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MUSES: A Multi-Sensor Dataset for Semantic Perception in Adverse Driving Conditions


Core Concepts
MUSES is a novel multi-sensor dataset that enables research on robust semantic perception for autonomous driving in diverse adverse conditions, including fog, rain, snow, and nighttime.
Abstract

MUSES is a comprehensive multi-sensor dataset for semantic perception in autonomous driving. It includes synchronized recordings from a frame camera, lidar, radar, event camera, and IMU/GNSS sensor, captured under a variety of adverse weather and illumination conditions.

The key highlights of MUSES are:

  1. Multimodal data: MUSES provides synchronized recordings from multiple sensors, including a frame camera, lidar, radar, event camera, and IMU/GNSS. This enables research on sensor fusion for robust semantic perception.

  2. Diverse adverse conditions: The dataset covers a wide range of weather and illumination conditions, such as fog, rain, snow, and nighttime. This challenges perception models to perform well across diverse visual conditions.

  3. High-quality annotations: MUSES features high-quality 2D panoptic annotations with class- and instance-level uncertainty labels. This allows the novel task of uncertainty-aware panoptic segmentation, which rewards models that can accurately predict their own confidence.

  4. Benchmark tasks: MUSES provides benchmarks for semantic segmentation, panoptic segmentation, and the new uncertainty-aware panoptic segmentation task. These benchmarks can be evaluated using either unimodal (camera-only) or multimodal inputs.

The authors show that the additional non-camera modalities in MUSES provide significant benefits for dense semantic perception compared to camera-only approaches. They also demonstrate that MUSES presents a more challenging evaluation setup compared to existing datasets, motivating future research on robust and generalizable perception models for autonomous driving.

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Stats
The number of annotated pixels per class in MUSES ranges from around 10^6 for rare classes like train to over 10^10 for common classes like road. Approximately 24.5% of pixels are labeled as difficult_class, and 1.5% of things pixels are labeled as difficult_instance.
Quotes
"MUSES is the first adverse conditions-focused dataset that includes event camera or MEMS lidar data." "Our specialized annotation protocol leverages the respective readings of the additional modalities and a corresponding normal-condition sequence, allowing the annotators to also reliably label degraded image regions that are still discernible in other modalities but would otherwise be impossible to label only from the image itself." "MUSES opens up the following new research directions: (1) Sensor fusion for pixel-level semantic perception in adverse conditions, (2) exploring event camera utility in adverse weather for automated driving, (3) examining challenges and opportunities of new-generation automotive MEMS lidars for semantic perception, and (4) researching the novel uncertainty-aware panoptic segmentation task."

Deeper Inquiries

How can the multimodal data in MUSES be effectively leveraged to improve the robustness and generalization of semantic perception models across diverse visual conditions?

The MUSES dataset provides a rich multimodal framework that includes synchronized recordings from a frame camera, lidar, radar, event camera, and IMU/GNSS sensors. This diverse sensor suite can be effectively leveraged to enhance the robustness and generalization of semantic perception models in several ways: Sensor Fusion: By integrating data from multiple sensors, models can benefit from the complementary strengths of each modality. For instance, while frame cameras may struggle in low-light or adverse weather conditions, lidar and radar can provide reliable depth and distance information, enabling better object detection and segmentation. Event cameras can capture rapid changes in the scene, which is particularly useful in dynamic environments. Training on Diverse Conditions: MUSES includes samples recorded under various adverse conditions (e.g., fog, rain, snow, and nighttime). Training models on this diverse dataset allows them to learn robust features that generalize well across different visual scenarios. This is crucial for autonomous driving, where conditions can change rapidly and unpredictably. Uncertainty Modeling: The dataset's two-stage panoptic annotation protocol captures both class-level and instance-level uncertainty. By incorporating uncertainty quantification into model training, developers can create perception systems that not only make predictions but also assess their confidence in those predictions. This is vital for safe decision-making in autonomous driving. Benchmarking and Evaluation: MUSES provides a challenging benchmark for evaluating semantic perception models. By assessing model performance across different modalities and conditions, researchers can identify weaknesses and areas for improvement, leading to the development of more robust algorithms. Cross-Domain Adaptation: The high-quality annotations and diverse conditions in MUSES facilitate research into domain adaptation techniques. Models trained on MUSES can be evaluated on other datasets, allowing researchers to explore how well these models generalize to unseen conditions and modalities.

What are the key limitations and failure modes of current state-of-the-art semantic segmentation approaches when evaluated on the challenging MUSES dataset, and how can these be addressed?

Current state-of-the-art semantic segmentation approaches face several limitations and failure modes when evaluated on the MUSES dataset: Performance Degradation in Adverse Conditions: Many models trained primarily on clear-weather datasets struggle to maintain performance in adverse conditions. For instance, the drop in panoptic quality (PQ) observed when models trained on MUSES are evaluated under challenging conditions highlights this issue. To address this, models should be explicitly trained on multimodal data that includes adverse conditions, ensuring they learn to handle such scenarios effectively. Insufficient Handling of Uncertainty: Traditional segmentation models often do not account for uncertainty in their predictions. The MUSES dataset emphasizes the importance of uncertainty quantification, revealing that many models fail to provide reliable confidence estimates. Incorporating uncertainty-aware mechanisms, such as the novel uncertainty-aware panoptic segmentation task in MUSES, can help models better manage uncertain predictions and improve overall robustness. Limited Generalization Across Modalities: Models that rely solely on RGB images may not generalize well to other modalities like lidar or radar. The MUSES dataset's multimodal nature provides an opportunity to develop and evaluate sensor fusion techniques that can enhance model performance across different input types. Researchers should focus on creating architectures that effectively combine information from various sensors. Annotation Challenges: The two-stage annotation process in MUSES highlights the difficulties in labeling data under adverse conditions. Models trained on datasets with less rigorous annotation protocols may not perform well on MUSES. To mitigate this, future datasets should adopt similar comprehensive annotation strategies that account for uncertainty and difficult conditions. Overfitting to Training Conditions: Models may overfit to the specific conditions present in their training datasets, leading to poor performance in real-world scenarios. To combat this, researchers should employ techniques such as data augmentation, domain randomization, and cross-validation across different conditions to ensure models are robust and adaptable.

Given the importance of uncertainty quantification for safe autonomous driving, how can the novel uncertainty-aware panoptic segmentation task in MUSES inspire new research directions in probabilistic and robust perception for embodied agents?

The introduction of the uncertainty-aware panoptic segmentation task in MUSES opens several new research directions in probabilistic and robust perception for embodied agents: Probabilistic Modeling: The task encourages the development of probabilistic models that can quantify uncertainty in predictions. Researchers can explore Bayesian approaches or other probabilistic frameworks that allow models to express confidence levels in their outputs, which is crucial for safe decision-making in autonomous driving. Robustness to Adverse Conditions: By focusing on uncertainty-aware segmentation, researchers can investigate how to build models that are robust to various adverse conditions. This includes developing techniques to handle noisy or incomplete data, which is common in real-world driving scenarios. Adaptive Decision-Making: The ability to quantify uncertainty can lead to more adaptive decision-making processes in embodied agents. For instance, agents can adjust their behavior based on the confidence of their perception, opting for more cautious actions in uncertain situations. This can enhance safety and reliability in autonomous systems. Multi-Modal Uncertainty Fusion: The MUSES dataset's multimodal nature provides a unique opportunity to study how to fuse uncertainties from different sensors. Research can focus on developing algorithms that effectively combine uncertainty estimates from various modalities, leading to more informed and reliable predictions. Evaluation Metrics for Uncertainty: The introduction of the average uncertainty-aware panoptic quality (AUPQ) metric in MUSES sets a precedent for developing new evaluation metrics that incorporate uncertainty. Future research can explore how to create comprehensive evaluation frameworks that assess not only accuracy but also the reliability of predictions in uncertain environments. Real-World Applications: The insights gained from uncertainty-aware segmentation can be applied to other domains beyond autonomous driving, such as robotics, healthcare, and environmental monitoring. Researchers can leverage the principles of uncertainty quantification to enhance perception systems in various fields, leading to broader impacts on safety and efficiency.
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