Venue: CoRL 2023 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_of_paper: This work proposed a rule-based trajectory predictor using rule hierarchies that achieved comparable performance to Trajectron++. Then the authors proposed the multi-predictor fusion (MPF) algorithm, which fuses the learning- and rule-based predictors using recursive Bayesian multi-modal filtering. It was shown that MPF achieved more consistent performance across different prediction metrics than the learning- and rule-based predictors.
review: **Strength**: The proposed rule-based planner based on rule hierarchy is interesting. It is quite impressive that it achieved performance comparable to Trajectron++. 

**Weakness**:  A very relevant and crucially important prior work [1] was missing in the related work. In [1], various methods to fuse learning- and model-based prediction models were proposed and evaluated, including a recursive Bayesian inference scheme that is very similar to the one proposed in this work. Thus, I would not consider the idea of predictor fusion and the proposed fusion scheme, which are claimed as the main novelty of this work, as novel and significant. The authors should compare their work with [1] and justify the novelty of their work during rebuttal. 

[1] Sun, L., X. Jia, and A. Dragan. "On Complementing End-To-End Human Behavior Predictors with Planning." Robotics science and systems (RSS). 2021.
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Additional details required
summary_of_recommendation: The most critical concern I have regarding this paper is the novelty and significance of the predictor fusion scheme. Thus, I lean towards rejecting this paper, given its current form. I am happy to raise my score if the authors can properly address my concerns during rebuttal.
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_of_paper:  The paper presents an algorithm called multi-predictor fusion (MPF) that enhances the performance of learning-based trajectory predictors for the safe and efficient planning of autonomous vehicles in highly interactive traffic scenarios. MPF combines learning- and rule-based predictors by mixing trajectories from both predictors in accordance with a belief distribution that reflects the online performance of each predictor. The proposed algorithm is shown to outperform the two standalone predictors on various metrics and delivers the most consistent performance in the results. The authors demonstrate that MPF augments the performance of learning-based predictors by imbuing them with motion planners that are tasked with satisfying logic-based rules.
review: Strengths:

* The paper is well-written and easy to follow.
* The proposed multi-predictor fusion module can be used with any off-the-shelf predictor, making it easily applicable to a variety of scenarios.
* The idea of combining learning-based and rule-based motion predictors is beneficial to real-world robotics applications.

Weaknesses:

* Computation expense may be a key problem to solve for real-world robotics applications, as the proposed method may require significant computational resources.
* The RH predictor does not exhibit multimodal behavior, which may limit its effectiveness in scenarios where multiple outcomes are possible.
* The paper would be better to address how the rule-based motion predictor would perform in scenarios where an HD map is not available, such as in a large intersection. This limitation should be discussed in the paper.

robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: Based on the strengths highlighted in the review, it appears that the paper proposes an interesting idea that may have an impact on real-world robotics applications. Therefore, I recommend accepting the paper for publication in CoRL. However, the weaknesses and concerns raised in the review should be addressed in the final version of the paper.
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_of_paper: An algorithm called multi-predictor fusion (MPF) that augments the performance of learning-based predictors is presented. MPF probabilistically combines learning- and rule-based predictors, by essentially mixing trajectories from both standalone predictors in accordance with a belief distribution that reflects the online performance of each predictor. It is shown in the paper that MPF outperforms the two standalone predictors on various metrics and delivers the most consistent performance.
review: I found that the paper is well developed, with clarity and sufficient contents, to describe the concept and to illustrate the performance of the proposed algorithm The topic is of great interest in the community, and the contributions of the paper are significant to the field.
robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: I have some background in the field and feel that the paper is of significant contribution to the filed and community.
review_title: null

Meta-review: The paper presents a very interesting idea for a tile-based method for trajectory prediction that is comparable to trajectron++. All reviewers agreed on the strengths of the paper on the multi-predictor fusion module that can be used with any off-the-shelf predictor, making it a flexible solution with a possible impact on real-world robotics applications. The updated version of the paper has also integrated well most of the reviewers feedback. I would suggest that the authors rearrange some experiments in their camera ready version to better highlight their contributions and findings.
Decision: Accept (Poster)