CV

Education, work experience, technical skills, and academic service.

01

General Information

Full Name Peizheng Li
Email peizheng.li@yahoo.com
Homepage edwardleelpz.github.io
Languages Chinese (native), English (professional), German (professional)
02

Education

  • 2023 - Present
    Ph.D. in Computer Science
    University of Tübingen, Germany
    • Advisors: Prof. Andreas Geiger and Prof. Andreas Zell
    • Research: multimodal learning, VLM / VLA, world models, embodied AI, robotics, and autonomous driving
  • 2020 - 2022
    M.Sc. in Electromobility
    University of Stuttgart, Germany
    • Grade: 1.6 (German scale, 1.0 best; approx. 3.7 / 4.0 US equivalent)
    • Thesis: End-to-End Agent Perception and Occupancy-Based Motion Prediction in Bird's-Eye View
  • 2014 - 2019
    B.Eng. in Vehicle Engineering
    Tongji University, Shanghai, China
    • Grade: 4.5 / 5.0
    • Thesis: Vehicle Detection and Tracking Based on Sensor Fusion
03

Work Experience

  • 2023 - Present
    Industrial Ph.D. Researcher
    Mercedes-Benz Group AG, Stuttgart, Germany
    Scene Understanding Group, R&D
    • Research on foundation models for autonomous systems, spanning VLM / VLA for autonomous driving, open-world 3D occupancy prediction, spatial and world-model-oriented reasoning, diffusion-based motion planning, self-supervised scene flow, and robotics / HRI collaborations.
    • First-author publications: SpaceDrive (CVPR 2026), AGO (ICCV 2025), PowerBEV (IJCAI 2023).
    • Built large-scale data-engine and evaluation pipelines for data acquisition, cleaning, temporal consistency, scene / instance retrieval, and automated dense 3D pseudo-label generation.
    • Scaled training and evaluation across local GPU clusters and cloud using PyTorch, CUDA, DDP, Docker, Kubernetes, Flyte, Azure, and GCP.
    • Mentored interns and master thesis students; collaborated across research and engineering teams on open-source releases and reproducible evaluation.
  • 2022
    Master Student
    Mercedes-Benz Group AG, Stuttgart, Germany
    Scene Understanding Group, R&D
    • Built a camera-based end-to-end BEV perception and future prediction model with Lift-Splat-Shoot-style view transformation, temporal prediction, and multi-task decoding; achieved 39.3% dynamic IoU on nuScenes.
    • Designed a multi-stage temporal GCN for the Waymo Occupancy and Flow Prediction Challenge; achieved 74.2% Flow-Grounded Occupancy AUC and ranked 4th on the leaderboard.
  • 2021
    Research Intern
    Mercedes-Benz Group AG, Stuttgart, Germany
    Scene Understanding Group, R&D
    • Studied contextual bias in 2D object detection and domain adaptation; disruptive context reduced mAP by 5.37%.
    • Developed context-separation components and baseline pipelines to support internal adaptation research.
04

Technical Skills

Programming
Python
C++
CUDA
C#
MATLAB
Bash
ML / Systems
PyTorch
TensorFlow
OpenMMLab
Distributed Training
Docker
Kubernetes
Flyte
W&B
Azure
GCP
Research Areas
Foundation Models & VLM / VLA for Driving
World Models
Multimodal Learning
3D Vision
Open-World Perception
BEV / Occupancy
Scene Flow
Auto-Labeling & Data Engines
Spatial Intelligence
Embodied AI
Multi-View Geometry
SLAM
HRI
05

Service

  • Organizer: 4th DriveX Workshop, in conjunction with CVPR 2026
  • Organizer: 6th DriveX Workshop, in conjunction with ECCV 2026
  • Reviewer: CVPR 2025/2026, ICCV 2025, ECCV 2026, ICRA 2026, AAAI 2026/2027, IROS 2025/2026, IEEE T-ITS 2025
CV

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