reviews: 
Comments: The authors apply a set of hand-crafted, logic rules to a BEV segmantic map. In case the rules are not consistent with the segmantic map, a correction is triggered, that makes the scene consistent again.The approach is evaluated in two different scenarios, using various CARLA towns. The authors also analyze the effect of using uncertainty as a metric to determine if the rule correction is possible relevant.
Strengths: Handling of corner cases, as considered by the authors, is a long standing problem in autonomous driving. Till now, no generalized solution is available and rule-based approaches offer an interesting path, as they are interpretable, as opposed to common (black box) deep learning models. Therefore, additional insight into this class of methods is significant. Textually, it is clearly written but lacks structurally (see weaknesses).
Weaknesses: The most substantial weakness concerns details wrt. the method and the experiments. In general, it should be state which factoids can be used and if the rule system allows for temporal logic. A flowchart and/or example would also greatly help to understand the details of the method.

Concerning the experiments, it needs to be clarified how the used rules look like, what exactly the prediction (evaluation) target is and how parameters (like thresholds) are selected. Furthermore, the experiments are currently not reproducible, because additional information is also missing. As example, how the weather variations are defined and how to interpret the "number of cars" (cars per ... ?)
Furthermore, it seems the experiments are designed to align with experiment setup, meaning that the used rules and uncertainty type are always correct. E.g. what happens when a convoy exits a highway? Additional studies are also required to be able to judge the results in a meaningful way: A comparison to baselines (like the ones state in related work) and relations between the number of other vehicles and the effectiveness of the methods (required levels of local density & consensus). Additionally, the evaluation seems to be performed on perception level, but it should be performed on policy level, because the stated issues can be resolved with policies, without a correct perception.

Another problem concerns hidden assumptions: The rules are assumed to be perfect, which is likely not possible in reality. Additionally, the authors assume that it is possible to hand-craft a sufficient number of rules to cover most/all relevant corner cases. This is also not viable. Of course, there are ways to possibly automatize but this is not discussed and a comparison with existing, automatized methods is also missing.

These issues also reoccur in some claims, that need to be substantiated: This approach can likely not lead to 100% accuracy, because there is not even 100% agreement between expert drivers. Why is high level reasoning/system 2 required? End-2-End stacks are arguably not working like this. Why must the system comprehend and reason like a human would?

Lastly, the structure of the paper makes it difficult to follow: The methology sections contains experiment settings, related work is only discussed in the very end. Background and Introduction are mixed. Furthermore, some references are missing (e.g. resnext50)
Recommendation: 1
Reviewer confidence: 3
Soundness/correctness: 1
Clarity: 2