World models, especially in autonomous driving, are trending and drawing extensive
attention due to their capacity for comprehending driving environments. The established …

Closed-loop simulation is essential for advancing end-to-end autonomous driving systems.
Contemporary sensor simulation methods, such as NeRF and 3DGS, rely predominantly on …

Drivable Area (DA) detection is crucial for autonomous driving. Camera-based methods rely
heavily on illumination conditions and often fail to capture accurate 3D information, while …

Recognition of passable areas of mine roads based on vehicle perspective is crucial for
autonomous vehicles to drive in unmanned open-pit mine scenes. In the past few years …

Ensuring autonomous vehicle (AV) security remains a critical concern. An area of paramount
importance is the study of physical-world adversarial examples (AEs) aimed at exploiting …

The rapid advancement of autonomous driving technology presents significant challenges
for multi-task road object detection, as different object features can exhibit notable variations …

Characterizing fatigue micro-cracks is crucial for understanding the mechanisms and
behaviors of material damage. In-situ fatigue testing is an essential method for observing the …

Lane detection approaches relying on abundant annotations have achieved great progress
in autonomous driving. These approaches may suffer from performance collapse when …

Closed-loop simulation is crucial for end-to-end autonomous driving. Existing sensor
simulation methods (eg, NeRF and 3DGS) reconstruct driving scenes based on conditions …

Lane lines play a crucial role in the traffic system. However, due to the diversity of lane
categories, road conditions and weather environments, as well as the different aspect ratios …