By Anonymous User
Review Details
Reviewer has chosen to be Anonymous
Overall Impression: Good
Content:
Technical Quality of the paper: Average
Originality of the paper: Yes, but limited
Adequacy of the bibliography: Yes
Presentation:
Adequacy of the abstract: Yes
Introduction: background and motivation: Good
Organization of the paper: Satisfactory
Level of English: Satisfactory
Overall presentation: Good
Detailed Comments:
- The authors contribute to the field of Neuro-Symbolic (NeSy) AI by investigating its application within the traffic domain, specifically for posthoc prediction tasks.
- The authors have developed a taxonomy of traffic tasks categorized into safety, perception, and inference, applicable to both autonomous vehicles and traffic monitoring systems.
- They have also explored how NeSy AI can enhance robustness and explainability by integrating various knowledge types, such as traffic rules, commonsense knowledge, and causal reasoning.
- The authors highlighted the potential of NeSy reasoning to improve multimodal data fusion, support coherent explanations, and facilitate generalization to novel situations.
- The paper also identifies challenges in data quality, the brittleness of rule-based systems, and integration complexity between neural and symbolic components, which can limit the adaptability and robustness of NeSy approaches in real-world traffic scenarios.
- This paper is acceptable but could be improved by expanding on practical applications, such as providing case studies to illustrate the real-world impact of the proposed taxonomy and methodologies.
- Additionally, a more detailed explanation of the role of commonsense knowledge and how it is structured within traffic tasks would enhance reader understanding.
- Finally, discussing specific integration techniques for combining neural and symbolic components would offer practical guidance for implementation.
- Despite these areas for improvement, this paper is considered a solid position paper that lays the groundwork for future developments in Neuro-Symbolic AI for traffic systems.
