Entropy-Based Uncertainty Modeling for Trajectory Prediction in Autonomous Driving
Autonomous Driving;Trajectory Prediction;Uncertainty Quantification
Yes
No ethics review needed.
1. The paper would be strengthened by including examples demonstrating how uncertainty analysis can directly contribute to reducing prediction error.
2. The paper could be strengthened by providing justification for how its findings on certain models might generalize to other model architectures.
1. The paper is well-presented, with a clear structure and highly informative images that effectively support the content.
2. The paper has comprehensive coverage of relevant prior work and contextual background.
This paper explores uncertainty modeling in trajectory prediction for autonomous driving, utilizing information-theoretic approaches to decompose uncertainty into aleatoric and epistemic components. Experiments are conducted on the nuScenes dataset to analyze correlations between prediction errors and various uncertainty types. Additionally, the paper examines model robustness in out-of-distribution (OOD) scenarios and investigates techniques for effectively detecting OOD scenarios.
1. The primary critique of this paper is that, despite its thorough analysis of uncertainty decomposition, the relationships between prediction errors and uncertainty components, and so on, it fails to address how these insights could be leveraged to reduce prediction errors. While uncertainty decomposition provides valuable interpretative insights, the autonomous driving community is more focused on actionable strategies for minimizing prediction error. Unfortunately, the paper does not propose any methods to achieve this.
2. In Section 3.4, the paper leverages uncertainty to identify out-of-distribution (OOD) scenarios, claiming that "it facilitates the collection of challenging cases for re-training and evaluation." However, in the autonomous driving community, practitioners typically focus on scenarios with the highest prediction error. The paper would benefit from justifying why using uncertainty to identify OOD scenarios offers advantages for training and evaluation compared to simply targeting high-error scenarios.
3. The paper’s analysis is limited to a selection of trajectory prediction models and their ensemble versions, making it difficult to generalize the findings to other model architectures.
Yes
No ethics review needed.
1. In table 1, why is $3\times LP$'s total uncertainty has lower correlation with the prediction error than its individual components? Can you provide some insights for it?
2. is this proposed approach a means for evaluating model uncertainty? Any other real-world applications for this method?
3. As most of the findings are aligned with the known conclusions from the existing researches, is this approach simply for ensuring the conclusions are correct?
1. It is interesting to see the idea of quantifying and decomposing the uncertainty of trajectory prediction models.
2. The experiments results show that most of the findings through the proposed approach aligns with the existing research conclusions.
This paper claims to propose a method to measure and decompose the uncertainty of trajectory prediction models, analyze the relationship between uncertainty and prediction error when the scenarios are either in-distribution or out-of-distribution, study how different configurations of ensemble compositions affect the uncertainty quantification and model robustness.
The biggest concern here, as what it is mentioned in the discussion, is the memory and computational burden. The inference time to sample from the ensembles, GPUs, memory usage used in the experiments are not reported. Even if we consider the distilling ensembles into one model, it also takes extra computations.
Yes
No ethics review needed.
**Questions:**
* **Q1:** The main baseline you compare your approach against is Robust Imitative Planning (RIP). Because that approach was developed for another dataset, can you clarify how exactly you used that approach in your test cases (as mentioned in **W2b**)?
* **Q2:** How reproducible is your approach? Did you conduct any hyperparameter analysis, or do you plan to open-source your implementation?
* **Q3:** You presented results for minADE, and I would expect similar trends for minFDE. If you analyzed the correlation and results for minFDE, including these or summarizing them in a single sentence could provide valuable insight for some readers.

**Improvement suggestions:**
* **S1:** Make a clear distinction throughout the text between the contributions of this work and elements borrowed from "Motion Forecasting via Model-Based Risk Minimization". One example is line 237.
* **S2:** Lines 245-249 disrupt the overview flow and should be revised for clarity.
* **S3:** In abstract you state: "_To ensure safety, planners must rely on reliable uncertainty information about the predicted future behavior of surrounding agents, yet this aspect has received limited attention. This paper addresses the so-far neglected problem of uncertainty modeling in trajectory prediction._" However, there are many approaches that address perception and prediction uncertainties in the environment. There are also dozens of approaches that predict the uncertainty, though often using a different method than yours. Therefore, these sentences must be revised for clarity.

**Minor suggestions:**
* **M1:** Consider adding a clear statement on whether the uncertainty quantification impacts prediction accuracy.
* **M2:** You used MBRM to sample trajectories from an ensemble of prediction models. Does your uncertainty quantification approach impose any specific requirements on the prediction model used? For instance, is fitting a Gaussian Mixture Model (GMM) necessary? Additionally, can this approach be used with arbitrary prediction methods?
* **M3:** Adding N. Kose, "Reliable Multimodal Trajectory Prediction via Error Aligned Uncertainty Optimization," ECCV, 2022, to related work might be helpful.
* **M4:** One last point: application-focused papers are typically expected to demonstrate a quantifiable, measurable advantage over baselines. For example, as in RIP, applying a planning benchmark to show how improved epistemic uncertainty estimates enhance the robustness of baseline planning algorithms could be beneficial. While not strictly necessary, such an analysis would strengthen the paper by providing concrete evidence of practical benefits.
The authors show that by separating aleatoric uncertainty, they can achieve a more precise estimation of epistemic uncertainty, which generalizes well to motion forecasting. Validated on the nuScenes dataset, the approach quantifies uncertainty across both in-distribution and out-of-distribution scenarios. In out-of-distribution scenarios, the authors show that mixed ensembles, which combine various model architectures, enhance reliability.

The proposed approach is evaluated across various scenarios, with additional results for different model variations provided in the appendix.
The paper focuses on quantifying and decomposing uncertainty into epistemic and aleatoric components for trajectory prediction in autonomous driving. It measures aleatoric uncertainty using conditional entropy and epistemic uncertainty through mutual information. The authors employ a Monte Carlo approximation to estimate these uncertainties and evaluate their approach on the nuScenes dataset, comparing it against a baseline method that quantifies only epistemic uncertainty.
* **W1:** The authors state that they believe they are the first to thoroughly investigate quantification of uncertainties for trajectory prediction models. However, after a quick research, I found that M. Itkina, "Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction", CoRL, 2022 also break down prediction uncertainties in aleotoric and epistemic components. Could you compare your approach to Itkina et al.'s method, highlighting key differences or improvements?

* **W2:** The paper compares the proposed method against only one baseline: Robust Imitative Planning (RIP) by Filos et al. "Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?", 2020. RIP is a heuristic-based approach, yet the paper lacks details on how RIP was used. Given that RIP was originally developed for a different dataset, its default parameters may not be optimally suited for the test cases used here.

  * **W2a:** Together with **M4** below, comparing your proposed method on the CARNOVEL benchmark could highlight its contribution.

  * **W2b:** Please specify how RIP was adapted or tuned for the dataset used in this study.

* **W3:** The underlying prediction work of this approach, Model-Based Risk Minimization (MBRM), is a work currently under review for IEEE ICRA 2025. MBRM uses the same ensembles (deep ensembles and dropout ensembles) used in this work. While a clear differentiation in the paper body is lacking, the main differences between this work and MBRM appear to be the use of Monte Carlo-based uncertainty estimation and the inclusion of OOD scenarios.

* **W4:** The analysis in Section 3.4, 'Detecting OOD Scenarios,' lacks a clear takeaway. While it shows that the mixed deep ensemble of LAformer, PGP, and LaPred assigns higher epistemic uncertainty to out-of-distribution datasets, a comparison with common OOD detection baselines (e.g. Mahalanobis distance, ensemble disagreement, or AUROC) would clarify the effectiveness of the proposed approach.

The novelty of this work appears incremental, primarily applying existing ideas to motion prediction rather than introducing new concepts or hypotheses. Additionally, the paper does not focus on representation learning, and its quantitative comparisons with baselines lack depth.
Yes
No ethics review needed.
This paper primarily utilizes the nuScenes dataset for experiments. Could the authors comment on the generalizability of their approach when applied to other datasets that may have different characteristics or geographical locations?
This paper mentions the increased computational burden of the proposed method. What specific strategies are being considered to optimize computational efficiency, especially for real-time applications?
This paper could benefit from experimenting with a broader range of neural network architectures. Have the authors considered or tested other architectures, and if so, what were the findings?
This paper could be strengthened by a more rigorous theoretical analysis of the uncertainty decomposition process. Are there any ongoing efforts to develop a more formal theoretical framework?
This paper suggests the potential integration of the uncertainty model with planning systems. Could the authors elaborate on any initial experiments or simulations that demonstrate this integration?
This paper mentions the application of uncertainty estimates in risk-sensitive reinforcement learning. Could the authors provide more details on how these estimates could inform decision-making processes under risk?
This paper presents a pioneering approach to uncertainty quantification in autonomous driving trajectory prediction by introducing an information-theoretic framework that decomposes total uncertainty into aleatoric and epistemic components. This method, supported by extensive experiments on the nuScenes dataset, not only enhances the safety and reliability of autonomous vehicles but also advances the field by providing a more nuanced understanding of prediction uncertainties, which is crucial for making informed decisions in complex traffic scenarios.
This paper presents an entropy-based approach for modeling uncertainty in trajectory prediction for autonomous driving, leveraging an information-theoretic framework to decompose total uncertainty into aleatoric and epistemic components. It conducts extensive experiments on the nuScenes dataset, analyzing the relationship between uncertainty and prediction error, and evaluates model robustness in both in- and out-of-distribution scenarios, contributing a novel method for assessing and improving the reliability of trajectory prediction models in autonomous vehicles.
This paper on entropy-based uncertainty modeling for autonomous driving trajectory prediction presents a significant advancement with its information-theoretic approach to quantify and decompose uncertainty, but could be further strengthened by expanding experimental validation to more diverse datasets, exploring additional model architectures, addressing computational efficiency, deepening the theoretical foundation, and demonstrating practical integration with planning systems to enhance its applicability and impact in the field.
@inproceedings{
anonymous2024entropybased,
title={Entropy-Based Uncertainty Modeling for Trajectory Prediction in Autonomous Driving},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=RflvsSxM0u},
note={under review}
}
In autonomous driving, accurate motion prediction is essential for safe and efficient motion planning. To ensure safety, planners must rely on reliable uncertainties in the future behavior of surrounding agents, yet this aspect has received limited attention. This paper addresses the problem of uncertainty modeling in trajectory prediction. We adopt a holistic approach that focuses on uncertainty quantification, decomposition, and the influence of model composition. Our method is based on a theoretically-grounded information-theoretic approach to measure uncertainty, allowing us to decompose total uncertainty into its aleatoric and epistemic components. We conduct extensive experiments on the nuScenes dataset to assess how different model architectures and configurations affect uncertainty quantification and model robustness. Our analysis thoroughly explores the uncertainty quantification capabilities of several state-of-the-art prediction models, examining the relationship between uncertainty and prediction error in both in- and out-of-distribution scenarios, as well as robustness in out-of-distribution.
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Autonomous Driving Trajectory Prediction Uncertainty Quantification
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applications to robotics, autonomy, planning
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Entropy-Based Uncertainty Modeling for Trajectory Prediction in Autonomous Driving
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