A Qualitative Analysis of Vehicle Positioning Requirements for Connected Vehicle Applications

Williams, Nigel; Barth, Matthew

doi:10.1109/mits.2019.2953521

Cited by 16 publications

(8 citation statements)

References 36 publications

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“…Shieh et al [78] propose a vehicle positioning method that uses two one-dimensional signal-direction discriminators mounted on a vehicle to calculate the relative position of another vehicle by measuring the coming directions of the signal emitted from the vehicle. A recent analysis on vehicle positioning requirements for vehicular IoT applications is conducted by Williams and Barth [79].…”

Section: ) Perception Layermentioning

confidence: 99%

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Yoshinaga

et al. 2020

IEEE Open J. Comput. Soc.

286

106

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Section: ) Perception Layermentioning

confidence: 99%

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Yoshinaga

et al. 2020

IEEE Open J. Comput. Soc.

286

106

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“…This section focuses on the use of SSR data to compute the common mode error terms in the second line of Eqn. (3). These calculations will depend on the given base position P b which is considered as known to the server and the satellite position Ps (t s ) which is computed as described in Section III.…”

Section: Common-mode Error Calculationsmentioning

confidence: 99%

“…US-TEC provides VTEC values in a uniform grid of point locations with 1 degree resolution in latitude and longitude. The official website [51] states that the normal update interval is 15 minutes, typically with 28 minutes latency 3 .…”

Section: Ssr Vtec-shmentioning

confidence: 99%

“…This OS accuracy is not sufficient to satisfy the requirements of many Intelligent Transportation System (ITS) and Connected and Autonomous Vehicle (CAV) applications. Several references state position accuracy specifications, for instance: the Society of Automotive Engineering (SAE) specification requires 68% of horizontal error to be less than 1.5 m and vertical error to be less than 3 m [1]; and, lane-level positioning accuracy for ITS applications require 95% of horizontal error to be less than 1 m [2], [3]. Mitigation of common-mode GNSS measurement errors (e.g., satellite orbit and clock errors, hardware bias, atmospheric delay) is required to improve position accuracy in real-time navigation to achieve these specifications.…”

mentioning

confidence: 99%

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Using PPP Information to Implement a Global Real-Time Virtual Network DGNSS Approach

Wang

Neupane

Farrell

2022

IEEE Trans. Veh. Technol.

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Differential GNSS (DGNSS) has been demonstrated to provide reliable, high-quality range correction information enabling real-time navigation with centimeter to sub-meter accuracy, which is required for applications such as connected and autonomous vehicles. However, DGNSS requires a local reference station near each user. For a continental or global scale implementation, this information dissemination approach would require a dense network of reference stations whose construction and maintenance would be prohibitively expensive. Precise Point Positioning (PPP) affords more flexibility as a public service for Global Navigation Satellite Systems (GNSS) receivers, but its State Space Representation (SSR) format is not supported by most receivers in the field or on the market.This article proposes a novel Virtual Network DGNSS (VN-DGNSS) approach and an optimization algorithm that is key to its implementation. The approach capitalizes on the existing PPP infrastructure without the need for new physical reference stations. Specifically, no reference station is needed in the local vicinity of any user. By connecting to public GNSS SSR data services, a VN-DGNSS server maintains current information about satellite code bias, satellite orbit and clock, and atmospheric models. Construction of the Radio Technical Commission for Maritime Services (RTCM) Observation Space Representation (OSR) messages from this SSR information requires both the signal time-of-transmission and the satellite position at that time which are consistent with the time-of-reception for each client. This article presents an algorithm to determine these quantities. Then the VN-DGNSS computes and transmits RTCM OSR messages to user receivers. This approach achieves global dissemination coverage for real-time navigation without the need for additional local base stations near each user.The results of real-time stationary and moving platform evaluations are included, using u-blox M8P and ZED-F9P receivers. The performance surpasses the Society of Automotive Engineering (SAE) specification (68% of horizontal error ⩽ 1.5 m and vertical error ⩽ 3 m) and shows significantly better horizontal performance than GNSS Open Service (OS). The moving tests also show better horizontal performance than the ZED-F9P receiver with Satellite Based Augmentation Systems (SBAS) enabled and achieve the lane-level accuracy (95% of horizontal errors less than 1 meter).

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“…Shieh et al [ 26 ] proposed an approach to use relative positions of vehicles calculated by measuring the coming directions of wireless signals based on two one-dimensional signal-direction discriminators. Williams and Barth [ 27 ] discussed the application requirements of vehicle positioning in vehicular environments.…”

Section: Blockchain and The Vehicular Internet Of Thingsmentioning

confidence: 99%

Blockchain for Vehicular Internet of Things: Recent Advances and Open Issues

Peng

Gao

et al. 2020

Sensors

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The vehicular Internet of Things (IoT) comprises enabling technologies for a large number of important applications including collaborative autonomous driving and advanced transportation systems. Due to the mobility of vehicles, strict application requirements, and limited communication resources, the conventional centralized control fails to provide sufficient quality of service for connected vehicles, so a decentralized approach is required in the vicinity to satisfy the requirements of delay-sensitive and mission-critical applications. A decentralized system is also more resistant to the single point of failure problem and malicious attacks. Blockchain technology has been attracting great interest due to its capability of achieving a decentralized, transparent, and tamper-resistant system. There are many studies focusing on the use of blockchain in managing data and transactions in vehicular environments. However, the application of blockchain in vehicular environments also faces some technical challenges. In this paper, we first explain the fundamentals of blockchain and vehicular IoT. Then, we conduct a literature review on the existing research efforts of the blockchain for vehicular IoT by discussing the research problems and technical issues. After that, we point out some future research issues considering the characteristics of both blockchain and vehicular IoT.

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A Qualitative Analysis of Vehicle Positioning Requirements for Connected Vehicle Applications

Cited by 16 publications

References 36 publications

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

Using PPP Information to Implement a Global Real-Time Virtual Network DGNSS Approach

Blockchain for Vehicular Internet of Things: Recent Advances and Open Issues

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