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

Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for
developing a new generation of intelligent transportation systems. Collaborative perception …

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

Semantic scene completion (SSC) is crucial for holistic 3D scene understanding by jointly
estimating semantics and geometry from sparse observations. However, progress in SSC …

Visual place recognition (VPR) capabilities enable autonomous robots to navigate complex
environments by discovering the environment's topology based on visual input. Most …

Large-scale datasets have fueled recent advancements in AI-based autonomous vehicle
research. However these datasets are usually collected from a single vehicle's one-time …

As an important component of intelligent transportation systems, collaborative perception
has made great progress in recent years. In collaborative perception, multiple devices rely …

The control of dynamical systems under temporal logic specifications among uncontrollable
dynamic agents is challenging due to the agents'a-priori unknown behavior. Existing works …

Quantifying a model's predictive uncertainty is essential for safety-critical applications such
as autonomous driving. We consider quantifying such uncertainty for multi-object detection …

Collaborative perception leverages rich visual observations from multiple robots to extend a
single robot's perception ability beyond its field of view. Many prior works receive messages …