Neural Networks for Driver Behavior Analysis
and Antonella Santone 3,†Round 1
Reviewer 1 Report
- It is suggested to rearrange the English expression. Many sentences are hard to understand.
- Too much space is used to introduce the background, but the author's contribution is not highlighted.
- The theory of deep learning needs a deeper understanding.
- For deep learning, the amount of data is too small.
- There is a large number of repeated experiments and repeated figures. The content of the experiment is monotonous and lack of persuasion.
Author Response
Comment #1: It is suggested to rearrange the English expression. Many sentences are hard to understand.
Response: We are thankful to the reviewer for the observation. We carefully proofread the revised version of the manuscript. Thank you again for your support.
Comment #2:Too much space is used to introduce the background, but the author's contribution is not highlighted.
Response: Thank you for your observation. In the revised version of the paper, in the introduction section, we highlighted the distinctive points of the proposed method. Thank you again for your time.
Comment #3:The theory of deep learning needs a deeper understanding.
Response: In the revised section of the paper we simplified the background section, in order to clarify the main deep learning concepts to make the paper self contained. Moreover we added several examples with the aim to better explain the preliminary concepts introduced in the background section.
Comment #4:For deep learning, the amount of data is too small.
Response: We are thankful to the reviewer for the opportunity to improve the experimental analysis of the proposed manuscript. We considered an additional dataset composed by 10 additional drivers, obtaining similar results. Thank you again for your observation,
Comment #5: There is a large number of repeated experiments and repeated figures. The content of the experiment is monotonous and lack of persuasion.
Response: We perfectly agree with the reviewer. In the revised version of the manuscript we removed several figures in the experimental section: as a matter of fact more of these figures were redundant. In the current version of the manuscript there are two figures (accuracy and loss) for one driver, one style and one path under analysis. Thank you for the opportunity to improve the paper presentation.
Reviewer 2 Report
1. Related work is briefly analyzed in section 2, authors do not adequately contrast their work to existing approaches. In the sense that they do not highlight what is missing from each of the other proposals. The authors should clearly describe related work in more detail, contrasting the limitations of the related works. Moreover, the reviewer recommend to ease the overview related works by using overview tables.
2. The proposed processes should be revised in a more formal pseudocode template. Moreover, the authors should include more technical details and explanations.
3. Some parameters and their values are unknown. It would be better to show all these parameters and explain the reason for those numbers in the table.
4. The reader may want to see how this work differs from other previous works. The comparison to other improved schemes (within the last 3 years) is required.
5. Please points out some insufficiency and limitation that needs further improvements in the conclusion. Moreover, formats of reference list lack consistency.
Author Response
Comment #1: Related work is briefly analyzed in section 2, authors do not adequately contrast their work to existing approaches. In the sense that they do not highlight what is missing from each of the other proposals. The authors should clearly describe related work in more detail, contrasting the limitations of the related works. Moreover, the reviewer recommend to ease the overview related works by using overview tables.
Response: In the revised version of the manuscript, in the related work section, we added a comparative table with the aim to better highlight the method proposed in the following contribution. Furthermore, we added other relevant work in the diver analysis context. Thank you for your observation.
Comment #2: The proposed processes should be revised in a more formal pseudocode template. Moreover, the authors should include more technical details and explanations.
Response: In the revised version of the paper we added the pseudocode code of the proposed neural network with the aim to explain the network implementation. Thank for opportunity to improve the presentation of the proposed manuscript.
Comment #3:Some parameters and their values are unknown. It would be better to show all these parameters and explain the reason for those numbers in the table.
Response: We added a python code snippet (with the relative explanation) to better explain how the proposed neural network (with the relative parameters) is implemented. Thank you for your observation.
Comment #4:The reader may want to see how this work differs from other previous works. The comparison to other improved schemes (within the last 3 years) is required.
Response: Thank you for your observation. In the revised version of the manuscript, in the related work section, we added recent related work with the aim to better compare the proposed method with respect to the current state of the art. We also added a comparative table to clearly explain how the proposed approach differs from the previous ones. Thank you again for the opportunity to improve the proposed manuscript.
Comment #5:Please points out some insufficiency and limitation that needs further improvements in the conclusion. Moreover, formats of reference list lack consistency.
Response: In the revised version of the manuscript, in the conclusion and future work section, we added several sentences aimed to highlight the limitations of the proposed method, by discussion also how it will be possible to overcome them.
Reviewer 3 Report
In this manuscript, authors suggested an approach of applying Neural Networks for Driver Behavior Analysis.
1. The novelty needs to be improved. What are main contributions?
2. There are many recent related works about analyzing vehicle data using several techniques such as clustering, classification, neural network and regression and so on. Recent references about the issues need to be added and analyzed. Below is an example of related reference.
U. Yun, Monitoring vehicle outliers based on clustering technique. Applied Soft Computing, 49, 845-860 (2016)
3. Authors did not provide the suggested detail algorithms of using neural network to analyze vehicle data. It is recommended that the proposed algorithm should be presented with detail explanations.
4. The main limitation of this manuscript is performance evaluation. In performance evaluation, the proposed approach was not compared with state of the art approaches. Please extended performance comparison and evaluations.
Author Response
Comment #1: The novelty needs to be improved. What are main contributions?
Response: Thank you for the opportunity to improve the quality of the proposed manuscript. We added several sentences, in the introduction section, aimed to clearly highlight the paper contribution. Thank you again for your observation.
Comment #2: There are many recent related works about analyzing vehicle data using several techniques such as clustering, classification, neural network and regression and so on. Recent references about the issues need to be added and analyzed. Below is an example of related reference: U. Yun, Monitoring vehicle outliers based on clustering technique. Applied Soft Computing, 49, 845-860 (2016)
Response: In the revised version of the manuscript we added, in the related work section, several relevant and recent research papers, including the one suggested by the reviewer. We also added a comparative table with the aim to better compare the proposed approach with respect to the current state-of-the-art literature. Thank you for the opportunity to improve the proposed manuscript.
Comment #3: Authors did not provide the suggested detail algorithms of using neural network to analyze vehicle data. It is recommended that the proposed algorithm should be presented with detail explanations.
Response: In the revised version of the manuscript we added a pseudocode snippet, with the relative explanation, with the aim to clearly explain how the proposed neural network was implemented. Thank you for the opportunity to improve the presentation of the proposed manuscript.
Comment #4: The main limitation of this manuscript is performance evaluation. In performance evaluation, the proposed approach was not compared with state of the art approaches. Please extended performance comparison and evaluations.
Response: We are really thankful for this observation. In the revised version of the manuscript, we added a comparative table with the aim to compare the proposed approach with the methods recently proposed in the current state of the art, also from a performance point of view. Thank you again for your suggestion.
Round 2
Reviewer 2 Report
1. The reviewer recommend to ease the overview related works by using overview tables.
2. The Figure 7 is not pseudocode.
3. The comparison to other improved schemes (within the last 3 years) is required, such as [A].
[A] J. Chen, C. Lee, P. Huang and C. Lin, Driver Behavior Analysis via Two-Stream Deep Convolutional Neural Network, Applied Sciences, vol. 10, no. 6, 2020.
Author Response
Comment #1:
The reviewer recommend to ease the overview related works by using overview tables.
Response: In the revised version of the manuscript we added a second table to compare the related papers and the proposed one from the detection rate point of view. Thank you for your observation and for the opportunity to improve the quality of the proposed manuscript.
Comment #2:
The Figure 7 is not pseudocode.
Response: In the revised version of the manuscript we replaced the Python snippet with the pseudocode one. Thank you for the opportunity to improve the manuscript presentation.
Comment #3:
The comparison to other improved schemes (within the last 3 years) is required, such as [A].
[A] J. Chen, C. Lee, P. Huang and C. Lin, Driver Behavior Analysis via Two-Stream Deep Convolutional Neural Network, Applied Sciences, vol. 10, no. 6, 2020.
Response: We added another table to compare the state of the art to compare also the performances and the features considered in related research. We added the reference highlighted by the reviewer and other references published in 2020 and 2021. Thank you for the interesting suggestion and for the opportunity to improve the proposed manuscript.
Reviewer 3 Report
I checked the responses and the revised manuscript.
It is fine to me.
It can be "Accept" now.
Author Response
Comment #1:
I checked the responses and the revised manuscript. It is fine to me. It can be "Accept" now.
Response: Thank you for your approval and for your help in improving the proposed manuscript.
Round 3
Reviewer 2 Report
This paper has edited and revised according to the reviewer's suggestions.
Author Response
We are thankful to the reviewer for the help to improve the proposed manuscript. According to the reviewer, in the revised version of the paper we improved the research design, the method description and in the conclusion section we added several sentences to better highlight the results we obtained. Thank you again for your observations.
