each layer that could result in various safety and security event such as congestion and collision. The safety and security risks added an extra layer of required protection for the CVs implementation that need to be studied and refined. To address this gap, this research aims to determine the basic components of safety and security for CVs implementation and propose a conceptual model for safety and security in CVs by applying the machine learning and deep learning techniques. The proposed model is highly correlated to safety and security and could be applied in congestion and collision prediction.

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“…At the same time, VANET data is not only used for the reasonable setting of traffic light time but also used for the statistics of the number of vehicles on the road every day. In the VANET, on-board unit (OBU) [2] installed on the vehicle and road-side unit (RSU) [3] deployed on the roadside are two core components. Both OBU and RSU have wireless communication modules that support the wireless information transmission of vehicles when moving.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Differential Evolution with Discrete Elite Guide in Internet of Vehicles Roadside Unit Deployment

Wang

et al. 2021

Wireless Communications and Mobile Computing

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In a vehicular ad hoc network (VANET), roadside units (RSUs) are installed at roadside and intersections to process vehicle-to-infrastructure communication, collect and analyse intelligent vehicle traffic data, send information to vehicles, and achieve early warning of safe driving of vehicles. Owning to the high cost of implementing and maintaining RSUs, it is of vital importance to determine where and how many RSUs to deploy. Optimal RSU deployment requires both a small number of RSUs and the maximum coverage of vehicle running process, which constitutes a conflicting multiobjective problem. Nevertheless, existing works do not explicitly utilize multiobjective algorithm to solve the RSU deployment problem. Therefore, a multiobjective differential evolution approach is proposed in this work to solve the problem. Firstly, to conquer the complexity of urban road RSU deployment, the static model is established. Secondly, in the proposed multiobjective differential evolution with discrete elitist guide (MODE-deg), the sigmoid function is applied to discrete individual values. Finally, elitist individuals are selected based on crowding distance ranking and nondominated ranking to generate new individuals, which further improve the convergence speed and population performance. Experimental results show that MODE-deg can generate the optimal nondominant solution set with good convergence and diversity, in contrast to other multiobjective evolutionary algorithms in five test functions of ZDT.

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Development of on-board unit in vehicular ad-hoc network for highways

Cited by 16 publications

References 7 publications

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Conceptual Model for Connected Vehicles Safety and Security using Big Data Analytics

Multiobjective Differential Evolution with Discrete Elite Guide in Internet of Vehicles Roadside Unit Deployment

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