3 research outputs found
On the Calibration of Uncertainty Estimation in LiDAR-based Semantic Segmentation
The confidence calibration of deep learning-based perception models plays a
crucial role in their reliability. Especially in the context of autonomous
driving, downstream tasks like prediction and planning depend on accurate
confidence estimates. In point-wise multiclass classification tasks like
sematic segmentation the model has to deal with heavy class imbalances. Due to
their underrepresentation, the confidence calibration of classes with smaller
instances is challenging but essential, not only for safety reasons. We propose
a metric to measure the confidence calibration quality of a semantic
segmentation model with respect to individual classes. It is calculated by
computing sparsification curves for each class based on the uncertainty
estimates. We use the classification calibration metric to evaluate uncertainty
estimation methods with respect to their confidence calibration of
underrepresented classes. We furthermore suggest a double use for the method to
automatically find label problems to improve the quality of hand- or
auto-annotated datasets.Comment: accepted at IEEE ITSC 202
Survey on LiDAR Perception in Adverse Weather Conditions
Autonomous vehicles rely on a variety of sensors to gather information about
their surrounding. The vehicle's behavior is planned based on the environment
perception, making its reliability crucial for safety reasons. The active LiDAR
sensor is able to create an accurate 3D representation of a scene, making it a
valuable addition for environment perception for autonomous vehicles. Due to
light scattering and occlusion, the LiDAR's performance change under adverse
weather conditions like fog, snow or rain. This limitation recently fostered a
large body of research on approaches to alleviate the decrease in perception
performance. In this survey, we gathered, analyzed, and discussed different
aspects on dealing with adverse weather conditions in LiDAR-based environment
perception. We address topics such as the availability of appropriate data, raw
point cloud processing and denoising, robust perception algorithms and sensor
fusion to mitigate adverse weather induced shortcomings. We furthermore
identify the most pressing gaps in the current literature and pinpoint
promising research directions.Comment: published at IEEE IV 202