Situation-Aware Causal Inference-Driven Vehicle Lane-Changing Decision-Making

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper presents a three-layer framework for vehicle lane-changing decisions using electronic rearview data, attention mechanisms, and counterfactual causal reasoning. While the topic is relevant and the approach shows promise, several areas require improvement.

 

1. The literature review is quite dense and would benefit from a clearer structure with well-defined subsections. The three identified deficiencies are rather broad and should be articulated with more specificity. It is recommended to restructure the literature review with clearer categorization, highlighting more precise technical gaps rather than general observations, and better integrating the relevance of electronic rearview mirror technology. Moreover, improving grammar and refining sentence structures throughout the section would enhance overall readability.
2. To better highlight your contributions, consider adding a clear, bullet-listed list that explicitly outlines the novelty of your work compared to existing studies. In your manuscript, the contributions are somewhat buried within broad statements and lack a clear technical distinction.
3. In the Description of the Scenario section, several equations are presented involving multiple parameters. However, the coefficients k_f and k_r, as well as the "infinitesimal parameter" ε = 10⁻⁹, are introduced without any theoretical justification. It is recommended to provide a theoretical basis for the dynamic adjustment coefficients and clarify the physical meaning of each parameter.

 

4. The paper lists the PSO hyperparameters, but it does not provide convergence plots or any sensitivity analysis. Adding convergence curves or a brief sensitivity study is recommended.

 

5. The manuscript describes three traffic density levels (“low,” “medium,” and “high”), but it does not specify the corresponding vehicle counts, arrival rates, or flow rates. Add a table that summarizes the key parameters for each scenario, such as the number of vehicles per kilometer, inter-arrival time distributions, the number of lanes, etc.
6. The manuscript mentions adding a 5% Gaussian perturbation to the perceived positions and speeds to simulate sensor noise. However, it's unclear whether this level of noise accurately reflects real-world sensor errors under different conditions. Authors can justify the 5% noise level by referencing empirical studies on sensor accuracy or by conducting a sensitivity analysis (e.g., testing 1%, 5%, and 10% noise levels) to demonstrate the framework's robustness to varying degrees of noise.
7. The authors use SUMO to model different traffic densities and test their lane‑change framework, but key setup details are missing. Specify the SUMO version, simulation timestep, and random seeds, network, route, flow rates, vehicle types, etc.

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

1. Conclusions can be improved. It is necessary to highlight and contrast the results obtained; for example, in comparison with those obtained by other authors.

2. Provide more discussion of the results obtained. Better justify the tests performed and the scenarios proposed for simulations and field tests to better evaluate the results obtained. Objectively evaluate the results compared to those obtained in other works with different techniques.

3. The texts in Figure 2 are not presented appropriately. The text is not completely legible as the letters overlap in the words.

Author Response

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Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

The title and objectives stated in the abstract of the paper correspond to its content.

The paper is well structured, and the bibliographical study is well done, documented and up-to-date.

The paper clearly mentions the use of modern techniques, such as structural causal models, PSO.

The proposed model brings an interesting combination of situational perception, adaptive attention, and causal reasoning – typical elements in AI and advanced autonomous driving.

How is causality modeled – is it graphical causal networks, interventions, Bayesian models?

Please check and correct the references in the text.

• the bibliographic reference [18] appears twice differently:
  • Wu [18] (2023)
  • Lu (2024)
• reference 19 in the text is [20] in the bibliography, Kim (2022) [20]  etc

Some questions for the authors:

Related to SUMO software:

• It would be useful to specify how many vehicles are simulated per experiment
• Was the simulation repeated several times? for statistical consistency and validation?
• Regarding traffic conditions – the authors only specify average traffic. It would be useful to analyze also for heavy and light traffic conditions

What statistical methods did you use to find out the differences between the models?

In the Conclusions I would suggest a sentence like for example: “The differences between the models were tested with ANOVA or t-test and proved significant (p < 0.01), thus validating the robustness of the results.”

The scientific content is good, the results obtained are relevant.  In conclusion, the paper is good.

Author Response

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Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have responded thoroughly to my previous comments and have revised the manuscript effectively. The improvements are well-executed and have strengthened the overall quality of the work. However, I recommend that the manuscript be formatted to align with standard journal conventions. In particular, the figures should be placed within the text, close to where they are referenced, to improve readability and coherence.

Moreover, the overall structure and sectioning of the manuscript should be more academic in nature. The authors are encouraged to incorporate relevant subsections where appropriate, which will help organize the content more clearly and guide the reader through the progression of the study.

Author Response

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Author Response File: Author Response.docx