Latest revision as of 10:53, 16 February 2021

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://dx.doi.org/10.1109/tiv.2016.2617625

• https://ieeexplore.ieee.org/document/7590077,

http://doi.org/10.1109/TIV.2016.2617625,

https://doi.org/10.1109/TIV.2016.2617625,

https://trid.trb.org/view/1439805,

https://dblp.uni-trier.de/db/journals/tiv/tiv1.html#GrossJWKS16,

https://academic.microsoft.com/#/detail/2531184939

• https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/72166,

https://doi.org/10.1109/TIV.2016.2617625