1 code implementation • 14 Mar 2024 • Tianyuan Yuan, Yucheng Mao, Jiawei Yang, Yicheng Liu, Yue Wang, Hang Zhao
Autonomous vehicles rely extensively on perception systems to navigate and interpret their surroundings.
no code implementations • 19 Feb 2024 • Xiaoyu Tian, Junru Gu, Bailin Li, Yicheng Liu, Chenxu Hu, Yang Wang, Kun Zhan, Peng Jia, Xianpeng Lang, Hang Zhao
We introduce DriveVLM, an autonomous driving system leveraging Vision-Language Models (VLMs) for enhanced scene understanding and planning capabilities.
1 code implementation • 24 Aug 2023 • Tianyuan Yuan, Yicheng Liu, Yue Wang, Yilun Wang, Hang Zhao
This approach limits their stability and performance in complex scenarios such as occlusions, largely due to the absence of temporal information.
no code implementations • CVPR 2023 • Xuan Xiong, Yicheng Liu, Tianyuan Yuan, Yue Wang, Yilun Wang, Hang Zhao
To the best of our knowledge, this is the first learning-based system for creating a global map prior.
1 code implementation • IEEE Transactions on Neural Networks and Learning Systems 2023 • Jie Wen, Chengliang Liu, Shijie Deng, Yicheng Liu, Lunke Fei, Ke Yan, Yong Xu
View missing and label missing are two challenging problems in the applications of multi-view multi-label classification scenery.
1 code implementation • 3 Oct 2022 • Chumeng Liang, Zherui Huang, Yicheng Liu, Zhanyu Liu, Guanjie Zheng, Hanyuan Shi, Kan Wu, Yuhao Du, Fuliang Li, Zhenhui Li
To the best of our knowledge, CBLab is the first infrastructure supporting traffic control policy optimization in large-scale urban scenarios.
2 code implementations • 17 Jun 2022 • Yicheng Liu, Tianyuan Yuan, Yue Wang, Yilun Wang, Hang Zhao
To the best of our knowledge, VectorMapNet is the first work designed towards end-to-end vectorized map learning from onboard observations.
Ranked #1 on HD semantic map learning on nuScenes
1 code implementation • CVPR 2021 • Yicheng Liu, Jinghuai Zhang, Liangji Fang, Qinhong Jiang, Bolei Zhou
Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety of autonomous driving.
1 code implementation • CUHK Course IERG5350 2020 • Yicheng Liu, CAO Qianqian
To this end we proposed a world model to model popular reinforcement learning environments through compressed spatio-temporal representations, which allow model-free method learning behaviors from imagined outcomes to increase sample-efficiency.
1 code implementation • NeurIPS 2020 • Wenchao Chen, Chaojie Wang, Bo Chen, Yicheng Liu, Hao Zhang, Mingyuan Zhou
Incorporating the natural document-sentence-word structure into hierarchical Bayesian modeling, we propose convolutional Poisson gamma dynamical systems (PGDS) that introduce not only word-level probabilistic convolutions, but also sentence-level stochastic temporal transitions.
1 code implementation • 21 Jul 2019 • Dong Wang, Yicheng Liu, Wenwo Tang, Fanhua Shang, Hongying Liu, Qigong Sun, Licheng Jiao
In this paper, we propose a new first-order gradient-based algorithm to train deep neural networks.