1 code implementation • 6 Feb 2024 • Guohang Yan, Jiahao Pi, Jianfei Guo, Zhaotong Luo, Min Dou, Nianchen Deng, Qiusheng Huang, Daocheng Fu, Licheng Wen, Pinlong Cai, Xing Gao, Xinyu Cai, Bo Zhang, Xuemeng Yang, Yeqi Bai, Hongbin Zhou, Botian Shi
With the development of implicit rendering technology and in-depth research on using generative models to produce data at scale, we propose OASim, an open and adaptive simulator and autonomous driving data generator based on implicit neural rendering.
no code implementations • 20 Dec 2023 • Lening Wang, Yilong Ren, Han Jiang, Pinlong Cai, Daocheng Fu, Tianqi Wang, Zhiyong Cui, Haiyang Yu, Xuesong Wang, Hanchu Zhou, Helai Huang, Yinhai Wang
For human-driven vehicles, we offer proactive long-range safety warnings and blind-spot alerts while also providing safety driving recommendations and behavioral norms through human-machine dialogue and interaction.
1 code implementation • 7 Dec 2023 • Xin Li, Yeqi Bai, Pinlong Cai, Licheng Wen, Daocheng Fu, Bo Zhang, Xuemeng Yang, Xinyu Cai, Tao Ma, Jianfei Guo, Xing Gao, Min Dou, Yikang Li, Botian Shi, Yong liu, Liang He, Yu Qiao
This paper explores the emerging knowledge-driven autonomous driving technologies.
1 code implementation • 9 Nov 2023 • Licheng Wen, Xuemeng Yang, Daocheng Fu, XiaoFeng Wang, Pinlong Cai, Xin Li, Tao Ma, Yingxuan Li, Linran Xu, Dengke Shang, Zheng Zhu, Shaoyan Sun, Yeqi Bai, Xinyu Cai, Min Dou, Shuanglu Hu, Botian Shi, Yu Qiao
This has been a significant bottleneck, particularly in the development of common sense reasoning and nuanced scene understanding necessary for safe and reliable autonomous driving.
2 code implementations • 28 Sep 2023 • Licheng Wen, Daocheng Fu, Xin Li, Xinyu Cai, Tao Ma, Pinlong Cai, Min Dou, Botian Shi, Liang He, Yu Qiao
Recent advancements in autonomous driving have relied on data-driven approaches, which are widely adopted but face challenges including dataset bias, overfitting, and uninterpretability.
1 code implementation • 13 Sep 2023 • Siyao Zhang, Daocheng Fu, Zhao Zhang, Bin Yu, Pinlong Cai
This integration yields the following key enhancements: 1) empowering ChatGPT with the capacity to view, analyze, process traffic data, and provide insightful decision support for urban transportation system management; 2) facilitating the intelligent deconstruction of broad and complex tasks and sequential utilization of traffic foundation models for their gradual completion; 3) aiding human decision-making in traffic control through natural language dialogues; and 4) enabling interactive feedback and solicitation of revised outcomes.
1 code implementation • 14 Jul 2023 • Daocheng Fu, Xin Li, Licheng Wen, Min Dou, Pinlong Cai, Botian Shi, Yu Qiao
In this paper, we explore the potential of using a large language model (LLM) to understand the driving environment in a human-like manner and analyze its ability to reason, interpret, and memorize when facing complex scenarios.
1 code implementation • 13 Jul 2023 • Licheng Wen, Daocheng Fu, Song Mao, Pinlong Cai, Min Dou, Yikang Li, Yu Qiao
With the growing popularity of digital twin and autonomous driving in transportation, the demand for simulation systems capable of generating high-fidelity and reliable scenarios is increasing.