Journal article
Cooperative Position Prediction: Beyond Vehicle-to-Vehicle Relative Positioning
IEEE Transactions on Intelligent Transportation Systems, Vol.21(3), pp.1121-1130
2020
Abstract
Reliable and accurate relative position prediction techniques are vital to the integrity of cooperative intelligent transportation systems (C-ITS) because most C-ITS safety applications should constantly access a (relative-) localization mechanism that satisfies not only a certain (relative-) position accuracy range but also a minimal computational complexity. A global navigation satellite system (GNSS) coupled with dedicated short-range communications (DSRC) links is a candidate for the localization system required for vehicular relative positioning. The vehicle-to-vehicle (V2V) exchange of safety-related data such as raw GNSS measurements, as accurate and frequent as possible, via DSRC links allows the vehicular localization mechanism to maintain real-time relative positioning (RRP) vectors. However, V2V DSRC safety messages may include inaccurate positioning data or may fast become outdated, and/or DSRC links may occasionally fail due to various adverse scenarios including network overheads that drop the bandwidth of vehicular ad-hoc networks. Hence, each DSRC-enabled vehicle may not necessarily know the latest positioning data of neighboring vehicles. This calls upon reliable and accurate relative position prediction mechanisms to cover the DSRC-related deficiencies of relative positioning. This paper, at first, studies the benefits of a terrestrial communications system complementing DSRC-based V2V RRP and then considers position prediction techniques suitable for V2V RRP and examines a few existing solutions and makes a detailed comparison based on a set of required positioning performance parameters for vehicle safety application.
Details
- Title
- Cooperative Position Prediction: Beyond Vehicle-to-Vehicle Relative Positioning
- Authors
- Keyvan Ansari (Author) - University of the Sunshine Coast - USC Business School
- Publication details
- IEEE Transactions on Intelligent Transportation Systems, Vol.21(3), pp.1121-1130
- Publisher
- Institute of Electrical and Electronics Engineers
- Date published
- 2020
- DOI
- 10.1109/TITS.2019.2902572
- ISSN
- 1524-9050
- Organisation Unit
- University of the Sunshine Coast, Queensland; USC Business School - Legacy; School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99451237302621
- Output Type
- Journal article
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- Web Of Science research areas
- Engineering, Civil
- Engineering, Electrical & Electronic
- Transportation Science & Technology
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Source: InCites