The Ultimate Solution for L4 Autonomous Driving: Cooperative Perception

[CFAR Outstanding PhD Student Seminar Series]
The Ultimate Solution for L4 Autonomous Driving: Cooperative Perception by Xu Runsheng
12 Apr 2023 | 10.00am (Singapore Time)

Autonomous driving perception systems are faced with significant challenges, such as occlusion and sparse sensor observations at a distance. Cooperative perception presents a promising solution to these challenges as it utilises vehicle-to-everything (V2X) communication that enables autonomous vehicles to share visual information with each other.

In this seminar, Xu Runsheng from the University of California, Los Angeles will explore the state-of-the-art technologies in cooperative perception and present his recent research on the topic, including three published papers: (1) V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer (ECCV2022), (2) CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers (CoRL2022) and (3) V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception (CVPR2023 Highlight). He will also share insights into the current developments and future directions in autonomous driving perception.

 
SPEAKER
talks---xu-runsheng
Xu Runsheng
Ph.D. Candidate
University of California, Los Angeles (UCLA)
Xu Runsheng is a Ph.D. candidate at the University of California, Los Angeles (UCLA), specialising in autonomous driving research. Having worked as a Senior Deep Learning Engineer at Mercedes-Benz R&D North America and as a Computer Vision Engineer at OPPO Mobile R&D US, he brings extensive industry experience. Runsheng has made significant contributions to several high-impact commercial projects, including OPPO Reno2 Low-Light Imaging and Mercedes-Benz Autopilot systems. As the first author, he has published numerous articles in top-tier vision and robotics conferences and journals, such as CVPR, ECCV, CoRL, ICRA, and IROS. Additionally, Runsheng has developed a widely recognised coding framework in the connected autonomous driving domain, garnering thousands of GitHub stars for its popularity and utility.