YUKAI MA

2025

1. 

   LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process Thinking

   Yukai Ma, Tiantian Wei, Naiting Zhong, and 6 more authors

   arXiv preprint arXiv:2501.08168, 2025

   Demo Code

2024

1. 

   LiCROcc: Teach radar for accurate semantic occupancy prediction using lidar and camera

   Yukai Ma, Jianbiao Mei, Xuemeng Yang, and 6 more authors

   IEEE Robotics and Automation Letters, 2024

   Bib Demo Code

   @article{ma2024licrocc,
     title = {LiCROcc: Teach radar for accurate semantic occupancy prediction using lidar and camera},
     author = {Ma, Yukai and Mei, Jianbiao and Yang, Xuemeng and Wen, Licheng and Xu, Weihua and Zhang, Jiangning and Zuo, Xingxing and Shi, Botian and Liu, Yong},
     journal = {IEEE Robotics and Automation Letters},
     year = {2024},
     publisher = {IEEE},
     demo = {https://hr-zju.github.io/LiCROcc/},
   }

2. 

   DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving

   Xuemeng Yang*, Licheng Wen*, Yukai Ma*, and 11 more authors

   arXiv preprint arXiv:2408.00415, 2024

   Abs arXiv Bib Demo Code

   This paper presetns DriveArena, the first high-fidelity closed-loop simulation system designed for driving agents navigating in real scenarios. DriveArena features a flexible, modular architecture, allowing for the seamless interchange of its core components: Traffic Manager, a traffic simulator capable of generating realistic traf- fic flow on any worldwide street map, and World Dreamer, a high-fidelity conditional generative model with infinite autoregression. This powerful synergy empowers any driving agent capable of processing real-world images to navigate in DriveArena simulated environment. The agent perceives its surroundings through images generated by World Dreamer and output trajectories; then these trajectories are fed into Traffic Manager, achieving realistic interactions with other vehicles and producing a new scene lay- out. Finally, the latest scene layout is relayed back into World Dreamer, perpetuating the simulation cycle. This iterative process fosters closed-loop exploration within a highly realistic environment, providing a valuable platform for developing and evaluating driving agents across diverse and challenging scenarios. DriveArena signifies a substantial leap forward in leveraging generative image data for the driving simulatior, opening insights for closed-loop autonomous driving.

   @article{yang2024drivearena,
     title = {DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving},
     author = {Yang*, Xuemeng and Wen*, Licheng and Ma*, Yukai and Mei*, Jianbiao and Li*, Xin and Wei*, Tiantian and Lei, Wenjie and Fu, Daocheng and Cai, Pinlong and Dou, Min and Shi, Botian and He, Liang and Liu, Yong and Qiao, Yu},
     journal = {arXiv preprint arXiv:2408.00415},
     year = {2024},
     demo = {https://pjlab-adg.github.io/DriveArena/},
   }

3. 

   Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous Driving

   Jianbiao Mei*, Yukai Ma*, Xuemeng Yang, and 8 more authors

   Advances in Neural Information Processing Systems (NeurIPS), 2024

   Abs arXiv Bib Demo Code

   Autonomous driving has advanced significantly due to sensors, machine learning, and artificial intelligence improvements. However, prevailing methods struggle with intricate scenarios and causal relationships, hindering adaptability and interpretability in varied environments. To address the above problems, we introduce LeapAD, a novel paradigm for autonomous driving inspired by the human cognitive process. Specifically, LeapAD emulates human attention by selecting critical objects relevant to driving decisions, simplifying environmental interpretation, and mitigating decision-making complexities. Additionally, LeapAD incorporates an innovative dual-process decision-making module, which consists of an Analytic Process (System-II) for thorough analysis and reasoning, along with a Heuristic Process (System-I) for swift and empirical processing. The Analytic Process leverages its logical reasoning to accumulate linguistic driving experience, which is then transferred to the Heuristic Process by supervised fine-tuning. Through reflection mechanisms and a growing memory bank, LeapAD continuously improves itself from past mistakes in a closed-loop environment. Closed-loop testing in CARLA shows that LeapAD outperforms all methods relying solely on camera input, requiring 1-2 orders of magnitude less labeled data. Experiments also demonstrate that as the memory bank expands, the Heuristic Process with only 1.8B parameters can inherit the knowledge from a GPT-4 powered Analytic Process and achieve continuous performance improvement.

   @article{mei2024continuously,
     title = {Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous Driving},
     author = {Mei*, Jianbiao and Ma*, Yukai and Yang, Xuemeng and Wen, Licheng and Cai, Xinyu and Li, Xin and Fu, Daocheng and Zhang, Bo and Cai, Pinlong and Dou, Min and others},
     journal = {Advances in Neural Information Processing Systems (NeurIPS)},
     year = {2024},
     demo = {https://leapad-2024.github.io/LeapAD/},
   }

4. 

   RIDERS: Radar-Infrared Depth Estimation for Robust Sensing

   Han Li*, Yukai Ma*, Yuehao Huang, and 4 more authors

   IEEE Transactions on Intelligent Transportation Systems, 2024

   Bib Code

   @article{10623522,
     author = {Li*, Han and Ma*, Yukai and Huang, Yuehao and Gu, Yaqing and Xu, Weihua and Liu, Yong and Zuo, Xingxing},
     journal = {IEEE Transactions on Intelligent Transportation Systems},
     title = {RIDERS: Radar-Infrared Depth Estimation for Robust Sensing},
     year = {2024},
     volume = {25},
     number = {11},
     pages = {18764-18778},
     keywords = {Radar;Radar imaging;Estimation;Cameras;Measurement;Laser radar;Accuracy;Autonomous driving;Infrared imaging;Multisensor systems;Depth estimation;radar perception;infrared camera;multi-sensor fusion},
     doi = {10.1109/TITS.2024.3432996},
   }

5. JFR

   FMCW Radar on LiDAR map localization in structural urban environments

   Yukai Ma*, Han Li*, Xiangrui Zhao, and 4 more authors

   Journal of Field Robotics, 2024

   Code

2023

1. RAL

   Geo-Localization With Transformer-Based 2D-3D Match Network

   Laijian Li*, Yukai Ma*, Kai Tang, and 5 more authors

   IEEE Robotics and Automation Letters, 2023

   Bib Code

   @article{10168166,
     author = {Li*, Laijian and Ma*, Yukai and Tang, Kai and Zhao, Xiangrui and Chen, Chao and Huang, Jianxin and Mei, Jianbiao and Liu, Yong},
     journal = {IEEE Robotics and Automation Letters},
     title = {Geo-Localization With Transformer-Based 2D-3D Match Network},
     year = {2023},
     volume = {8},
     number = {8},
     pages = {4855-4862},
     keywords = {Laser radar;Point cloud compression;Feature extraction;Three-dimensional displays;Satellites;Location awareness;Global Positioning System;Geo-localization;2D-3D match;SLAM},
     doi = {10.1109/LRA.2023.3290526},
   }

2. 

   RoLM: Radar on LiDAR Map Localization

   Yukai Ma, Xiangrui Zhao, Han Li, and 3 more authors

   In 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023

   Bib

   @inproceedings{10161203,
     author = {Ma, Yukai and Zhao, Xiangrui and Li, Han and Gu, Yaqing and Lang, Xiaolei and Liu, Yong},
     booktitle = {2023 IEEE International Conference on Robotics and Automation (ICRA)},
     title = {RoLM: Radar on LiDAR Map Localization},
     year = {2023},
     volume = {},
     number = {},
     pages = {3976-3982},
     keywords = {Location awareness;Laser radar;Automation;Autonomous systems;Robot sensing systems;Cameras;Robustness},
     doi = {10.1109/ICRA48891.2023.10161203},
   }