Capturing uncertainty in object detection is indispensable for safe autonomous driving. In
recent years, deep learning has become the de-facto approach for object detection, and …

Autonomous vehicles are becoming central for the future of mobility, supported by advances
in deep learning techniques. The performance of aself-driving system is highly dependent …

How should we integrate representations from complementary sensors for autonomous
driving? Geometry-based fusion has shown promise for perception (eg, object detection …

While deep learning (DL) is data-hungry and usually relies on extensive labeled data to
deliver good performance, Active Learning (AL) reduces labeling costs by selecting a small …

Active learning aims to reduce labeling costs by selecting only the most informative samples
on a dataset. Few existing works have addressed active learning for object detection. Most …

The existing active learning methods select the samples by evaluating the sample's
uncertainty or its effect on the diversity of labeled datasets based on different task-specific or …

Driven by deep learning techniques, perception technology in autonomous driving has
developed rapidly in recent years, enabling vehicles to accurately detect and interpret …

With the recent growth of urban mapping and autonomous driving efforts, there has been an
explosion of raw 3D data collected from terrestrial platforms with lidar scanners and color …

A desirable data selection algorithm can efficiently choose the most informative samples to
maximize the utility of limited annotation budgets. However, current approaches …

Object detectors trained with weak annotations are affordable alternatives to fully-supervised
counterparts. However, there is still a significant performance gap between them. We …