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Article
Peer-Review Record

Comprehensive Automated Driving Maneuvers under a Non-Signalized Intersection Adopting Deep Reinforcement Learning

Appl. Sci. 2022, 12(19), 9653; https://doi.org/10.3390/app12199653
by Quang-Duy Tran 1 and Sang-Hoon Bae 2,*
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Reviewer 3: Anonymous
Appl. Sci. 2022, 12(19), 9653; https://doi.org/10.3390/app12199653
Submission received: 26 July 2022 / Revised: 21 September 2022 / Accepted: 23 September 2022 / Published: 26 September 2022

Round 1

Reviewer 1 Report

The manuscript presents evidence from experimental simulations to improve AV operations in non0signalised intersections. The findings presented are compelling and useful for AV research and development.

The manuscript is written clearly and is easy for follow. The only comment that I have is that the conclusion section should be expanded to discuss the implications of the simulation findings and to link it back to the knowledge gaps and real-world needs that were set out in the introduction.

Author Response

Dear Reviewer

We would like to thank you for your careful and thorough reading of our manuscript and for the thoughtful comments and constructive suggestions. We have revised our manuscript in response to your suggestion and hope that this improved manuscript is acceptable for publication in the Applied Sciences journal.

Best regards.

Author Response File: Author Response.docx

Reviewer 2 Report

This work tries to investigate the effects of adopting deep reinforcement learning methods in comprehensive automated driving maneuvers under a non-signalized intersection. The topic is very interesting and all the text is straightforward and quite easy to follow. I only have one concern is that many figures are in low resolution and are not carefully labeled or explained. I hope the authors can further improve this.

Author Response

Dear Reviewer

We would like to thank you for your careful and thorough reading of our manuscript and for the thoughtful comments and constructive suggestions. We have revised our manuscript in response to your suggestion and hope that this improved manuscript is acceptable for publication in the Applied Sciences journal.

Best regards.

Author Response File: Author Response.docx

Reviewer 3 Report

Comparing our method to other machine learning algorithms aiming to achieve better performance of decision-making for AVs under a mixed-traffic environment.

The authors should clearly mention the names of other machine learning algorithms used to compare their research.  This is not clear to the reader.

Author Response

Dear Reviewer

We would like to thank you for your careful and thorough reading of our manuscript and for the thoughtful comments and constructive suggestions. We have revised our manuscript in response to your suggestion and hope that this improved manuscript is acceptable for publication in the Applied Sciences journal.

Best regards.

Author Response File: Author Response.docx

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