Autonomous-Driving-Papers

🚘 A curated list of papers in autonomous driving

Table of Contents

• Perception
• Behavior Prediction
• Planning
• Simulation
• General Machine Learning

format:
- title [paper link, code link, summary link]
  - authors (institution)
  - publisher
  - keywords

LLM4AD

• A Language Agent for Autonomous Driving [Paper, Summary]
  • Jiageng Mao, Junjie Ye, Yuxi Qian, Marco Pavone, Yue Wang (University of Southern California, Stanford University, NVIDIA)
  • arXiv 2023
  • LLM

Perception

Behavior Prediction

Planning

• Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula [Paper, Summary]
  • Eli Bronstein, Sirish Srinivasan, Supratik Paul, Aman Sinha, Matthew O'Kelly, Payam Nikdel, Shimon Whiteson (Waymo)
  • CoRL 2022 Oral
  • Imitation Learning, Curriculum Learning

Simulation

General Machine Learning

• Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios [Paper, Summary]

  • Yiren Lu, Justin Fu, George Tucker, Xinlei Pan, Eli Bronstein, Becca Roelofs, Benjamin Sapp, Brandyn White, Aleksandra Faust, Shimon Whiteson, Dragomir Anguelov, Sergey Levine (Waymo Research, Google Research)
  • ML4AD@NeurIPS 2022
  • Imitation Learning, Reinforcement Learning

• Online Decision Transformer [Paper, Summary]

  • Qinqing Zheng, Amy Zhang, Aditya Grover (Meta AI Research, University of California, University of California)
  • ICML 2022 Oral
  • Reinforcement Learning

• Accelerating Online Reinforcement Learning with Offline Datasets [Paper, Summary]

  • Ashvin Nair, Abhishek Gupta, Murtaza Dalal, Sergey Levine (UC Berkeley)
  • CoRR 2021
  • Reinforcement Learning

• Test-Time Robust Personalization for Federated Learning [Paper, Summary]

  • Liangze Jiang, Tao Lin (Westlake University, EPFL)
  • ICLR 2023
  • Federated Learning

• Tent: Fully Test-Time Adaptation by Entropy Minimization [Paper, Summary]

  • Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, Trevor Darrell (UC Berkeley, Adobe Research)
  • ICLR 2021
  • Test-time Adaptation

• Continual Test-Time Domain Adaptation [Paper, Summary]

  • Qin Wang, Olga Fink, Luc Van Gool, Dengxin Dai (ETH Zurich, MPI for Informatics, EPFL, KU Lueven)
  • CVPR 2022
  • Test-time Adaptation

• TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive? [Paper, Summary]

  • Yuejiang Liu, Parth Kothari, Bastien van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, Alexandre Alahi (EPFL)
  • NeurIPS 2021
  • Test-time Adaptation

• MEMO: Test time robustness via adaptation and augmentation [Paper, Summary]

  • Marvin Zhang, Sergey Levine, Chelsea Finn (UC Berkeley, Stanford University)
  • NeurIPS 2022
  • Test-time Adaptation

• When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions [Paper, Summary]

  • Weiming Zhuang, Chen Chen, Lingjuan Lyu (�SONY AI)
  • arXiv 2023
  • Federated Learning

• Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition [Paper, Summary]

  • Yifan Zhang, Bryan Hooi, Lanqing Hong, Jiashi Feng (National University of Singapore, Huawei Noah’s Ark Lab, ByteDance)
  • arXiv 2021
  • Test-time Adaptation

• Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization [Paper, Summary]

  • Yusuke Iwasawa, Yutaka Matsuo (The University of Tokyo)
  • NeurIPS 2021
  • Test-time Adaptation