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: Although nowadays autonomous vehicles can handle most scenarios, their performance in a small number of safety-critical cases is not evaluated comprehensively. The main reason is that these cases are difficult to collect and thus the number is limited. This paper proposes a new generative method taking causal graphs as input to produce countless safety-critical scenarios, which could help tame the long tail problem and help improve the safety of ADS. Extensive experiments are conducted to verify the effectiveness the proposed method.
strengths_and_weaknesses: **Strength**:
1) This paper proposes a new graph-based scenario description tool so that causality could be integrated
2) The idea of generating scenarios from a causality graph is original and interesting
3) It seems that the authors give a solid convergence guarantee, based on the delicate notation. But I am not an expert at the graph model, and thus can not ensure the correctness of the modeling.
4) The experiment is complete, including baseline comparisons, ablation study (on COM and CVM) and RL experiments.

**Weakness**:
The weakest part of this paper is that the experiment setting should be made more clear and some qualitative results should be added. 

1) What do the scores in Table 1 mean? Is it the collision rate of the generated case?
2) What do the generated scenarios look like? Some qualitative results should be visualized to show the diversity of generated scenarios.
3) Are there only 3 synthetic maps? If not, please describe how many maps are contained in these simulators and how do they look like, since the map structure will greatly influence the generated scenarios. If these maps are synthetic, I would like to know if there are any future plans to extend this method to real maps, which are more complex and unstructured
4) The pre-defined types of nodes and edges used in the experiment should be made clear
5) In the RL experiments, are the safety-critical cases split into training and test sets? 




robotics_focus: Relevant but unlikely to deploy to hardware in near future
quality_of_the_limitations_section: Additional details required
summary_of_recommendation: I think the weakness mentioned above is easy to solve in revision. Therefore, I would like to give a weak acceptance.
issues: See weakness
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: This paper presents a solution to generating scenarios in an efficient manner (targeted to automated driving, but more generally applicable).  The authors present a means for leveraging causality, in particular causal graphs, in constructing a behavior graph that can serve to instantiate scenarios.  The sample efficiency comes from the behavior graph, which alleviates the need for all combinations of behaviors, even implausible ones, that may occur with standard generative models.
strengths_and_weaknesses: The paper is well-written, offering a unique take on an important problem of finding rare events and corner cases for AV safety in a principled way.  The presented approach seems to offer a definitive improvement over existing methods in terms of sample efficiency, in cases where causality can be leveraged.  The authors introduce a novel procedure for using AF to construct a behavior graph in generating two types of causal masks: a causal order mask, and a causal visible mask to properly impose causal ordering of nodes and edges.  The authors also include a well-written code artifact, which will certainly boost reproducibility.

One major weakness of the paper is the rather shallow descriptions motivating the various components, particularly in generating the causal graph and autoregressive flows.  It seems quite a bit counterintuitive that to generate a simulation of a rare event or rare scenario, that the details of such a scenario must already be specified by a human (i.e. the causal graph must be known).  Indeed, in all such scenarios It would be worth elaborating on why such a scheme helps, starting with some example scenarios.

There is also little background given on the choice of autoregressive flows, and no introduction to normalizing flows.  What are the redeeming properties of autoregressive flows?

Some further questions on the implications of using causal graphs.  For both the causal graph and behavioral graph, do they allow for two-way interactions between agents?  For instance, the case of two vehicles negotiating a merge?  From the definitions given, this does not seem to be the case.  More generally, the authors are strongly encouraged to note any inherent limitations in the causal graph that may make it impossible to form scenarios that may otherwise be possible using causality-free approaches.  Also, the node and edge types are also not clear - what do the authors mean by “type”?  Further explanations in the behavioral graph section (Section 2.1) should be added, and examples should be more complete.

While I acknowledge that the authors do address the causal graph requirement as a limitation, I would recommend that authors strongly consider adding an empirical exploration on the diversity of the generated scenarios on a limited set of causal graphs versus the diversity of causality-free methods.  In other words, how much diversity is lost by relying on users to supply a causal graph?
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Additional details required
summary_of_recommendation: Because this paper presents a novel approach to scenario generation, with clear empirical benefits, I would advocate for acceptance.  The algorithmic contributions make for a very strong contribution, as does the code that is released along with the paper. The authors should address the issues brought forth in this review; in particular, by better motivating the use of autoregressive flows and explaining further the limitations of having a causal graph, particularly in loss of scenario coverage (diversity).  Further empirical studies on the diversity issue are highly encouraged.
issues: 1. Addressing what redeeming qualities are for autoregressive flows over other types of normalizing flows, any background on why normalizing flows and its impact on likelihood computation, as this is not explained anywhere.
2. Regarding causal graphs, what are the limitations in terms of diversity of the scenarios if the user is expected to provide such domain-specifics?  The authors should provide an empirical justification for causal graphs, and improve the discussion on this topic.
3. The authors should address whether there are inherent limitations in the causal graphs and the generated behavior graphs.  For instance, are bi-directional interactions (one agent influences another, and vice versa) possible or not?
4. A better background and discussion on behavior graphs and causal graphs.  For instance, better descriptions of what "type" means in context, by way of better examples.
5. How are $V_i$, $E_{i,j}$ initialized in the algorithm 1?
6. There are a number of grammar mistakes. For instance, inconsistent references to figures ("Figure X" vs. "Fig X"), and sentence fragments ("... merges the naturalistic and collision datasets with conditional." on line 282).
review_title: null

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: The objective of this paper is to generate test cases for automated driving systems that present realistic and difficult situations (which are only rarely occurring under generic sampling methods).  The idea is to augment a generative model for sequences of important events with constraints from a causal model, thus generating more plausible scenarios that still result in a high probability of impact and that therefore represent good test problems.
strengths_and_weaknesses: The problem addressed by the paper is important and the ideas seem sensible.  In particular, this method provides us with a way of taking some human expertise (basic, generic causal structures) in a form that is easy for humans to supply and leveraging it to generate good test scenarios.

One possible objection is that this work is not too relevant to the field of robotics;  but it is relevant to the extent that automated vehicles are robots and that it is critical to train them on data distributions that exhibit important possible failure modes.   

An answer to this objection which (I think) would actually make the paper stronger, would be to assert that this work is important to any agent that is being trained via typical learning methods to take actions in a complex domain in which there are seriously risky situations that are relatively rare, and that these situations can come up in a wide variety of robotics applications (including industrial or household robots sharing workspace with humans, or robots working on disaster recovery.)

A primary difficulty with this paper is clarity and organization of the writing.  It is very important to describe, rigorously and early,   exactly what kind of testing data you will produce and how it is used.
- What exactly is the type of a scenario $\tau$?  Does it specify detailed trajectories for all the vehicles, stop-lights, etc?   Or all but the agent?
- How are the scenarios used during reinforcement learning?   Do we use the current RL policy $\pi$ inside $\mathcal E$ to generate new scenarios, and then do offline RL in those scenarios to generate a new policy?  
- Are the constraints from the causal graph imposed *at scenario generation time* or are they somehow incorporated into $\phi$ so that the underlying autoregressive generation process tends to generate graphs that satisfy them?

Here are some other questions that came up for me as I was reading, which might be addressable by rewriting, taking care to make sure the paper is readable by someone new to your work, defining terms before using them, etc.
- What does it mean for a graph to "work as a high-level planner"?
- How does the behavioral graph encode the ordering of actions given that there can be parallel self-loops?   How do you guarantee the actions are legal?
- It would be better to explain the behavioral graph (semantics and use) before talking about how to generate them.
- Also, explain to us how the behavioral graph will help generate rare-but-interesting scenarios.  (I finally understood this later in the paper)
- 2.2 Say earlier that G^B is a DAG.
- Are CG and BG the same type?  What does it mean to minimize distance between them?
- How can a node be directed?
- The description of causal visible masks could be clearer.  It "filters the non-causal information when generating edges"  But what does it
mean to do that?
- Doesn't the likelihood of collision depend on the policy being executed?  I guess you are going to do this dynamically?  Given a
policy, generate scenarios that cause crashes, use those scenarios to improve your policy, etc.?
- It is generally good practice to only put into bold the method(s) that are statistically significantly best or indistinguishable from best (not just with the highest mean)
- Are these scenarios necessarily interesting if they cause a simple rule-based policy to crash?  But I guess the idea is that this method can be used
to cause other policies to crash, as well?
- I like that you put in a pedagogical figure (2) but for it to actually teach, it would need more explanation.

The study on irrelevant nodes was good and illuminating.

It would be great to explain more fully the importance of having monotonicity of likelihood.

This whole framework of auto-regressive graph generation is very cool, and adding causal constraints is a nice idea.   Might there be other kinds of constraints that humans could specify (or "skeleton" scenarios) that could also be incorporated in this framework?

-------------------
After discussion, I feel that:
- the exposition is improved but could probably be made more systematic still
- the solution really is interesting and feels novel
- I still don't quite understand what you mean by:
"Nodes in behavior graph represent objects we are interested in, e.g., vehicles, pedestrians, traffic signs, traffic lights, etc. Edges in represent the properties of objects, e.g., initial positions, velocities, and colors. Therefore, the behavior graph describes the sequential interaction between objects, which can be viewed as a motion planner."
To me, a "planner" takes some kind of goal or objective and some kind of transition model and does some search to find an action sequence that optimizes the objective.

I will improve my recommendation

robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: The ideas here seem interesting but are not really clearly enough articulated for me to have understood the proposed methods well enough for me to evaluate them.  I would encourage a rewrite of the paper.

issues: I wouldn't ask for any additional work during the revision period.  I think the rewriting required is fairly substantial.
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: The paper proposes CausalAF, a method to generate safety-critical scenarios by learning the behavior graph given the causal graph. The paper claims dramatic improvements on generating scenarios that meet the objective over data-driven baselines. Finally, they also show that the generated scenarios are beneficial for training RL policies in contrast to random scenario generation.
strengths_and_weaknesses: ### Strengths
- The introduction captures very well the state of the self-driving industry. Scenario generation is a highly relevant task at the moment towards solving self-driving.
- The paper is well structured and the manuscript is fairly polished.
- Representing scenarios in a graphical way makes sense, and learning behavior graphs that satisfy the causal structure is a relevant problem.
- It is good to see that causality helps the generation process as covered in L238-251.

### Weaknesses
- Adversarial optimization is not treated as a first-class baseline. This is a constant throughout the introduction, the related work as well as the experiment. However, I believe that with the objective described in L160, methods such as STRIVE, KING and AdvSim (see citations below) would do extremely well. Unfortunately, such comparisons aren't available in the paper.
- In L82 it says that the edge matrix represents the sequential interaction between objects. I didn't understand what's the sequential part. As far as I understand, the only attrributes getting optimized are the initial position and velocity. This is what I understand from L89.
- It is not clear what is the exact parameterization/architecture of $\mathcal{F}_\phi$. Can it handle a variable number of actors in the scenario? Or a different module needs to be trained for every scenario?
- Expert knowledge and effort is required to provide the causal graph. While it is interesting to include prior knowledge in the problem, this route seems very time-consuming and that it will not generalize to more complex scenarios. While the paper acknowledges this limitation, I believe it hinders the applicability far too much for this work to be useful as it stands.
- The scenarios are extremely simple, and the baselines perhaps too weak. In terms of scenario generation, I'd be interested in seeing a comparison against adversarial optimization as well as simple heuristics based on time-to-collision in Table 1. Moreover, in Table 2 the impact of the generated scenarios in training RL policies is only compared to a random baseline, as opposed to comparing to the other baselines for scenario generation.
- Finally, in the limitations section it is naively stated that the sim-to-real gap is "almost negligible". The paper doesn't provide any support for this argument, and I believe is not true.

```
@inproceedings{rempe2022generating,
  title={Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior},
  author={Rempe, Davis and Philion, Jonah and Guibas, Leonidas J and Fidler, Sanja and Litany, Or},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={17305--17315},
  year={2022}
}
@article{hanselmann2022king,
  title={KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients},
  author={Hanselmann, Niklas and Renz, Katrin and Chitta, Kashyap and Bhattacharyya, Apratim and Geiger, Andreas},
  journal={arXiv preprint arXiv:2204.13683},
  year={2022}
}
@inproceedings{wang2021advsim,
  title={Advsim: Generating safety-critical scenarios for self-driving vehicles},
  author={Wang, Jingkang and Pun, Ava and Tu, James and Manivasagam, Sivabalan and Sadat, Abbas and Casas, Sergio and Ren, Mengye and Urtasun, Raquel},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9909--9918},
  year={2021}
}
```
robotics_focus: Relevant but unlikely to deploy to hardware in near future
quality_of_the_limitations_section: Limitations are not well addressed
summary_of_recommendation: While I think the idea has merit, I believe removing the need for the expert-provided causal graphs and making more thorough experiments are a must to accept this paper. The baselines the method is compared against are weaker than they should.
issues: See weaknesses section.
review_title: null

Meta-review: metareview: Below is a summary of the strengths and weaknesses. Please see the reviews for more information, as they provide detailed feedback both on these points and on other points.

Strengths:
- The problem setting is interesting and relevant to the CoRL community (though the paper could also convey how these ideas may be relevant beyond autonomous driving to any robot that may encounter risky situations that are rare)
- The method is sensible and provides a way to translate human expertise into test scenarios
- Some of the technical ideas, such as autoregressive graph generation, are new and interesting.

Weaknesses:
- The comparisons provided in the paper are weak, and not representative of the state-of-the-art. (see review Gk4u)
- While some reviewers found the paper to be well-written, others identified issues with the clarity of the writing, especially around the assumptions made
- Several design choices are not motivated in the text

----

The authors provided a solid response that addressed some of the reviewer's concerns. There are some concerns that remain, e.g. around the task set-up from review Gk4u and as mentioned in the updated review pK7P, which I encourage the authors to improve for the revised version. Nonetheless, the paper makes an interesting contribution to CoRL.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Accept (Poster)