Using Maneuver Transition Probabilities to Estimation of Driving Transition Pattern in Traditional Vehicle Environment and Connected Vehicle Environment - Details

Author：

Sun, G. (Sun, G..) | Rong, J. (Rong, J..) | Chang, X. (Chang, X..) | Wang, Y. (Wang, Y..) | Zhou, C. (Zhou, C..)

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Abstract：

Considering　the　developments　in　intelligent　transportation　technology,　accurately　estimating　the　driving　maneuver　transition　in　the　connected　vehicle　(CV)　environment　is　crucial　to　design　driver　assistance　systems.　In　this　study,　driving　data　were　collected　using　a　driving　simulator.　Thirty　six　test　subjects　participated　in　the　experiment　and　the　length　of　the　road　was　about　20　km.　Two　types　of　test　vehicle　were　used:　equipped　with　a　driver　assistance　system　and　not　equipped　with　a　driver　assistance　system.　Driving　behavior　in　this　study　was　categorized　into　nine　maneuver　states.　The　experimental　road　was　divided　into　nine　zones.　A　comparative　analysis　was　conducted　to　compare　the　changes　in　speed　performance　and　driving　maneuver　transition　between　the　connected　vehicle　(CV)　environment　and　the　traditional　vehicle　(TV)　environment.　The　experiment　used　maneuver　transition　probabilities　to　represent　the　driving　behavior.　The　results　show　that　connected　vehicle　can　help　drivers　in　adjusting　vehicle　speed　to　ensure　driving　safety　and　comfort.　©　ASCE.

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[ 1 ] [Sun G.]Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing Univ. of Technology, Beijing, 100124, China
[ 2 ] [Rong J.]Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing Univ. of Technology, Beijing, 100124, China
[ 3 ] [Chang X.]Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing Univ. of Technology, Beijing, 100124, China
[ 4 ] [Wang Y.]Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing Univ. of Technology, Beijing, 100124, China
[ 5 ] [Zhou C.]Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing Univ. of Technology, Beijing, 100124, China

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Year： 2020

Page： 2263-2270

Language： English

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WoS CC Cited Count： 0

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ESI Highly Cited Papers on the List： 0 Unfold All

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30 Days PV： 8

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