Cooperative Vehicle Position Estimation

Parker, Ryan A.; Valaee, Shahrokh

doi:10.1109/icc.2007.967

Cited by 55 publications

(32 citation statements)

References 10 publications

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“…We assume that all vehicles have initial positions obtained either from GPS or any other localization system as in [11]. Then, these vehicles travel in urban areas where GPS is unavailable due to excessive multipath from large buildings or complete blockage as in tunnels.…”

Section: A Preliminariesmentioning

confidence: 99%

“…On the other hand, cooperative GPS-free localization in [11, 12, and 13] are applicable to our considered scenario. In [11], signal strength is used to estimate the distance between vehicles. Then, extended Kaman filter uses the measured distance in addition to the kinematic motion model and road map to update the current vehicle's position.…”

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: A Preliminariesmentioning

confidence: 99%

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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“…al. [15] uses signal strength of DSRC to calculate the inter node distance. It then performs CL by fusing inter node distance, road maps and vehicle kinematics using Extended Kalman Filter.…”

Section: Introductionmentioning

confidence: 99%

Graph based cooperative localization using symmetric measurement equations and dedicated short range communication

Gulati

Zhang

Clarke³

et al. 2017

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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Abstract-Safety is one of the critical challenges for a semi or fully automated driving assistance systems. One of the key parameters for a safe automated driving assistance system is precise localization of the self and surrounding vehicles. Our previous work demonstrated the use of Symmetric Measurement Equations (SME) in a Factor Graph framework and exploited the use of sensor located outside the vehicle. In this paper we present a new approach which not only performs above mentioned cooperative vehicle infrastructure localization but also uses Dedicated Short Range Communication (DSRC). This work goes further in the direction of a complete V2X solution not only involving the infrastructure sensor but also the neighbouring vehicles. DSRC has been increasingly incorporated in all the the modern vehicles. Better state estimation is achieved by formulating the range information from DSRC as a new DSRC Range Factor in the Factor Graph. Simulations indicate better performance over the previously proposed approach of only using SME in the Factor Graph, thereby progressing a step further towards safe automated driving assistance systems.

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“…The problem of cooperative localization has been approached from various perspectives such as Extended Kalman Filter [5], [3] or ad-hoc trilateration [16]. The system uses signal strength based inter-vehicle distance measurements, road maps, vehicle kinematics, and Extended Kalman Filtering to estimate relative positions of vehicles in a cluster.…”

Section: B Multi-agent Vehicular Simulationmentioning

confidence: 99%