reviews: 
Comments: The paper attributes the lack of SAE Level 5 autonomous vehicles on the market primarily to an over-reliance on machine learning. It proposes incorporating automated commonsense reasoning using hand-coded logic programs alongside existing ML models, as a solution. To demonstrate the benefits, the paper presents two scenarios using the CARLA simulator.
Strengths: The paper highlights alternative approaches to address the challenges of out-of-distribution data in machine learning models. It conducts tests and analyses on standard evaluation settings and provides source code for the experiments as supplementary material.
Weaknesses: * **W1:** While the methodology of using logic programs to safeguard against unsafe decisions is noteworthy, similar approaches, such as shielding mechanisms to safeguard against unsafe actions, have been central to planning under uncertainty in works published around 2015–2020 (see references below)

* **W2:**  There are several approaches to applying commonsense reasoning. A significant limitation of logic program-based approaches has been their restricted generalization to unseen scenarios. As a result, the question of how these methods can be effectively used for plausibility testing or achieving system self-awareness remains open. The paper does not offer a new perspective on this issue. Recently, using Large Language Models has emerged as a promising approach (see reference below).

* **W3:** In the experiments section, comparisons with additional state-of-the-art approaches would enhance the evaluation, rather than relying on a single baseline.

* **W4:** The presentation style could be enhanced to increase information density, particularly in Section 3, "Motivation." Textual comparisons with human behavior could be replaced with logical formulas. Additionally, explanations about BEV (Lines 208-213) can be omitted. Details on CARLA’s sensor configurations, parameter values, and API commands could be moved to the appendix, as they do not contribute methodologically.

* **W5:** The "General Procedure" section could be specified using variables and mathematical notation, which would facilitate analysis of bounds and enable a clearer abstraction of the example scenarios. The textual description could also shift from a reporting style to an analysis that examines causal relationships between design choices and outcomes. Additionally, the sentences in lines 503-506 could be revised for improved flow.

* **W6:** The related work section may benefit from organization into subcategories, such as approaches for commonsense reasoning, uncertainty quantification, and self-awareness. Capitalization rules should be observed in the bibliography entries.

1. Shalev-Shwartz et al. "On a formal model of safe and scalable self-driving cars", 2017.
2. Tas et al. "Automated vehicle system architecture with performance assessment", 2017 
3. Hess et al. "Formal Verification of Maneuver Automata  for Parameterized Motion Primitives", 2014
3. Alshiekh et al. "Safe Reinforcement Learning via Shielding", 2018
5. Sinha et al. "Real-Time Anomaly Detection and Reactive Planning with Large Language Models", 2024
Recommendation: 1
Reviewer confidence: 3
Soundness/correctness: 2
Clarity: 1