2 citations · 3 across the 3 of their papers we have counts for
6 papers
MSC-Bench: Benchmarking and Analyzing Multi-Sensor Corruption for Driving Perception
Xiaoshuai Hao, Guanqun Liu, Yuting Zhao +6
Multi-sensor fusion models play a crucial role in autonomous driving perception, particularly in tasks like 3D object detection and HD map construction. These models provide essent…
MapDistill: Boosting Efficient Camera-based HD Map Construction via Camera-LiDAR Fusion Model Distillation
Xiaoshuai Hao, Ruikai Li, Hui Zhang +7
Online high-definition (HD) map construction is an important and challenging task in autonomous driving. Recently, there has been a growing interest in cost-effective multi-view ca…
Is Your HD Map Constructor Reliable under Sensor Corruptions?
Xiaoshuai Hao, Mengchuan Wei, Yifan Yang +7
Driving systems often rely on high-definition (HD) maps for precise environmental information, which is crucial for planning and navigation. While current HD map constructors perfo…
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
Lingdong Kong, Shaoyuan Xie, Hanjiang Hu +88
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sen…
Team Samsung-RAL: Technical Report for 2024 RoboDrive Challenge-Robust Map Segmentation Track
Xiaoshuai Hao, Yifan Yang, Hui Zhang +4
In this report, we describe the technical details of our submission to the 2024 RoboDrive Challenge Robust Map Segmentation Track. The Robust Map Segmentation track focuses on the…
UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather
Haimei Zhao, Jing Zhang, Zhuo Chen +2
LiDAR semantic segmentation (LSS) is a critical task in autonomous driving and has achieved promising progress. However, prior LSS methods are conventionally investigated and evalu…