Latest revision as of 20:16, 22 March 2021

Abstract

Future driver assistance systems will rely on accurate, reliable and continuous knowledge on the position of other road participants, including pedestrians, bicycles and other vehicles. The usual approach to tackle this requirement is to use on-board ranging sensors inside the vehicle. Radar, laser scanners or vision-based systems are able to detect objects in their line-of-sight. In contrast to these non-cooperative ranging sensors, cooperative approaches follow a strategy in which other road participants actively support the estimation of the relative position. The limitations of on-board ranging sensors regarding their detection range and angle of view and the facility of blockage can be approached by using a cooperative approach based on vehicle-to-vehicle communication. The fusion of both, cooperative and non-cooperative strategies, seems to offer the largest benefits regarding accuracy, availability and robustness. This survey offers the reader a comprehensive review on different techniques for vehicle relative positioning. The reader will learn the important performance indicators when it comes to relative positioning of vehicles, the different technologies that are both commercially available and currently under research, their expected performance and their intrinsic limitations. Moreover, the latest research in the area of vision-based systems for vehicle detection, as well as the latest work on GNSS-based vehicle localization and vehicular communication for relative positioning of vehicles, are reviewed. The survey also includes the research work on the fusion of cooperative and non-cooperative approaches to increase the reliability and the availability.

Document type: Article

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The different versions of the original document can be found in:

• http://dx.doi.org/10.3390/s17020271 under the license https://creativecommons.org/licenses/by

• http://europepmc.org/articles/PMC5335929 under the license https://creativecommons.org/licenses/by/4.0

• https://dx.doi.org/10.3390/s17020271

• https://elib.dlr.de/109545

• https://www.mdpi.com/1424-8220/17/2/271/pdf

• http://www.mdpi.com/1424-8220/17/2/271,

https://doaj.org/toc/1424-8220 under the license cc-by

• http://www.mdpi.com/1424-8220/17/2/271/pdf,

http://dx.doi.org/10.3390/s17020271

• https://dblp.uni-trier.de/db/journals/sensors/sensors17.html#Muller17,

https://elib.dlr.de/109545,

https://www.ncbi.nlm.nih.gov/pubmed/28146129,

http://europepmc.org/abstract/MED/28146129,

https://doi.org/10.3390/s17020271,

https://trid.trb.org/view/1468146,

https://core.ac.uk/display/90880747,

https://academic.microsoft.com/#/detail/2566918566 under the license https://creativecommons.org/licenses/by/4.0/