Scipediacontent: Scipediacontent moved page Draft Content 896411624 to 238,295pk

2021-03-23T03:20:29Z

Scipediacontent moved page Draft Content 896411624 to 238,295pk

Scipediacontent: Created page with " == Abstract == In the fields of advanced driver assistance systems (ADAS) and Internet of Vehicles (IoV), predicting the vehicle state is essential, including the ego vehicl..."

2021-03-23T03:18:33Z

Created page with " == Abstract == In the fields of advanced driver assistance systems (ADAS) and Internet of Vehicles (IoV), predicting the vehicle state is essential, including the ego vehicl..."

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== Abstract ==

In the fields of advanced driver assistance systems (ADAS) and Internet of Vehicles (IoV), predicting the vehicle state is essential, including the ego vehicle’s position, velocity, and acceleration. In ADAS, an early position prediction helps to avoid traffic accidents. In IoV, the vehicle state prediction is essential for the required calculation of the expected reliable communication time between two vehicles. Many approaches have emerged to perform this vehicle state prediction. However, such approaches consider limited information of the ego vehicle and its surroundings, and they may not be very effective in practice because the real situation is highly complex and complicated. Moreover, some of the approaches often lead to a delayed prediction time due to collecting and calculating the substantial history information. By assuming that the driver is a robot driver, which eliminates distinct driving behaviors of different persons when facing the same situation, this paper creates a decision tree as a new quick and reliable method adapted to all road segments, and it proposes a new method to perform the vehicle state prediction based on this decision tree.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_896411624-beopen1427-5717-document.pdf</pdf>

== Original document ==

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

* [http://downloads.hindawi.com/journals/misy/2018/9728328.pdf http://downloads.hindawi.com/journals/misy/2018/9728328.pdf] under the license https://creativecommons.org/licenses/by

* [http://dx.doi.org/10.1155/2018/9728328 http://dx.doi.org/10.1155/2018/9728328] under the license cc-by

* [http://downloads.hindawi.com/journals/misy/2018/9728328.pdf http://downloads.hindawi.com/journals/misy/2018/9728328.pdf],
: [http://downloads.hindawi.com/journals/misy/2018/9728328.xml http://downloads.hindawi.com/journals/misy/2018/9728328.xml],
: [http://dx.doi.org/10.1155/2018/9728328 http://dx.doi.org/10.1155/2018/9728328] under the license http://creativecommons.org/licenses/by/4.0

* [http://dx.doi.org/10.1155/2018/9728328 http://dx.doi.org/10.1155/2018/9728328],
: [https://doaj.org/toc/1574-017X https://doaj.org/toc/1574-017X],
: [https://doaj.org/toc/1875-905X https://doaj.org/toc/1875-905X] under the license http://creativecommons.org/licenses/by/4.0/

* [https://www.hindawi.com/journals/misy/2018/9728328 https://www.hindawi.com/journals/misy/2018/9728328],
: [https://dblp.uni-trier.de/db/journals/mis/mis2018.html#LiuCWCG18 https://dblp.uni-trier.de/db/journals/mis/mis2018.html#LiuCWCG18],
: [http://downloads.hindawi.com/journals/misy/2018/9728328.pdf http://downloads.hindawi.com/journals/misy/2018/9728328.pdf],
: [https://academic.microsoft.com/#/detail/2804416452 https://academic.microsoft.com/#/detail/2804416452]

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Scipediacontent: Scipediacontent moved page Draft Content 896411624 to 238,295pk

Scipediacontent: Created page with " == Abstract == In the fields of advanced driver assistance systems (ADAS) and Internet of Vehicles (IoV), predicting the vehicle state is essential, including the ego vehicl..."