Collaborative perception is essential to address occlusion and sensor failure issues in
autonomous driving. In recent years, theoretical and experimental investigations of novel …

Humans can easily imagine the complete 3D geometry of occluded objects and scenes. This
appealing ability is vital for recognition and understanding. To enable such capability in AI …

Accurate perception is essential for advancing autonomous driving and addressing safety
challenges in modern transportation systems. Despite significant advancements in computer …

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …

This paper presents CORE, a conceptually simple, effective and communication-efficient
model for multi-agent cooperative perception. It addresses the task from a novel perspective …

Recent advancements in Vehicle-to-Everything (V2X) technologies have enabled
autonomous vehicles to share sensing information to see through occlusions, greatly …

Existing multi-agent perception algorithms usually select to share deep neural features
extracted from raw sensing data between agents, achieving a trade-off between accuracy …

Collaborative perception learns how to share information among multiple robots to perceive
the environment better than individually done. Past research on this has been task-specific …

Multiple robots could perceive a scene (eg, detect objects) collaboratively better than
individuals, although easily suffer from adversarial attacks when using deep learning. This …

Monocular scene understanding is a foundational component of autonomous systems.
Within the spectrum of monocular perception topics, one crucial and useful task for holistic …