The grid-centric perception is a crucial field for mobile robot perception and navigation.
Nonetheless, the grid-centric perception is less prevalent than object-centric perception as …

Vision-centric joint perception and prediction (PnP) has become an emerging trend in
autonomous driving research. It predicts the future states of the traffic participants in the …

Predicting the trajectory of other road users, especially vulnerable road users (VRUs), is an
important aspect of safety and planning efficiency for autonomous vehicles. With recent …

Road surface conditions, especially geometry profiles, enormously affect driving
performance of autonomous vehicles. Vision-based online road reconstruction promisingly …

The perception of motion behavior in a dynamic environment holds significant importance
for autonomous driving systems wherein class-agnostic motion prediction methods directly …

Understanding the motion behavior of dynamic environments is vital for autonomous driving,
leading to increasing attention in class-agnostic motion prediction in LiDAR point clouds …

Learning the dense bird's eye view (BEV) motion flow in a self-supervised manner is an
emerging research for robotics and autonomous driving. Current self-supervised methods …

Class-agnostic motion prediction methods aim to comprehend motion within open-world
scenarios, holding significance for autonomous driving systems. However, training a high …

Predicting the future occupancy states of the surrounding environment is a vital task for
autonomous driving. However current best-performing single-modality methods or multi …

Autonomous vehicles may mistakenly predict the future trajectories of neighboring vehicles
when the trajectory prediction model is under attack. Recent works utilize adversarial …