RoAD Benchmark: How LiDAR Models Fail under Coupled Domain Shifts and Label Evolution
Summary: arXiv:2601.07855v2 Announce Type: replace-cross
In the realm of 3D perception systems, the ability to operate effectively in real-world environments hinges upon the robustness of these systems to evolving sensor characteristics and dynamic object taxonomies. However, existing adaptive learning methodologies frequently encounter significant challenges in the context of LiDAR technology, particularly when domain shifts and label-space evolution occur simultaneously. To address this pressing need, researchers have introduced the Robust Autonomous Driving under Dataset shifts (RoAD), a novel benchmark aimed at evaluating model robustness in LiDAR-based object classification amidst intertwined domain shifts and label evolution.
The RoAD benchmark encompasses several critical aspects of the challenges faced in autonomous driving applications. These challenges include:
- Subclass refinement: This pertains to the enhancement of existing object categories, which may complicate the classification tasks.
- Unseen-class insertion: The introduction of new object categories that were not present in the training dataset necessitates adaptive learning capabilities.
- Label expansion: This involves the addition of new labels to existing classes, requiring the model to adapt to an evolving label space.
RoAD evaluates three distinct learning scenarios that vary in their level of adaptation. These scenarios include:
- Fixed representations: This scenario encompasses zero-shot transfer and linear probing, where models are tested without further training on new data.
- Sequential updates: This scenario involves continual learning, allowing models to incrementally learn from new data.
The experiments conducted under the RoAD benchmark span several large-scale autonomous driving datasets, including Waymo, nuScenes, and Argoverse2. The findings from these experiments provide invaluable insights into the performance and limitations of current LiDAR models.
Our analysis identified two central failure modes that underscore the critical challenges faced by these models:
- Limited transferability: Models exhibited significant difficulty in transferring knowledge under conditions of subclass refinement and unseen-class insertion, particularly when dealing with non-vehicle classes.
- Accelerated forgetting during continual adaptation: This phenomenon was driven by feature collapse and self-supervised learning objectives, indicating that models tend to forget previously learned information when adapting to new data.
In conclusion, the RoAD benchmark represents a significant step forward in understanding and enhancing the robustness of LiDAR-based object classification systems. By systematically addressing the challenges posed by coupled domain shifts and label evolution, this benchmark offers researchers and practitioners the tools necessary to develop more resilient autonomous driving technologies. As the field of 3D perception continues to evolve, the insights gleaned from RoAD will be instrumental in paving the way for more reliable autonomous systems that can adapt to the complexities of the real world.