Calibrated and Efficient Sampling-Free Confidence Estimation for LiDAR Scene Semantic Segmentation
This work addresses the need for efficient and reliable uncertainty estimation in safety-critical autonomous driving applications, though it appears incremental by building on existing calibration methods with a sampling-free adaptation.
The paper tackled the problem of estimating well-calibrated confidence values for LiDAR semantic segmentation in autonomous driving, introducing a sampling-free method that reduces inference time compared to sampling-based approaches while maintaining calibration, as shown by improved Adaptive Calibration Error (ACE) metrics and reliability diagrams indicating underconfidence for safety.
Reliable deep learning models require not only accurate predictions but also well-calibrated confidence estimates to ensure dependable uncertainty estimation. This is crucial in safety-critical applications like autonomous driving, which depend on rapid and precise semantic segmentation of LiDAR point clouds for real-time 3D scene understanding. In this work, we introduce a sampling-free approach for estimating well-calibrated confidence values for classification tasks, achieving alignment with true classification accuracy and significantly reducing inference time compared to sampling-based methods. Our evaluation using the Adaptive Calibration Error (ACE) metric for LiDAR semantic segmentation shows that our approach maintains well-calibrated confidence values while achieving increased processing speed compared to a sampling baseline. Additionally, reliability diagrams reveal that our method produces underconfidence rather than overconfident predictions, an advantage for safety-critical applications. Our sampling-free approach offers well-calibrated and time-efficient predictions for LiDAR scene semantic segmentation.