1 code implementation • 15 Dec 2024 • Guan Wang, Haoyi Niu, Jianxiong Li, Li Jiang, Jianming Hu, Xianyuan Zhan
Among various branches of offline reinforcement learning (RL) methods, goal-conditioned supervised learning (GCSL) has gained increasing popularity as it formulates the offline RL problem as a sequential modeling task, therefore bypassing the notoriously difficult credit assignment challenge of value learning in conventional RL paradigm.
no code implementations • 13 Dec 2024 • Zhihang Song, Lihui Peng, Jianming Hu, Danya Yao, Yi Zhang
The multi-modal perception methods are thriving in the autonomous driving field due to their better usage of complementary data from different sensors.
no code implementations • 4 Dec 2024 • Ruibo Ming, Jingwei Wu, Zhewei Huang, Zhuoxuan Ju, Jianming Hu, Lihui Peng, Shuchang Zhou
Recent advances in auto-regressive large language models (LLMs) have shown their potential in generating high-quality text, inspiring researchers to apply them to image and video generation.
no code implementations • 22 Sep 2024 • Shu Li, Honglin He, Jingxuan Yang, Jianming Hu, Yi Zhang, Shuo Feng
This severely hinders the testing and evaluation process, especially for third-party testers and governmental bodies with very limited testing budgets.
1 code implementation • 13 Sep 2024 • Haoyi Niu, Qimao Chen, Tenglong Liu, Jianxiong Li, Guyue Zhou, Yi Zhang, Jianming Hu, Xianyuan Zhan
This process effectively corrects underlying domain gaps, enhancing state realism and dynamics reliability in source data, and allowing flexible integration with various single-domain and cross-domain downstream policy learning methods.
no code implementations • 27 May 2024 • Zhihang Song, Lihui Peng, Jianming Hu, Danya Yao, Yi Zhang
As the research on the calibration influence on fusion detection performance is relatively few, flexible calibration dependency multi-sensor detection method has always been attractive.
no code implementations • 29 Feb 2024 • Jingxuan Yang, Ruoxuan Bai, Haoyuan Ji, Yi Zhang, Jianming Hu, Shuo Feng
A common approach involves designing testing scenarios based on prior knowledge of CAVs (e. g., surrogate models), conducting tests in these scenarios, and subsequently evaluating CAVs' safety performances.
1 code implementation • 7 Feb 2024 • Haoyi Niu, Jianming Hu, Guyue Zhou, Xianyuan Zhan
Consequently, researchers often resort to data from easily accessible source domains, such as simulation and laboratory environments, for cost-effective data acquisition and rapid model iteration.
no code implementations • 2 Feb 2024 • Shu Li, Jingxuan Yang, Honglin He, Yi Zhang, Jianming Hu, Shuo Feng
To alleviate the considerable uncertainty inherent in a small testing scenario set, we frame the FST problem as an optimization problem and search for the testing scenario set based on neighborhood coverage and similarity.
no code implementations • 26 Jan 2024 • Ruibo Ming, Zhewei Huang, Zhuoxuan Ju, Jianming Hu, Lihui Peng, Shuchang Zhou
Future Frame Synthesis (FFS) aims to enable models to generate sequences of future frames based on existing content.
1 code implementation • 25 Sep 2023 • Haoyi Niu, Qimao Chen, Yingyue Li, Yi Zhang, Jianming Hu
The deployment of autonomous vehicles (AVs) has faced hurdles due to the dominance of rare but critical corner cases within the long-tail distribution of driving scenarios, which negatively affects their overall performance.
1 code implementation • 25 Sep 2023 • Haoyi Niu, Yizhou Xu, Xingjian Jiang, Jianming Hu
To tackle this challenge, a surge of research in scenario-based autonomous driving has emerged, with a focus on generating high-risk driving scenarios and applying them to conduct safety-critical testing of AV models.
no code implementations • 22 Sep 2023 • Haoyi Niu, Tianying Ji, Bingqi Liu, Haocheng Zhao, Xiangyu Zhu, Jianying Zheng, Pengfei Huang, Guyue Zhou, Jianming Hu, Xianyuan Zhan
Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of offline data can be quite challenging.
no code implementations • 24 Apr 2023 • Zhihang Song, Zimin He, Xingyu Li, Qiming Ma, Ruibo Ming, Zhiqi Mao, Huaxin Pei, Lihui Peng, Jianming Hu, Danya Yao, Yi Zhang
In this paper, we summarize the evolution of synthetic dataset generation methods and review the work to date in synthetic datasets related to single and multi-task categories for to autonomous driving study.
2 code implementations • 27 Feb 2023 • Haoyi Niu, Kun Ren, Yizhou Xu, Ziyuan Yang, Yichen Lin, Yi Zhang, Jianming Hu
Autonomous driving and its widespread adoption have long held tremendous promise.
7 code implementations • 5 Oct 2022 • Silvio Giancola, Anthony Cioppa, Adrien Deliège, Floriane Magera, Vladimir Somers, Le Kang, Xin Zhou, Olivier Barnich, Christophe De Vleeschouwer, Alexandre Alahi, Bernard Ghanem, Marc Van Droogenbroeck, Abdulrahman Darwish, Adrien Maglo, Albert Clapés, Andreas Luyts, Andrei Boiarov, Artur Xarles, Astrid Orcesi, Avijit Shah, Baoyu Fan, Bharath Comandur, Chen Chen, Chen Zhang, Chen Zhao, Chengzhi Lin, Cheuk-Yiu Chan, Chun Chuen Hui, Dengjie Li, Fan Yang, Fan Liang, Fang Da, Feng Yan, Fufu Yu, Guanshuo Wang, H. Anthony Chan, He Zhu, Hongwei Kan, Jiaming Chu, Jianming Hu, Jianyang Gu, Jin Chen, João V. B. Soares, Jonas Theiner, Jorge De Corte, José Henrique Brito, Jun Zhang, Junjie Li, Junwei Liang, Leqi Shen, Lin Ma, Lingchi Chen, Miguel Santos Marques, Mike Azatov, Nikita Kasatkin, Ning Wang, Qiong Jia, Quoc Cuong Pham, Ralph Ewerth, Ran Song, RenGang Li, Rikke Gade, Ruben Debien, Runze Zhang, Sangrok Lee, Sergio Escalera, Shan Jiang, Shigeyuki Odashima, Shimin Chen, Shoichi Masui, Shouhong Ding, Sin-wai Chan, Siyu Chen, Tallal El-Shabrawy, Tao He, Thomas B. Moeslund, Wan-Chi Siu, Wei zhang, Wei Li, Xiangwei Wang, Xiao Tan, Xiaochuan Li, Xiaolin Wei, Xiaoqing Ye, Xing Liu, Xinying Wang, Yandong Guo, YaQian Zhao, Yi Yu, YingYing Li, Yue He, Yujie Zhong, Zhenhua Guo, Zhiheng Li
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
1 code implementation • 27 Jun 2022 • Haoyi Niu, Shubham Sharma, Yiwen Qiu, Ming Li, Guyue Zhou, Jianming Hu, Xianyuan Zhan
This brings up a new question: is it possible to combine learning from limited real data in offline RL and unrestricted exploration through imperfect simulators in online RL to address the drawbacks of both approaches?
2 code implementations • 22 Oct 2021 • Guan Wang, Haoyi Niu, Desheng Zhu, Jianming Hu, Xianyuan Zhan, Guyue Zhou
Heated debates continue over the best autonomous driving framework.
no code implementations • 25 Jul 2021 • Haoyi Niu, Jianming Hu, Zheyu Cui, Yi Zhang
How to explore corner cases as efficiently and thoroughly as possible has long been one of the top concerns in the context of deep reinforcement learning (DeepRL) autonomous driving.
no code implementations • 9 Sep 2020 • Haoyi Niu, Jianming Hu, Zheyu Cui, Yi Zhang
The following approach reveals that DRL could complement rule-based avoiding strategy in generalization, and on the contrary, the rule-based avoiding strategy could complement DRL in stability, and their combination could lead to less response time, lower collision rate and smoother trajectory.
no code implementations • 30 Sep 2019 • Yusen Huo, Qinghua Tao, Jianming Hu
In the proposed model, a multi-task learning structure is used to get the cooperative policy by learning.
no code implementations • 20 Jun 2019 • Guan Wang, Jianming Hu, Zhiheng Li, Li Li
In this paper, we study how to learn an appropriate lane changing strategy for autonomous vehicles by using deep reinforcement learning.
no code implementations • 25 Nov 2016 • Xiaolong Ma, Xiatian Zhu, Shaogang Gong, Xudong Xie, Jianming Hu, Kin-Man Lam, Yisheng Zhong
Crucially, this model does not require pairwise labelled training data (i. e. unsupervised) therefore readily scalable to large scale camera networks of arbitrary camera pairs without the need for exhaustive data annotation for every camera pair.