Vehicle trajectory prediction and generation using LSTM models and GANs

Luca Rossi; Andrea Ajmar; Marina Paolanti; Roberto Pierdicca

doi:10.1371/journal.pone.0253868

Peer Review History

Original SubmissionApril 15, 2021
6 May 2021 Decision Letter - Thippa Reddy Gadekallu, Editor PONE-D-21-10854 Vehicle trajectory prediction and generation using LSTM models and GANs PLOS ONE Dear Dr. Paolanti, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Based on the comments received from the reviewers and my own observation, I recommend minor revisions for the manuscript. Please submit your revised manuscript by Jun 18 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. 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Authors’ affiliations should reflect the institution where the work was done (if authors moved subsequently, you can also list the new affiliation stating “current affiliation:….” as necessary). [Note: HTML markup is below. Please do not edit.] 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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? 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Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 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: The paper is written good. - What are the evaluations used for the verification of results? - Clearly highlight the terms used in the algorithm and explain them in the text. - Please make a comparision with existing works. - Authors should add the most recent reference: 1) Anomaly Detection in Automated Vehicles Using Multistage Attention-Based Convolutional Neural Network, IEEE Transactions on Intelligent Transportation Systems 2) CANintelliIDS: Detecting In-Vehicle Intrusion Attacks on a Controller Area Network using CNN and Attention-based GRU, IEEE Transactions on Network Science and Engineering Reviewer #2: • Add the advantages of the proposed system in one quoted line for justifying the proposed approach in the Introduction section. • The motivation for the present research would be clearer, by providing a more direct link between the importance of choosing your own method. • In the introduction, the findings of the present research work should be compared with the recent work of the same field towards claiming the contribution made. , kindly provide several references to substantiate the claim made in the abstract (that is, provide references to other groups who do or have done research in this area). The authors can cite the following references A multidirectional LSTM model for predicting the stability of a smart grid Comparative analysis of machine learning algorithms for prediction of smart grid stability† Genetically Optimized Prediction of Remaining Useful Life Predictive model for battery life in IoT networks ****** 6. 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 #1: No Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0253868.r001
Revision 1
11 Jun 2021 Author Response Dear Dr. Thippa Reddy Gadekallu, We would like to thank the Reviewers for their valuable review and very useful comments for improvement, which we believe have helped us to strengthen the article (PONE-D-21-10854) significantly. We have revised the manuscript (revised manuscript.pdf) to address the Reviewers' concerns, and all main changes are highlighted in red. We believe that the improved version better clarifies the overall contribution and we hope that the revised version meets the high quality standards of your respectable Journal. We thank you for considering our revised manuscript for publication and look forward to receiving your kind response. As far as point 3 (figures 5-9 licensing) is concerned, the background image is derived from a web map service called Dark Gray Canvas provided by ESRI (https://www.arcgis.com/home/item.html?id=1970c1995b8f44749f4b9b6e81b5ba45). The terms of use summary states that a user may include screen captures or printed or plotted maps in academic publications (research journals, textbooks, etc.). See here for additional details: https://downloads2.esri.com/arcgisonline/docs/tou_summary.pdf Affiliation details for author Andrea Ajmar has been added (point 4) A complete point by point response letter is attached. Best regards, Marina Paolanti on behalf of all authors Dipartimento di Ingegneria dell’Informazione (DII) Università Politecnica delle Marche via Brecce Bianche 12, Ancona (Italy) m.paolanti@univpm.it Response to Reviewer #1 Response: We thank the Reviewer for the comments and for the valuable suggestions. Our responses can be found in this response letter. We updated our manuscript adding new text in red (please refer to manuscript marked with changes.pdf). R1.1: The paper is written good. What are the evaluations used for the verification of results? Response: We are really grateful for your efforts in reviewing our manuscript, and we thank you for the positive feedback. Your comments have been fundamental to improve the manuscript, which has been reworked following your suggestions. Concerning the verification of results in the GIS environment, we added a statistical analysis based on planimetric distance values between real and predicted values (see new table 6 and associated text). We also added a brief paragraph in the introduction to further emphasize our proposed evaluation metrics as an important contribution of this work. R1.2: - Clearly highlight the terms used in the algorithm and explain them in the text. Response: We added a few paragraphs in the Implementation Details section, explaining the LSTM layers and how the Generator and Discriminator process the GAN input. R1.3:- Please make a comparision with existing works. Response: We added new references and highlighted the difference between those works and our methodology in the Results and Discussions section: 1. Path inference from sparse floating car data for urban networks. Transportation Research 2. Weight-based shortest-path aided map-matching algorithm for low-frequency positioning data 3. Map-matching forlow-sampling-rate GPS trajectories. Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems 4. On-line map-matching framework for floating car data with low sampling rate in urban road networks. IET Intelligent Transport Systems 5. Estimating congestion zones and travel time indexes based on the floating car data. Computers, Environment and Urban Systems 6. A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks. International Journal of Geographical Information Science R1.4:- Authors should add the most recent reference: 1) Anomaly Detection in Automated Vehicles Using Multistage Attention-Based Convolutional Neural Network, IEEE Transactions on Intelligent Transportation Systems 2) CANintelliIDS: Detecting In-Vehicle Intrusion Attacks on a Controller Area Network using CNN and Attention-based GRU, IEEE Transactions on Network Science and Engineering Response: Thanks for your wise suggestions, these references have been added in the introduction section to expand the potential scenario where similar approaches can be adopted. We also included two other references related to the use of FCD data and derived trajectories: one referring to a review on the impact of FCD penetration rates in different analysis environments and one related to a machine learning technique applied to FCD trajectories to model traffic crashes. 1. Examining the potential of floating car data for dynamic traffic management. IET Intelligent Transport Systems 2. Crash prediction based on traffic platoon characteristics using floating car trajectory data and the machine learning approach. Accident Analysis Prevention Response to Reviewer #2 Response: We thank the Reviewer for the comments and for the valuable suggestions. Our responses can be found in this response letter. We updated our manuscript adding new text in red (please refer to manuscript marked with changes.pdf). R2.1: Add the advantages of the proposed system in one quoted line for justifying the proposed approach in the Introduction section. Response: We appreciate your effort and attention in evaluating our paper. We have corrected and improved the article considering your useful comments and suggestions. We added a paragraph at the end of the introduction that summarizes the advantages of the proposed method. We also added a couple of sentences, and related references, highlighting how trajectory predictions are considered useful for both traffic management and autonomous vehicles. R2.2: The motivation for the present research would be clearer, by providing a more direct link between the importance of choosing your own method. Response: We added a paragraph in the introduction that summarizes the motivations already stated in the Challenges subsection. R2.3: In the introduction, the findings of the present research work should be compared with the recent work of the same field towards claiming the contribution made. , kindly provide several references to substantiate the claim made in the abstract (that is, provide references to other groups who do or have done research in this area). The authors can cite the following references A multidirectional LSTM model for predicting the stability of a smart grid Comparative analysis of machine learning algorithms for prediction of smart grid stability† Genetically Optimized Prediction of Remaining Useful Life Predictive model for battery life in IoT networks Response: Thank you for your suggestions. New updated references have been added to expand the literature in this domain (diverse from the one kindly suggested by you as they do not cover the specific topic of our approach) and to highlight the differences between those works and ours. Those included references are the following: 1. Path inference from sparse floating car data for urban networks. Transportation Research 2. Weight-based shortest-path aided map-matching algorithm for low-frequency positioning data 3. Map-matching forlow-sampling-rate GPS trajectories. Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems 4. On-line map-matching framework for floating car data with low sampling rate in urban road networks. IET Intelligent Transport Systems 5. Estimating congestion zones and travel time indexes based on the floating car data. Computers, Environment and Urban Systems 6. A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks. International Journal of Geographical Information Science 7. Examining the potential of floating car data for dynamic traffic management. IET Intelligent Transport Systems 8. Crash prediction based on traffic platoon characteristics using floating car trajectory data and the machine learning approach. Accident Analysis Prevention 9. Anomaly Detection in Automated Vehicles Using Multistage Attention-Based Convolutional Neural Network. IEEE Transactions on Intelligent Transportation Systems 10. CANintelliIDS: Detecting In-Vehicle Intrusion Attacks on a Controller Area Network using CNN and Attention-based GRU. IEEE Transactions on Network Science and Engineering Attachments Attachment Submitted filename: Plos_One_Response.docx.pdf https://doi.org/10.1371/journal.pone.0253868.r002
15 Jun 2021 Decision Letter - Thippa Reddy Gadekallu, Editor Vehicle trajectory prediction and generation using LSTM models and GANs PONE-D-21-10854R1 Dear Dr. Paolanti, 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. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Thippa Reddy Gadekallu 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 #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 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 #1: Partly Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: 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 #1: Yes Reviewer #2: 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 #1: Yes Reviewer #2: 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 #1: The authors have addressed my comments partly. AUthors should revisit the R1 comments and address them fully. Reviewer #2: The authors have addressed all of my comments. The paper can can be accepted in the current format. Thank you ****** 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 #1: No Reviewer #2: No https://doi.org/10.1371/journal.pone.0253868.r003
Formally Accepted
21 Jun 2021 Acceptance Letter - Thippa Reddy Gadekallu, Editor PONE-D-21-10854R1 Vehicle trajectory prediction and generation using LSTM models and GANs Dear Dr. Paolanti: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Thippa Reddy Gadekallu Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0253868.r004

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