Venue: CoRL 2022 Conference

Reviews:

final_score: rating: Weak Accept: I recommend accepting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary: This paper introduces a sensor-fusion approach that provides interpretable intermediate representations of the world scene. The approach can fuse multi-view RGB images along with LiDAR scans. The network architecture involves a CNN backbone which feeds into a transformer encoder for fusion. A transformer decoder generates an object density map, a set of waypoints for the ego, and a set of rules to be enforced (such as traffic lights) which are then fused in a safety controller to generate open-loop actions.
strengths_and_weaknesses: Strengths:
1. I like the idea of training the perception pipeline with the planning and control modules in tow. This would allow the perception pipeline to extract the features that are relevant to behavior generation and control.
2. The approach provides interpretable perception outputs which would be a great asset for verification of the correctness of the decisions made by the downstream planning and control modules, modulo the correctness of the perception outputs.
3. The experimental results are strong (first rank in CARLA leaderboard in driving score) and thorough (extensive ablation studies that do give reasonable insights such as the importance of fusion).

Weaknesses:
1. In a simulator, the data collection for training was easy because direct access to the scene ground truth is available. However, perfectly annotating the object density map for training from real driving logs will be very challenging.
2. The planning and control modules in an actual AV might be significantly more complex than the safety controller used in this paper with multiple layers and potentially non-differentiable components.
3. The paper suffers from some grammatical errors which can be fixed.

Clarification question:
1. In the context of control, line 109 says: “rule-based methods hardly scale to complex environments due to the extensive human labor required.” What human labor is being discussed here? Also, could you cite a reference which suggests rule-based methods fail to scale. This paper (Helou, Bassam, et al. "The Reasonable Crowd: Towards evidence-based and interpretable models of driving behavior." IROS 2021) seems to suggest otherwise.
2. Why does picking the local maximum for object probability in the map help with identifying objects with high position uncertainty (as suggested in line 198)?

robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: I recommend a Weak Accept. The paper is good and provides detailed experimental results. My reservations listed in weaknesses are of an “open-problems” nature.
issues: 1. Please clarify your comment about the scalability of rule-based control methods.
2. Please clarify why local max probability helps with objects suffering from high position uncertainty.
3. Please fix the grammar issues in the paper.
review_title: null

final_score: rating: Strong Accept: I recommend accepting the paper and will argue for my recommendation even if other reviewers hold a different opinion.
review_content: summary: The authors present an interpretable autonomous vehicle (AV) policy which features a sensor fusion transformer. The authors develop a transformer encoder which takes in which uses multiple camera viewpoints and LIDAR. Additionally, a transformer decoder which output waypoints, an object density map, and traffic rules in order to determine a nominal trajectory and use a safety controller to adjust the velocity of the planned trajectory. The resulting AV policy beats state of the art methods in CARLA leaderboard and benchmarks.

strengths_and_weaknesses: Strengths:
- Justification for methods are well argued and the paper is accessible 
- The results on the CARLA leaderboard and benchmarks are impressive and show the potential of the method for more realistic/larger scale settings. 
- The inclusion of code for the method is a great strength and I hope it is made public upon publication. This will allow follow up work to more easily compare to this strong method and in a straightforward way and extend portions of the method (such as with different safety controllers as the authors mention on L210-L11).

Weaknesses:
- The addition of an ablation study is important in understanding the importance of the choices the authors made in the resulting method. However, as it stands, the interpretation of the ablation study and tables 2 and 3 are quite confusing and I am not sure how to view the results. The authors seem to have a different interpretation of the table than I do, but this is possibly due to a mistake in the table.
- The overall approach is very hand-crafted and there are many possibly non-obvious choices which must be made. E.g. L554-L555 cyclists’ and pedestrians’ bounding boxes are scaled up but not vehicles’. I believe this is common for large-scale learning for AVs but this does somewhat weaken the approach. It is possible that the good performance is due to these many choices and the careful cost function shaping instead of the transformer and interpretable outputs. 
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: The method uses transformers in an AV sensor fusion problem and leverages them to provide useful, interpretable, intermediate features which are then used improve the safety of a planned trajectory. The results are impressive and show the strength and potential of the methods. The method seems easily adjustable to suit other problems and are amenable to follow up work. Therefore, I recommend acceptance.

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**Update after revisions and replies**: The authors have address all major issues and weaknesses. I continue to support acceptance.
issues: - Related work: The authors should include and distinguish their work from “End-To-End Interpretable Neural Motion Planner”, Zeng et al. CVPR 2019. The interpretable portion of this method is similar in concept to the one presented. 

- L218: The authors do not seem to elaborate on the rule-based expert used to generate the experimental data. Is the expert based on another paper? If not, what are the rules? How important is the performance of the expert to the downstream performance of InterFuser? Would an human driver expert be preferable because of the larger variety of behaviors?

- What is the inference time for the InterFuser? Can the method be used for real-time planning/control in AV settings? If not, I suggest including this in the limitations. 

- Table 2 and 3: I believe the authors have switched the route completion and infraction score column labels. If not, the interpretation of the tables 2 and 3 in L263-L288 seems incorrect. Additionally, if not, the authors need to explain why the infraction score gets worse when adding additional sensor inputs or when removing effective fusing techniques, which doesn’t make sense to me. 

- Table 2 and 3: It would be helpful to include other methods to compare to in Tables 2 and 3. If other methods outperform e.g. “concatenated input” in Table 3, then it’s clear the sensor fusion portion of InterFuser is vital to success. On the other hand, if they do not outperform concatenated input, then it may be that other choices in designing the system results in the better performance.

- Appendix C: The authors should consider including a section with training time, resources used for the results in the paper, and number of parameters in the InterFuser. Since AV networks can be extremely expensive to train and evaluate, it is important to consider the accessibility of such methods which require expensive computing equipment to train.
review_title: null

final_score: rating: Weak Reject: I recommend rejecting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary: This paper proposed a transformer fusion architecture for controlling autonomous driving agents. Various image and LiDAR inputs are processed by CNNs and fused by a transformer encoder, which is followed by a transformer decoder to output driving action and auxiliary outputs. Evaluation results on various benchmarks, including the public CARLA leaderboard, demonstrate the effectiveness of the proposal. 
strengths_and_weaknesses: Strengths: 

1. This paper covers an important topic. 

2. The proposal achieves impressive performance. 

3. The writing is generally clear and the diagrams are helpful for understanding the architecture. 

Weaknesses: 

My main concern about this paper is the lack of more careful analysis of safety and interpretability, the two main benefits claimed by this paper. These two concepts are very related in this paper, as safety is ensured by the "interpretable" intermediate outputs generated by the model, such as inferred traffic state information. 

However, since these outputs are generated by equally non-interpretable black box model (actually the same transformer decoder), so this set up seems more like an auxilliary loss setup, rather than providing any kind of interpretability or safety guarantee. 

For safety, it is mainly tackled by verification, as already discussed in the related work, or barrier functions [e.g. 1]. However, the safety notion strongly depends on the quality of the intermediate output prediction, which seems hard to offer any guarantee. 

On the interpretability side, I wouldn't call such an architecture interpretable. Specifically, it is not clear what additional benefits the "explanation" (i.e. intermediate outputs) offer, in terms of understanding the model. Because the explanation is generated along side the action predictions, they do not need to be coupled in anyway, so that, for example, the action could be the "drive forward" prediction even if a red traffic light is also predicted. For claims on interpretability, I would like to see some concrete evidence, such as helping with model debugging [2, 3], improving human-model collaboration [4], or some other use cases [5]. 

At the very least, some more careful analysis of the intermediate output is needed to understand when they can help and when they cannot. Then the lack of rigorous studies of safety and interpretability could be acknowledged in the limitation section. 

[1] https://arxiv.org/abs/2109.06689

[2] https://arxiv.org/abs/2104.14403

[3] https://openreview.net/forum?id=xNOVfCCvDpM

[4] https://arxiv.org/abs/2006.14779

[5] https://dl.acm.org/doi/10.1145/3511299
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: I believe that this paper could make an important contribution, but at this moment, neither of the central claims, safety and interpretability, are supported by strong empirical evidence. However, if such evidence is convincingly demonstrated, this paper could have potentially high impact. 
issues: See concerns in the weakness section.
review_title: null

Meta-review: metareview: This paper proposes a new sensor-fusion approach that provides interpretable intermediate representations of the world scene and a safety-enhanced feature for autonomous driving. The authors propose to fuse multi-view RGB images along with LiDAR scans. The feature extraction part is also enhanced with the planning and control modules. The reported experiment results are promising and strong, i.e. first rank in CARLA leaderboard in driving score, and accompanied with an extensive ablation studies. The justification for the method of fusing multi-view RGB images and LiDAR scans are well articulated. The authors have greatly clarified open questions from the authors regarding the safety and interpretability. These additional details would be very helpful to understand the paper and its impact, the authors should consider to add them to the final version or its appendix.

confidence: 4: The area chair is confident but not absolutely certain
Decision: Accept (Poster)