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 …

The ability to predict, anticipate and reason about future outcomes is a key component of
intelligent decision-making systems. In light of the success of deep learning in computer …

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

Abstract From CNN, RNN, to ViT, we have witnessed remarkable advancements in video
prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated …

Many existing autonomous driving paradigms involve a multi-stage discrete pipeline of
tasks. To better predict the control signals and enhance user safety, an end-to-end approach …

Autonomous driving promises transformative improvements to transportation, but building
systems capable of safely navigating the unstructured complexity of real-world scenarios …

Driving requires interacting with road agents and predicting their future behaviour in order to
navigate safely. We present FIERY: a probabilistic future prediction model in bird's-eye view …

Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial
prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …

Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for
contextual information extraction and decision making. Beyond modeling advances, the …

In a world with rapidly growing levels of automation, robotics is playing an increasingly
significant role in every aspect of human endeavour. In particular, many types of mobile …