Kognitive Systeme

Peizheng Li

Background

Since 2023
PhD student in cooperation with Mercedes-Benz AG
2020 - 2022
M. Sc. Electromobility, University of Stuttgart
2014 - 2019
B. Sc. Automotive Engineering, Tongji University

Current Project

Open World Model, Vision-Language-Action Model in Autonomous Driving

Research Interests

Currently my research interests lie in the intersection of machine learning, robotics and computer vision. In particular, I am focusing on extending autonomous driving capability, such as perception, prediction, etc., to real-life open world scenarios through multimodal models, 3D reconstruction, self-supervised training, and other techniques.

Personal webpage: https://edwardleelpz.github.io/

Publications

[1]
Li, Peizheng, Shuxiao Ding, You Zhou, Qingwen Zhang, Onat Inak, Larissa Triess, Niklas Hanselmann, Marius Cordts, and Andreas Zell. "AGO: Adaptive Grounding for Open World 3D Occupancy Prediction." ICCV 2025. [Page] [arXiv] [Code]
[2]
Zhang, Qingwen, Yi Yang, Peizheng Li, Olov Andersson, and Patric Jensfelt. "Seflow: A self-supervised scene flow method in autonomous driving." ECCV 2024. [arXiv] [Code]
[3]
Li, Peizheng, Shuxiao Ding, Xieyuanli Chen, Niklas Hanselmann, Marius Cordts, and Juergen Gall. "PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird’s-Eye View." IJCAI 2023. [arXiv] [Code]
[4]Ding, Shuxiao, Yutong Yang, Julian Wiederer, Markus Braun, Peizheng Li, Juergen Gall, and Bin Yang. "TQD-Track: Temporal Query Denoising for 3D Multi-Object Tracking." arXiv 2025. [arXiv]
[5]
Lai, Yuzhi, Shenghai Yuan, Boya Zhang, Benjamin Kiefer, Peizheng Li, Tianchen Deng, and Andreas Zell. "Fam-hri: Foundation-model assisted multi-modal human-robot interaction combining gaze and speech." arXiv 2025. [arXiv]
[6]
Lai, Yuzhi, Shenghai Yuan, Peizheng Li, Jun Lou, and Andreas Zell. "SEER-VAR: Semantic Egocentric Environment Reasoner for Vehicle Augmented Reality." arXiv 2025. [arXiv]

Teaching

Proseminar "Topics in Deep Neural Networks" (WS 2024/25)

Proseminar “Topics in Deep Neural Networks” (SS 2025)

Lecture “Deep Learning” (WS 2025/26)