Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …

In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant
interest from both industry and academia due to its inherent advantages, such as providing …

High-Definition (HD) maps are essential for the safety of autonomous driving systems. While
existing techniques employ camera images and onboard sensors to generate vectorized …

This paper presents a vector HD-mapping algorithm that formulates the mapping as a
tracking task and uses a history of memory latents to ensure consistent reconstructions over …

Current research is primarily dedicated to advancing the accuracy of camera-only 3D object
detectors (apprentice) through the knowledge transferred from LiDAR-or multi-modal-based …

In recent years, the field of autonomous vehicles has seen a significant increase in
academic research, with high-definition (HD) maps emerging as a critical component of …

Sparse algorithms offer great flexibility for multi-view temporal perception tasks. In this
paper, we present an enhanced version of Sparse4D, in which we improve the temporal …

Recently the rise of query-based Transformer decoders is reshaping camera-based 3D
object detection. These query-based decoders are surpassing the traditional dense BEV …

In autonomous driving perception systems, 3D detection and tracking are the two
fundamental tasks. This paper delves deeper into this field, building upon the Sparse4D …

Abstract Vectorized High-Definition (HD) map construction requires predictions of the
category and point coordinates of map elements (eg road boundary lane divider pedestrian …