Automotive perception involves understanding the external driving environment and the
internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving …

In the context of autonomous vehicles on highways, one of the first and most important tasks
is to localize the vehicle on the road. For this purpose, the vehicle needs to be able to take …

In this paper, we propose PETRv2, a unified framework for 3D perception from multi-view
images. Based on PETR, PETRv2 explores the effectiveness of temporal modeling, which …

Lane is critical in the vision navigation system of the intelligent vehicle. Naturally, lane is a
traffic sign with high-level semantics, whereas it owns the specific local pattern which needs …

Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …

This paper presents a novel parametric curve-based method for lane detection in RGB
images. Unlike state-of-the-art segmentation-based and point detection-based methods that …

Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …

Methods for 3D lane detection have been recently proposed to address the issue of
inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.) …

Lane detection is a challenging task that requires predicting complex topology shapes of
lane lines and distinguishing different types of lanes simultaneously. Earlier works follow a …

Unlike RGB cameras that use visible light bands (384∼ 769 THz) and Lidars that use
infrared bands (361∼ 331 THz), Radars use relatively longer wavelength radio bands (77∼ …