Automotive perception involves understanding the external driving environment and the
internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving …

This review article is an attempt to survey all recent AI based techniques used to deal with
major functions in This review paper presents a comprehensive overview of end-to-end …

In this paper, we introduce neural texture learning for 6D object pose estimation from
synthetic data and a few unlabelled real images. Our major contribution is a novel learning …

As 3D object detection on point clouds relies on the geometrical relationships between the
points, non-standard object shapes can hinder a method's detection capability. However, in …

Since real-world training datasets cannot properly sample the long tail of the underlying data
distribution, corner cases and rare out-of-domain samples can severely hinder the …

In self-driving cars, object detection algorithms are becoming increasingly important, and the
accurate and fast recognition of objects is critical to realize autonomous driving. The existing …

Deep neural network algorithms have achieved impressive performance in object detection.
Real-time evaluation of perception uncertainty from deep neural network algorithms is …

Panoptic segmentation methods assign a known class to each pixel given in input. Even for
state-of-the-art approaches, this inevitably enforces decisions that systematically lead to …

Uncertainty estimation is crucial in safety-critical settings such as automated driving as it
provides valuable information for several downstream tasks including high-level decision …

As a basic and indispensable module, LiDAR odom-etry estimation is widely used in
robotics. In recent years, learning-based modeling approaches for odometry estimation have …