Vehicle localization in VANETs using data fusion and V2V communication

Golestan, Keyvan; Seifzadeh, Sepideh; Kamel, Mohamed S.; Karray, Fakhri; Sattar, F.

doi:10.1145/2386958.2386977

Cited by 48 publications

(14 citation statements)

References 14 publications

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“…This shows the potential improvement in accuracy from the use of stationary vehicles in V2V systems. Furthermore, in [56] the authors used multiple GPS position estimates from other vehicles and ranging estimates using TOA and AOA measurements to improve localisation accuracy. Also, the proposed approach used vehicle state information from a gas pedal, brake pedal, and steering wheel sensors.…”

Section: Cooperative Localisation Techniquesmentioning

confidence: 99%

A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications

Kuutti

Fallah

Κατσαρός

et al. 2018

IEEE Internet Things J.

640

284

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For an autonomous vehicle to operate safely and effectively, an accurate and robust localisation system is essential. While there are a variety of vehicle localisation techniques in literature, there is a lack of effort in comparing these techniques and identifying their potentials and limitations for autonomous vehicle applications. Hence, this paper evaluates the state-of-the-art vehicle localisation techniques and investigates their applicability on autonomous vehicles. The analysis starts with discussing the techniques which merely use the information obtained from on-board vehicle sensors. It is shown that although some techniques can achieve the accuracy required for autonomous driving but suffer from the high cost of the sensors and also sensor performance limitations in different driving scenarios (e.g. cornering, intersections) and different environmental conditions (e.g. darkness, snow). The paper continues the analysis with considering the techniques which benefit from off-board information obtained from V2X communication channels, in addition to vehicle sensory information. The analysis shows that augmenting off-board information to sensory information has potential to design low-cost localisation systems with high accuracy and robustness however their performance depends on penetration rate of nearby connected vehicles or infrastructure and the quality of network service.

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Section: Cooperative Localisation Techniquesmentioning

confidence: 99%

A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications

Kuutti

Fallah

Κατσαρός

et al. 2018

IEEE Internet Things J.

640

284

View full text Add to dashboard Cite

show abstract

“…This path loss model varies from one environment to another and requires burden calibration. In another study, time of arrival (ToA) was used in [12] to estimate the inter-vehicle distance and then fused with kinematic model through extended Kalman filter.…”

Section: A Related Workmentioning

confidence: 99%

Integrated Cooperative Localization for Connected Vehicles in Urban Canyons

Elazab¹,

Noureldin²,

Hassanein³

2014

2015 IEEE Global Communications Conference (GLOBECOM)

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The goal to achieve accurate and ubiquitous localization is the driving force for location based services in vehicular ad hoc networks (VANETs). In urban areas, global positioning system (GPS) and in-vehicle navigation sensors (e.g. odometers) suffer from prolonged outages and unsustainable error accumulation, respectively. The need for precise vehicle localization remains paramount, and cooperative vehicle localization based on ranging techniques are being exploited to this end. This paper presents a novel cooperative localization scheme that utilizes round trip time (RTT) for inter-vehicle distance calculation, integrated with inertial sensor measurements to update the position of not only the vehicle to be localized, but its neighbors as well. We adopted the extended Kalman filter (EKF), to limit the effect of errors in both the sensors and the neighbors' positions, in computing the new location. In comparison to the existing cooperative localization techniques, our proposed cooperative scheme does not depend on GPS updates for the neighbors' positions thus making it far more suitable in urban canyons and tunnels. In addition, our scheme considers updating the neighbors' positions using their current inertial sensor measurements resulting in; better position estimation. The scheme is implemented and tested using the network simulator 3 (ns-3), vehicle traces are generated using SUMO and error models are introduced to the sensors and initial positions for different velocities and densities. Results show that our scheme outperforms the inertial navigation systems (INS) technology typically used in environments where GPS fails.

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“…In addition, data structure representation of road map depend on digital map provider and a set of standards it supports. Another work to improve the position estimates by GPS provided by [8]. They used Particle Filter and Extended Kalman Filter to predict the location of vehicles, and refining it using the information received from other vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed relative cooperative positioning in Vehicular Ad-Hoc Networks

Alotaibi

Boukerche

Mouftah

2014

2014 Global Information Infrastructure and Networking Symposium (GIIS)

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we propose a distributed Relative Cooperative Positioning algorithm (ReCoP) that computes the position in relation to other vehicles in the network. ReCoP does not depend on fixed reference node/s or infrastructure, and it is independent of Global Navigation Satellite System (GNSS) reading. Instead one node in each group of one-hop connected neighbours establishes relative map for group members. Then when messages (e.g., warning messages) arrive to the vehicle with gateway responsibility, the coordinates transform to be recognized by different relative maps. This approach can be utilized in data dissemination to reduce broadcast storm problem due to redundant retransmissions. It has been compared with the Local Self Positioning (LSP) in which each vehicle individually builds its own local relative map. The performance of the proposed algorithm, ReCoP, has been evaluated with respect to different traffic density, transmission range, and speed. The simulation results illustrate that ReCoP computes the relative position with more precision than LSP. It also demonstrates the scalability, robustness, and flexibility of the proposed ReCoP compared to LSP.

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Vehicle localization in VANETs using data fusion and V2V communication

Cited by 48 publications

References 14 publications

A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications

A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications

Integrated Cooperative Localization for Connected Vehicles in Urban Canyons

Distributed relative cooperative positioning in Vehicular Ad-Hoc Networks

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