A Distance-based Data Fusion Technique for Minimizing GPS Positioning Error in Vehicular Ad Hoc Networks

Lobo, Felipe Leite; Grael, Danilo Carvalho; Oliveira, Horácio A. B. F.; Villas, Leandro A.; Almehmadi, Abdulaziz; El-Khatib, Khalil

doi:10.1145/3345837.3355956

Cited by 3 publications

(8 citation statements)

References 20 publications

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“…Also, we can notice in our previous work [13] that the vehicles farther away from the target can provide less accurate distance information than closer vehicles. The main idea in this work is to put more weight in the distance information given by neighbors closer to the target and less weight for the ones that are farther.…”

Section: Covalid Localizationmentioning

confidence: 98%

“…In this section, we describe the details of our proposed solution, the CoVaLID (Cooperative Vehicle Localization Improvement using Distance Information) algorithm which is an extension and improvement over our previously proposed BOuND algorithm [13]. We also explore and discuss some of its challenges and real-world implementation.…”

Section: Covalid Localizationmentioning

confidence: 99%

“…The concept of similarity of the triangles as demonstrated in our previous work [13], states that if two triangles share congruent angles, they are similar, as shown in Figure 1. Hence, the ratios of the corresponding sides of any two triangles are equivalent, no matter the hypotenuse length.…”

Section: Covalid Localizationmentioning

confidence: 99%

“…The CoVaLID localization technique is constituted of two equations, the same used in BOuND [13]. We can obtain these equations as follows:…”

Section: Covalid Localizationmentioning

confidence: 99%

“…In this work, we propose a novel location data fusion technique, called cooperative vehicle localization improvement using distance information (CoVaLID), that cooperatively gathers GPS and distance information from nearby vehicles to improve their locations. Our CoVaLID solution is an extension and improvement over our previously proposed BOuND algorithm [13]. In this current work, we are using a weighted average model in GPS positions to put more confidence in distance information provided by vehicles closer to the target.…”

Section: Introductionmentioning

confidence: 99%

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Cooperative Localization Improvement Using Distance Information in Vehicular Ad Hoc Networks

Lobo

Grael

Oliveira

et al. 2019

Sensors

Self Cite

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In vehicular ad hoc networks (VANets), a precise localization system is a crucial factor for several critical safety applications. The global positioning system (GPS) is commonly used to determine the vehicles’ position estimation. However, it has unwanted errors yet that can be worse in some areas, such as urban street canyons and indoor parking lots, making it inaccurate for most critical safety applications. In this work, we present a new position estimation method called cooperative vehicle localization improvement using distance information (CoVaLID), which improves GPS positions of nearby vehicles and minimize their errors through an extended Kalman filter to execute Data Fusion using GPS and distance information. Our solution also uses distance information to assess the position accuracy related to three different aspects: the number of vehicles, vehicle trajectory, and distance information error. For that purpose, we use a weighted average method to put more confidence in distance information given by neighbors closer to the target. We implement and evaluate the performance of CoVaLID using real-world data, as well as discuss the impact of different distance sensors in our proposed solution. Our results clearly show that CoVaLID is capable of reducing the GPS error by 63%, and 53% when compared to the state-of-the-art VANet location improve (VLOCI) algorithm.

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Section: Covalid Localizationmentioning

confidence: 98%

Section: Covalid Localizationmentioning

confidence: 99%

Section: Covalid Localizationmentioning

confidence: 99%

“…The CoVaLID localization technique is constituted of two equations, the same used in BOuND [13]. We can obtain these equations as follows:…”

Section: Covalid Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cooperative Localization Improvement Using Distance Information in Vehicular Ad Hoc Networks

Lobo

Grael

Oliveira

et al. 2019

Sensors

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GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles

Kamal

Barua

Vitale

et al. 2021

2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)

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Attacks on the GPS receiver of Connected and Autonomous Vehicles (CAV) and specifically GPS location spoofing is of great concern for the automotive industry as the attacker can compromise the security of CAVs leading to serious repercussions for the drivers and pedestrians. Attack detection solutions based on specialized hardware (e.g., antenna arrays) and satellite signal processing techniques are accurate, yet bulky and expensive to mount on CAVs. Thus, lightweight and cost-effective solutions for detecting location spoofing attacks are highly desirable. This work presents an in-vehicle attack detection solution that fuses multi-source data readily available from the CAV's onboard sensors. It can be implemented in software running on cheap embedded computing platforms integrated into the CAV. The proposed solution is validated using the real-time CARLA simulator, while extensive experimental results demonstrate its effectiveness under different attack scenarios.

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Mecanismo Eficiente de Localização Cooperativa para Veículos Autônomos Conectados

Lobato

Souza

Cerqueira³

et al. 2021

Anais Do XXXIX Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2021)

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A localização robusta e precisa é fundamental para garantir o funcionamento de aplicações de Veículos Autônomos Conectados (CAVs). No entanto, os mecanismos de navegação veicular (GNSS) são afetados por diversos problemas nas áreas urbanas, principalmente devido à refração ou reﬂexão dos sinais de satélite em prédios. Assim, para usar a localização do GNSS e satisfazer os requisitos das aplicações de CAVs, é necessário considerar técnicas de fusão de dados para obter um posicionamento mais eﬁciente por meio da troca de dados entre os CAVs. Neste cenário, este artigo apresenta um mecanismo de fusão de dados para localização veicular cooperativa, denominado de DUELAR. O mecanismo considera o Filtro de Kalman Sem Cheiro em conjunto com um algoritmo de map matching a nível de faixa para corrigir os erros de localização. Os resultados da simulação mostraram que o DUELAR reduz o erro de localização em pelo menos 70% comparado com outras abordagens da literatura.

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A Distance-based Data Fusion Technique for Minimizing GPS Positioning Error in Vehicular Ad Hoc Networks

Cited by 3 publications

References 20 publications

Cooperative Localization Improvement Using Distance Information in Vehicular Ad Hoc Networks

Cooperative Localization Improvement Using Distance Information in Vehicular Ad Hoc Networks

GPS Location Spoofing Attack Detection for Enhancing the Security of Autonomous Vehicles

Mecanismo Eficiente de Localização Cooperativa para Veículos Autônomos Conectados

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