Scipediacontent: Scipediacontent moved page Draft Content 359175545 to Schuller et al 2020a

2021-02-16T09:53:29Z

Scipediacontent moved page Draft Content 359175545 to Schuller et al 2020a

Scipediacontent: Created page with " == Abstract == In this paper, an approach is presented to predict the route and stopping intent of human-driven vehicles at urban intersections using a selection of distinct..."

2021-02-16T09:53:23Z

Created page with " == Abstract == In this paper, an approach is presented to predict the route and stopping intent of human-driven vehicles at urban intersections using a selection of distinct..."

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== Abstract ==

In this paper, an approach is presented to predict the route and stopping intent of human-driven vehicles at urban intersections using a selection of distinctive features observed on the vehicle state (position, heading, acceleration, velocity). For potential future advanced driver assistance systems, this can facilitate the situation analysis and risk assessment at road intersections, helping to improve the protection of vulnerable road users. After extracting recorded driving data for nine intersections (featuring over 50 000 crossings) from a database, they are assigned to possible routes and transformed from a time-based representation to a distance-based one. Using random decision forests, the route intent can be predicted with a mean unweighted average recall (UAR) of 0.76 at 30 m before the relevant intersection center, the stopping intent prediction scores a mean UAR of 0.78.

== Original document ==

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

* [http://xplorestaging.ieee.org/ielx7/7274857/7769266/07590077.pdf?arnumber=7590077 http://xplorestaging.ieee.org/ielx7/7274857/7769266/07590077.pdf?arnumber=7590077],
: [http://dx.doi.org/10.1109/tiv.2016.2617625 http://dx.doi.org/10.1109/tiv.2016.2617625]

* [https://ieeexplore.ieee.org/document/7590077 https://ieeexplore.ieee.org/document/7590077],
: [http://doi.org/10.1109/TIV.2016.2617625 http://doi.org/10.1109/TIV.2016.2617625],
: [https://doi.org/10.1109/TIV.2016.2617625 https://doi.org/10.1109/TIV.2016.2617625],
: [https://trid.trb.org/view/1439805 https://trid.trb.org/view/1439805],
: [https://dblp.uni-trier.de/db/journals/tiv/tiv1.html#GrossJWKS16 https://dblp.uni-trier.de/db/journals/tiv/tiv1.html#GrossJWKS16],
: [https://academic.microsoft.com/#/detail/2531184939 https://academic.microsoft.com/#/detail/2531184939]

* [https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/72166 https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/72166],
: [https://doi.org/10.1109/TIV.2016.2617625 https://doi.org/10.1109/TIV.2016.2617625]

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Schuller et al 2020a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 359175545 to Schuller et al 2020a

Scipediacontent: Created page with " == Abstract == In this paper, an approach is presented to predict the route and stopping intent of human-driven vehicles at urban intersections using a selection of distinct..."