Simulating autonomous driving styles: accelerations for three road profiles

J. Karjanto, N. Yusof, J.M.B. Terken, F.L.M. Delbressine, M.Z.B. Hassan, G.W.M. Rauterberg

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Abstract

This paper presents a new experimental approach to simulate projected autonomous driving styles based on the accelerations at three road profiles. This study was focused on the determination of ranges of accelerations in triaxial direction to simulate the autonomous driving experience. A special device, known as the Automatic Acceleration and Data controller (AUTOAccD), has been developed to guide the designated driver to accomplish the selected accelerations based on the road profiles and the intended driving styles namely assertive, defensive and light rail transit (LRT). Experimental investigations have been carried out at three different road profiles (junction, speed hump, and corner) with two designated drivers with five trials on each condition. A driving style with the accelerations of LRT has also been included in this study as it is significant to the present methodology because the autonomous car is predicted to accelerate like an LRT, in such a way that it enables the users to conduct activities such as working on a laptop, using personal devices or eating and drinking while travelling. The results demonstrated that 92 out of 110 trials of the intended accelerations for autonomous driving styles could be achieved and simulated on the real road by the designated drivers. The differences between the two designated drivers were negligible, and the rates of succeeding in realizing the intended accelerations were high. The present approach in simulating autonomous driving styles focusing on accelerations can be used as a tool for experimental setup involving autonomous driving experience and acceptance.
Original languageEnglish
Article number01005
Pages (from-to)1-16
Number of pages16
JournalMATEC Web of Conferences
Volume90
DOIs
Publication statusPublished - 2017
Event2nd International Conference on Automotive Innovation and Green Vehicle (AiGEV 2016) - Mai, Cyberjaya, Malaysia
Duration: 2 Aug 20163 Aug 2016
http://aigev.ump.edu.my/index.php

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abstract = "This paper presents a new experimental approach to simulate projected autonomous driving styles based on the accelerations at three road profiles. This study was focused on the determination of ranges of accelerations in triaxial direction to simulate the autonomous driving experience. A special device, known as the Automatic Acceleration and Data controller (AUTOAccD), has been developed to guide the designated driver to accomplish the selected accelerations based on the road profiles and the intended driving styles namely assertive, defensive and light rail transit (LRT). Experimental investigations have been carried out at three different road profiles (junction, speed hump, and corner) with two designated drivers with five trials on each condition. A driving style with the accelerations of LRT has also been included in this study as it is significant to the present methodology because the autonomous car is predicted to accelerate like an LRT, in such a way that it enables the users to conduct activities such as working on a laptop, using personal devices or eating and drinking while travelling. The results demonstrated that 92 out of 110 trials of the intended accelerations for autonomous driving styles could be achieved and simulated on the real road by the designated drivers. The differences between the two designated drivers were negligible, and the rates of succeeding in realizing the intended accelerations were high. The present approach in simulating autonomous driving styles focusing on accelerations can be used as a tool for experimental setup involving autonomous driving experience and acceptance.",
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Simulating autonomous driving styles: accelerations for three road profiles. / Karjanto, J.; Yusof, N.; Terken, J.M.B.; Delbressine, F.L.M.; Hassan, M.Z.B.; Rauterberg, G.W.M.

In: MATEC Web of Conferences, Vol. 90, 01005, 2017, p. 1-16.

Research output: Contribution to journalArticleAcademicpeer-review

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