-->PONE-D-25-42907-->-->Research on vehicle lateral stability control under low-adhesion road conditions using proximal policy optimization algorithm-->-->PLOS ONE

Dear Dr. Huang

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We look forward to receiving your revised manuscript.

Kind regards,

Jinhao Liang

Academic Editor

PLOS ONE

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Additional Editor Comments:

Please revise the manuscript thoroughly according to the reviewers’ comments. In addition, the literature review on vehicle lateral stability control should incorporate more recent studies, such as "A Robust Dynamic Game-Based Control Framework for Integrated Torque Vectoring and Active Front-Wheel Steering System, IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 7, pp. 7328-7341, July 2023", and "A Direct Yaw Moment Control Framework Through Robust T-S Fuzzy Approach Considering Vehicle Stability Margin, IEEE/ASME Transactions on Mechatronics, vol. 29, no. 1, pp. 166-178, Feb. 2024".

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Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #1: Yes

Reviewer #2: Yes

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-->2. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: Yes

Reviewer #2: Yes

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-->3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

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-->4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #1: Yes

Reviewer #2: Yes

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-->5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: This paper proposes to use PPO deep reinforcement learning to achieve longitudinal and horizontal cooperative stability control of low-attached pavement, and verifies it by Amesim-CarSim-Python co-simulation and HIL experiments. The overall idea is clear, the experiment is sufficient, and the results are credible. However, there are still some problems, it is recommended to modify and adopt, see the following for the specific problems:

1.The limitations of deep reinforcement learning (DRL) (safety, interpretability, training costs) are not mentioned. The authors should explain these limitations in a few sentences.

2.There is no uniform definition of “low-adhesion road surface.” Line 122 of the manuscript states that μ < 0.3, but line 596 states that μ = 0.2. It is recommended that the definition of “low friction” be standardized.

3.The symbols ε1 and ε2 in formula (13) have no values or references. The author needs to provide reference values or explanations.

4.The parameters in Table 4 do not have physical dimensions. The author should explain this to prevent misunderstanding by readers.

5.Table 4 ω₁=-2 may make the reward value negative, and its physical meaning needs to be explained, does this punish excessive braking?

6.The PPO does not specify the network structure, activation function, observation normalization method, random seed, or training-testing data division ratio, making it difficult to reproduce. The authors should supplement the table with an explanation of the number of network layers, number of nodes, and training-testing data division ratio.

7.Reference 26 contains an error in the DOI number (2000-01-1060 corresponds to 2001-01-1060).

8.The overall clarity of the picture is not high enough, and the resolution of each schematic needs to be improved.

Reviewer #2: This manuscript presents a study on an integrated longitudinal and lateral vehicle stability control system using the Proximal Policy Optimization (PPO) deep reinforcement learning algorithm. The simulation experiments verified the effectiveness of the proposed algorithm. Some issues need to be resolved before the manuscript can be accepted.

1. A more detailed description of the hyperparameter tuning methodology is recommended.

2. The manuscript currently lacks sufficient comparisons with other learning algorithms to highlight the superiority of the proposed method.

3. It is recommended that the authors conduct a thorough discussion on the limitations of the current research and provide an evaluation of the feasibility of deploying it on real automotive electronic control units.

4. The problem of vehicle lateral control is a well-established example of motion control for systems under nonholonomic constraints, a field with extensive research. It is suggested that the introduction incorporate more key studies on the lateral control of nonholonomic systems. Doing so would not only enrich the literature review but also help to define the unique contributions of this study and underscore the significance of tackling this classic problem with advanced reinforcement learning techniques more sharply.

a) "Paired Interactions of Magnetic Millirobots in Confined Spaces Through Data-Driven Disturbance Rejection Control Under Global Input," IEEE/ASME Transactions on Mechatronics, doi: 10.1109/TMECH.2024.3521085.

b) "A Robot Motion Learning Method Using Broad Learning System Verified by Small-Scale Fish-Like Robot," in IEEE Transactions on Cybernetics, vol. 53, no. 9, pp. 6053-6065, Sept. 2023, doi: 10.1109/TCYB.2023.3269773.

5. The manuscript is generally well-written, but a few minor grammatical checks and rephrasing could improve readability. For example, some sentences are quite long and could be broken down.

6. The manuscript’s images are blurry and high-resolution versions should be provided.

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Reviewer #1: No

Reviewer #2: No

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Research on vehicle lateral stability control under low-adhesion road conditions using proximal policy optimization algorithm

PONE-D-25-42907R1

Dear Dr. Huang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Jinhao Liang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

-->Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.-->

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

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-->2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. -->

Reviewer #2: Yes

Reviewer #3: Yes

**********

-->3. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #2: Yes

Reviewer #3: Yes

**********

-->4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #2: Yes

Reviewer #3: Yes

**********

-->5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.-->

Reviewer #2: Yes

Reviewer #3: Yes

**********

-->6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #2: (No Response)

Reviewer #3: The author proposed a research on vehicle lateral stability control under low-adhesion road conditions using proximal policy optimization algorithm, and provided detailed data analysis. This research is rich in content and the conclusions are reasonable.

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-->7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .-->

Reviewer #2: No

Reviewer #3: No

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