Qi Fang(方淇)

Hi! I am Qi Fang, a senior student majoring in Computer Science in Beijing University of Posts and Telecommunications (BUPT), expected to graduate in 2023.

Seeing everything related to autonomous driving makes me extremely excited, and this has led me to join the related research field with enthusiasm. Generally, My research interest lies in Computer Vision, AI Security, and Robotics. Specifically, I am particularly interested in achieving robust autonomous driving at the perception level, especially under adverse weather and adversarial attacks.

Currently I am a research intern focusing on robust robot vision at AI4CE LAB, New York University, fortunately supervised by Prof. Chen Feng and Dr. Felix Juefei Xu. Previously I interned at iDLAB, Tsinghua University, advised by Prof. Wenjun Wang, where I focused on human behavior interacting with self-driving vehicles.

In addition to academia, I also worked at XPeng Motors as a Test Engineering Intern to perform quality assurance on its industry-leading Valet Parking Assist(VPA) feature.

Feel free to contact me by email: qifang804 at gmail dot com

Gmail  |  Google Scholar  |  Github

"We proposed a novel probability-guaranteed sampling method inspired by RANSAC to achieve a trust-worthy group of benign agents, outperforming adversarial training Method by 73%."

"We validated a key indicator of relative position between the ego vehicle and surrounding vehicles having important effects on take-over performance and driving safety."

"To augment the performance of lidar perception under challenging meteorological conditions. Focusing on noise-removal and noise-utilization methods."

"To explore the potential of reverse adversarial attacks to realize robust vision as well as robust 'objects' to current deep vision models."

Research Topics:
[1] Multi-agent Collaboration / [2] Adversarial Machine Learning / [3] Robust Robot Vision

[1][2] -> Among Us, in submission to ICCV 23'
[2] -> Reverse Adversarial Learning, ongoing
[3] -> 3D Perception in Adverse Weather Conditions, ongoing

Tasks: Research, quality assurance, user testing, evaluation and optimization of XPeng’s Valet Parking Assist (VPA) feature

Research Topics: Human factors and ergonomics in conditional automated driving. -> AHFE 2021 Paper

Major Courses: Operating System / Database System / Discrete Mathematics / Machine Learning / Data Structures / Compilers / Computer Networks