Lan Feng
  • Bio
  • Papers
  • Experience
  • Projects
  • Recent & Upcoming Talks
    • Example Talk
  • Publications
    • OmniTraj: Pre-Training on Heterogeneous Data for Adaptive and Zero-Shot Human Trajectory Prediction
    • Tarot: Targeted data selection via optimal transport
    • Weak-for-Strong: Training Weak Meta-Agent to Harness Strong Executors
    • Synh2r: Synthesizing hand-object motions for learning human-to-robot handovers
    • Unitraj: A unified framework for scalable vehicle trajectory prediction
    • Scenarionet: Open-source platform for large-scale traffic scenario simulation and modeling
    • Human-ai shared control via policy dissection
    • Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning
    • Trafficgen: Learning to generate diverse and realistic traffic scenarios
  • Projects
  • Blog
    • 🎉 Easily create your own simple yet highly customizable blog
    • 🧠 Sharpen your thinking with a second brain
    • 📈 Communicate your results effectively with the best data visualizations
    • 👩🏼‍🏫 Teach academic courses
    • ✅ Manage your projects
  • Projects
    • Benchmarking Safety Performance in Reinforcement Learning
    • Floorplan Localization and Navigation
    • Towards World Models for Robot Navigation
  • Experience
  • Teaching
    • Learn JavaScript
    • Learn Python

Trafficgen: Learning to generate diverse and realistic traffic scenarios

Jan 1, 2022·
Lan Feng
,
Quanyi Li
,
Zhenghao Peng
,
Shuhan Tan
,
Bolei Zhou
· 0 min read
PDF Link
Type
Journal article
Publication
arXiv preprint arXiv:2210.06609
Last updated on Jan 1, 2022
← Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning Jan 1, 2022

© 2025 Lan Feng. This work is licensed under CC BY NC ND 4.0

Made with Hugo Blox — Open Source. Build yours →