README.md

Awesome V2X

A curated list of Vehicle to X (V2X) resources (continually updated). You can reference Journal Information for more information.

Table of Contents

• Awesome V2X
  • Table of Contents
  • Paper
    • 2021
    • 2023
    • 2024
    • Traffic Signal Control (信号灯控制)
    • CAVs and TSC (自动驾驶&信号灯控制)
    • Driver Simulator (仿真框架)
  • License

Paper

format:
- [title](paper link) [links]
  - author1, author2, and author3...
  - keyword
  - publisher
  - code
  - experiment environments and datasets
  - paper reading url

2021

• Planning and Decision-making for Connected Autonomous Vehicles at Road Intersections: A Review
  • Shen Li, Keqi Shu, Chaoyi Chen and Dongpu Cao
  • Planning, Decision-making, Autonomous intersection management, Connected autonomous vehicles
  • Chinese Journal of Mechanical Engineering
  • Paper Reading

2023

• GPT-Driver: Learning to Drive with GPT

  • Jiageng Mao, Yuxi Qian, Hang Zhao, Yue Wang
  • Paper Reading, (轨迹规划, 大语言模型)

• Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles

  • Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Ziran Wang
  • Paper Reading, (自动驾驶, 大语言模型)

• Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles

  • Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Ziran Wang
  • Paper Reading, (自动驾驶, 大语言模型)，这一篇上 Drive as You Speak 的详细版本

• Talk2BEV: Language-enhanced Bird’s-eye View Maps for Autonomous Driving

  • Tushar Choudhary, Vikrant Dewangan, Shivam Chandhok, Shubham Priyadarshan, Anushka Jain, Arun K. Singh, Siddharth Srivastava, Krishna Murthy Jatavallabhula, K. Madhava Krishna
  • Paper Reading, (自动驾驶, 场景理解, 大语言模型)

• Open-TI: Open Traffic Intelligence with Augmented Language Model

  • Longchao Da, Kuanru Liou, Tiejin Chen, Xuesong Zhou, Xiangyong Luo, Yezhou Yang, Hua Wei
  • Paper Reading, (智慧交通系统, 人机交互, 大语言模型)

• LLM-ASSIST: Enhancing Closed-Loop Planning with Language-Based Reasoning

  • S P Sharan, Francesco Pittaluga, Vijay Kumar B G, Manmohan Chandraker
  • Paper Reading, (轨迹生成, 自动驾驶, 大语言模型)
  • 利用 rule-based planner + LLM，如果 rule-based planner 产生的轨迹得分较低，则使用 LLM 去分析场景并产生新的轨迹。

• Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving

  • Yuqi Wang, Jiawei He, Lue Fan, Hongxin Li, Yuntao Chen, Zhaoxiang Zhang
  • Paper Reading, (场景预测, 自动驾驶, 大语言模型)
  • 对多视角的视频进行预测（结合当前 state 和 action，对场景预测），对不同动作的未来场景进行打分，最后选择分数高的场景对应的动作进行执行。

2024

• World Models for Autonomous Driving: An Initial Survey
  • Yanchen Guan, Haicheng Liao, Zhenning Li, Guohui Zhang, Chengzhong Xu
  • Paper Reading, (World Model, RSSM, PETA, AV)
  • World Model 在 Autonomous Driving 上的综述，主要介绍了两种 World Model 的结构，RSSM 和 JEPA，以及 World Model 在 AV 中的一些应用，（1）场景生成，（2）决策控制；

Traffic Signal Control (信号灯控制)

• Network Clustering-based Multi-agent Reinforcement Learning for Large-scale Traffc Signal Control

  • Zhicheng Tao, Chao Li, Qinmin Yang
  • 2023 International Annual Conference on Complex Systems and Intelligent Science (CSIS-IAC), 2023
  • Paper Reading, (信号灯控制, 路口相似度聚类)
  • 多路口的信号灯控制，将路网进行分割，每个小区域在使用 MARL 来进行训练，提出了路网切分的方法。

• UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control

  • Maonan Wang, Xi Xiong, Yuheng Kan, Chengcheng Xu, Man-On Pun
  • IEEE Transactions on Vehicular Technology, 2024
  • Paper Reading（相关代码，Github-UniTSA）
  • 利用数据增强的方式，使得 agent 可以见到训练集中不包含的路口情况，使得 agent 可以在没有见过的路口上获得更好的结果。

CAVs and TSC (自动驾驶&信号灯控制)

• CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles Using Deep Reinforcement Learning
  • Jiaying Guo, Long Cheng, Shen Wang
  • IEEE Transactions on Intelligent Transportation Systems ( Volume: 24, Issue: 10, October 2023)
  • Paper Reading, (信号灯控制，CAV 速度控制，多智能体强化学习)
  • 利用强化学习同时控制 CAV（速度） 和 Traffic Light，为了解决扩展性，这里只控制距离 Traffic Light 最接近的 CAV。

Driver Simulator (仿真框架)

• CARLA: An Open Urban Driving Simulator

  • Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, Vladlen Koltun
  • Proceedings of the 1st Annual Conference on Robot Learning, PMLR 78:1-16, 2017.
  • Paper Reading, (Carla 仿真平台)
  • 介绍了 Carla 仿真平台，从两个方面，（1）Carla 仿真部分；（2）自动驾驶实验，测试不同任务下，不同决策方法的结果；

• Microscopic Traffic Simulation using SUMO

  • Pablo Alvarez Lopez, Michael Behrisch, Laura Bieker-Walz, Jakob Erdmann, Yun-Pang Flotterod, Robert Hilbrich, Leonhard Lucken, Johannes Rummel, Peter Wagner and Evamarie Wießner
  • 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018
  • Paper Reading, (SUMO 仿真平台，无需多言)
  • 把 SUMO 主要功能介绍了一遍。通过一个例子，介绍了路网生成，流量生成，仿真过程中的模型等。读一下对 SUMO 有更多的理解，有更多场景生成的方式。

• Flow: A Modular Learning Framework for Mixed Autonomy Traffic

  • Cathy Wu, Abdul Rahman Kreidieh, Kanaad Parvate, Eugene Vinitsky, Alexandre M. Bayen
  • IEEE Transactions on Robotics, vol. 38, no. 2, April 2022
  • Paper Reading, (Flow 混合交通流仿真框架)
  • 混合交通流下的 MDP 数学建模。希望解决混合车辆下对 AVs 的控制，于是提出了框架和任务，并给出基础的实验结果（文章的写作可以参考）。

• LimSim: A Long-term Interactive Multi-scenario Traffic Simulator

  • Licheng Wen, Daocheng Fu, Song Mao, Pinlong Cai, Min Dou, Yikang Li, Yu Qiao
  • 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 2023
  • Paper Reading, (仿真平台，交通场景介绍)
  • 介绍了一款自动驾驶仿真器，包含多样性的场景和车辆之间的交互。文章里面有对交通仿真器的总结，networkFiles 文件夹里面包含 SUMO 路网，从仿真器四个特色来介绍。

• MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning

  • Quanyi Li, Zhenghao Peng, Lan Feng, Qihang Zhang, Zhenghai Xue, Bolei Zhou
  • IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 3461-3475, 1 March 2023
  • Paper Reading, (仿真平台，自动驾驶泛化性的研究)
  • MetaDrive 目标是具有泛化性的自动驾驶，提出了框架和一些自动驾驶任务（文章的写作可以参考）。

License

Awesome V2X is released under the Apache 2.0 license.