reviews: 
Comments: This paper proposes a commonsense reasoning layer to advance the autonomy of self-driving vehicles, aiming for SAE Level 5 autonomy. It addresses limitations in machine learning-based models by incorporating commonsense reasoning to handle ambiguous scenarios. Two primary cases are explored:

 - Malfunctioning traffic lights at intersections.
 - Unexpected obstacles on the road.

The authors demonstrate that combining deep learning with commonsense reasoning improves decision-making accuracy in the CARLA simulator environment. Uncertainty quantification activates the commonsense layer only when needed, optimizing computational efficiency for real-time systems. This approach showcases how human-like reasoning, structured as a logical model atop a deep learning model, enhances the accuracy of perception tasks in self-driving vehicles.
Strengths: The paper presents an approach for self-driving vehicles to interpret and respond to complex situations by integrating commonsense reasoning, which mimics human-like decision-making processes. Additionally, the scenarios where this approach is applicable are well explained, helping readers easily understand how commonsense reasoning can enhance decision-making in ambiguous or novel driving situations.

- The uncertainty-based approach is efficiently applied, simultaneously improving computational efficiency and decision-making accuracy, both vital for real-time applications. By activating the commonsense layer only in high-uncertainty situations, the method optimizes computational resources, which is essential for system deployment.
- The paper extends its application beyond the initial traffic light recognition and obstacle detection scenarios, suggesting that the commonsense reasoning approach could generalize to handle diverse driving situations.
- The choice of Evidential Deep Learning (EDL) for uncertainty quantification is well-motivated by computational cost considerations. EDL is advantageous in scenarios where real-time efficiency is critical, as it allows uncertainty estimation in a single forward pass without requiring multiple model evaluations.
Weaknesses: The lack of explicit details on the logic programming rules makes it difficult to verify or reproduce the commonsense reasoning layer’s effectiveness, posing a challenge for reproducibility. Additionally, clear architecture diagrams are missing, making it harder to visualize the system’s workflow and understand how each component interacts.

- Rule-based reasoning generally lacks flexibility in handling unexpected and highly complex situations, as fixed rules can be rigid and challenging to generalize for unpredicted events.
- The use of Uncertainty Quantification primarily to invoke the commonsense layer, rather than directly contributing to the reasoning process, somewhat limits the integration depth of UQ with the logic layer.
- **The assumption that commonsense reasoning reflects human-like decision-making may not hold in scenarios where all vehicles in the environment are autonomous, raising questions about the relevance of “commonsense” when collective behaviors are not human-driven.**
Recommendation: 5
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 2