Scipediacontent: Scipediacontent moved page Draft Content 845434210 to Sim et al 2019a

2021-02-15T08:53:34Z

Scipediacontent moved page Draft Content 845434210 to Sim et al 2019a

Scipediacontent: Created page with " == Abstract == The smart regenerative braking system (SRS) is an autonomous version of one-pedal driving in electric vehicles. To implement SRS, a deceleration planning algo..."

2021-02-15T08:53:32Z

Created page with " == Abstract == The smart regenerative braking system (SRS) is an autonomous version of one-pedal driving in electric vehicles. To implement SRS, a deceleration planning algo..."

New page

== Abstract ==

The smart regenerative braking system (SRS) is an autonomous version of one-pedal driving in electric vehicles. To implement SRS, a deceleration planning algorithm is necessary to generate the deceleration used in automatic regenerative control. To reduce the discomfort from the automatic regeneration, the deceleration should be similar to human driving. In this paper, a deceleration planning algorithm based on multi-layer perceptron (MLP) is proposed. The MLP models can mimic the human driving behavior by learning the driving data. In addition, the proposed deceleration planning algorithm has a classified structure to improve the planning performance in each deceleration condition. Therefore, the individual MLP models were designed according to three different deceleration conditions: car-following, speed bump, and intersection. The proposed algorithm was validated through driving simulations. Then, time to collision and similarity to human driving were analyzed. The results show that the minimum time to collision was 1.443 s and the velocity root-mean-square error (RMSE) with human driving was 0.302 m/s. Through the driving simulation, it was validated that the vehicle moves safely with desirable velocity when SRS is in operation, based on the proposed algorithm. Furthermore, the classified structure has more advantages than the integrated structure in terms of planning performance.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_845434210-beopen619-8762-document.pdf</pdf>

== Original document ==

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

* [http://dx.doi.org/10.3390/s19184020 http://dx.doi.org/10.3390/s19184020] under the license https://creativecommons.org/licenses/by

* [http://dx.doi.org/10.3390/s19184020 http://dx.doi.org/10.3390/s19184020] under the license http://creativecommons.org/licenses/by/3.0/

* [http://europepmc.org/articles/PMC6766928 http://europepmc.org/articles/PMC6766928] under the license https://creativecommons.org/licenses/by/4.0

* [https://www.mdpi.com/1424-8220/19/18/4020/pdf https://www.mdpi.com/1424-8220/19/18/4020/pdf]

* [https://www.mdpi.com/1424-8220/19/18/4020 https://www.mdpi.com/1424-8220/19/18/4020],
: [https://doaj.org/toc/1424-8220 https://doaj.org/toc/1424-8220] under the license cc-by

* [https://www.mdpi.com/1424-8220/19/18/4020 https://www.mdpi.com/1424-8220/19/18/4020],
: [https://www.mdpi.com/1424-8220/19/18/4020/pdf https://www.mdpi.com/1424-8220/19/18/4020/pdf],
: [https://dblp.uni-trier.de/db/journals/sensors/sensors19.html#SimMASJ19 https://dblp.uni-trier.de/db/journals/sensors/sensors19.html#SimMASJ19],
: [https://pubmed.ncbi.nlm.nih.gov/31540382 https://pubmed.ncbi.nlm.nih.gov/31540382],
: [https://academic.microsoft.com/#/detail/2974606803 https://academic.microsoft.com/#/detail/2974606803]

* [https://www.mdpi.com/1424-8220/19/18/4020/pdf https://www.mdpi.com/1424-8220/19/18/4020/pdf],
: [http://dx.doi.org/10.3390/s19184020 http://dx.doi.org/10.3390/s19184020]

under the license https://creativecommons.org/licenses/by/4.0/

← Older revision	Revision as of 08:53, 15 February 2021
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Sim et al 2019a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 845434210 to Sim et al 2019a

Scipediacontent: Created page with " == Abstract == The smart regenerative braking system (SRS) is an autonomous version of one-pedal driving in electric vehicles. To implement SRS, a deceleration planning algo..."