Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …

Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …

Sensor fusion is an essential topic in many perception systems, such as autonomous driving
and robotics. Existing multi-modal 3D detection models usually involve customized designs …

Camera and lidar are important sensor modalities for robotics in general and self-driving
cars in particular. The sensors provide complementary information offering an opportunity for …

Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle
technology. Image based benchmark datasets have driven development in computer vision …

Autonomous vehicles require constant environmental perception to obtain the distribution of
obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional …

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

To assure that an autonomous car is driving safely on public roads, its object detection
module should not only work correctly, but show its prediction confidence as well. Previous …

Multi-modal 3D object detection has been an active research topic in autonomous driving.
Nevertheless, it is non-trivial to explore the cross-modal feature fusion between sparse 3D …

As the foundation of intelligent systems, machine vision perceives the surrounding
environment and provides a basis for decision-making. Object detection is the core task in …