Kati8/ICLR_2023_2025 huggingface dataset:
authors: Keegan Kimbrell
authors: Tianhao Wang
authors: Gopal Gupta
authors: Feng Chen
reviews: 
Comments: The paper introduces a novel approach to achieving fully autonomous driving by combining deep learning with commonsense reasoning. 

It proposes a hybrid architecture with a base layer using deep learning for environmental perception and target detection, and an upper layer using “commonsense reasoning” to identify and correct misclassifications made by the deep learning models. 

The effectiveness of this approach is demonstrated in scenarios： 1. traffic light failure and 2. unexpected obstacles on the road. 

The paper claims that by integrating commonsense reasoning with deep learning, the robustness and safety of AD systems can be significantly enhanced. Authors claims they offer a promising path towards achieving fully autonomous driving.
Strengths: I do believe that it would be beneficial for the AD system to incorporate elements of human common sense and reasoning. 

The paper provides a way for efficiently invoking commonsense reasoning by measuring uncertainty in the computer vision model and using commonsense reasoning to handle uncertain scenarios.
While individual components in the paper are not novel, the attempt to bridge the gap between deep learning approaches and human reasoning capabilities is interesting. 

The implementation on the CARLA simulator enhances reproducibility, further supported by the authors' commitment to open science through their detailed documentation and open-sourced code.
Weaknesses: - The motivationof the paper seems not convincing. The example in Figure 1, which highlights errors in traffic light recognition, seems outdated. Current state-of-the-art end-to-end systems have largely addressed this kind of issue with high accuracy. The authors should focus on more prevaling challenges within AD, such as complex urban scenarios or interactions with non-standard road users.

- The method of observing surrounding vehicles to infer basic information is not novel. Many existing AD methods already incorporate similar strategies for behavior prediction. The paper should clarify how their approach extends beyond observational methods to real "commonsense" reasoning.

- The experiment section lacks clarity, particularly regarding the baseline model used. The paper should provide detailed descriptions of the baseline architecture, training procedures, and performance metrics. Additionally, the integration mechanism between the commonsense reasoning layer and the baseline detection model needs to be clearly explained, including how these components interact and resolve potential conflicts.

- Besides, the paper lacks a comprehensive literature review. Given the rapid advancements and extensive research in this field, a more up-to-date thorough engagement with existing literature is necessary.
Recommendation: 3
Reviewer confidence: 4
Soundness/correctness: 1
Clarity: 2