Point clouds and RGB images are two general perceptional sources in autonomous driving.
The former can provide accurate localization of objects, and the latter is denser and richer in …

Camera and LiDAR sensor modalities provide complementary appearance and geometric
information useful for detecting 3D objects for autonomous vehicle applications. However …

LiDAR-Camera fusion-based 3D detection is a critical task for automatic driving. In recent
years, many LiDAR-Camera fusion approaches sprung up and gained promising …

Safe local motion planning for autonomous driving in dynamic environments requires
forecasting how the scene evolves. Practical autonomy stacks adopt a semantic object …

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 …

Extending the success of 2D Large Kernel to 3D perception is challenging due to: 1. the
cubically-increasing overhead in processing 3D data; 2. the optimization difficulties from …

Predicting the future location of vehicles is essential for safety-critical applications such as
advanced driver assistance systems (ADAS) and autonomous driving. This paper introduces …

Segmenting or detecting objects in sparse Lidar point clouds are two important tasks in
autonomous driving to allow a vehicle to act safely in its 3D environment. The best …

Modern methods for vision-centric autonomous driving perception widely adopt the bird's-
eye-view (BEV) representation to describe a 3D scene. Despite its better efficiency than …

Recently, the pure camera-based Bird's-Eye-View (BEV) perception provides a feasible
solution for economical autonomous driving. However, the existing BEV-based multi-view …