Search Results for author: Jianming Hu

Found 16 papers, 7 papers with code

A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agents

1 code implementation7 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.

Few-Shot Scenario Testing for Autonomous Vehicles Based on Neighborhood Coverage and Similarity

no code implementations2 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 and optimize scenario utilization, we frame the FST problem as an optimization problem and search for a small scenario set based on neighborhood coverage and similarity.

Autonomous Vehicles

A Survey on Video Prediction: From Deterministic to Generative Approaches

no code implementations26 Jan 2024 Ruibo Ming, Zhewei Huang, Zhuoxuan Ju, Jianming Hu, Lihui Peng, Shuchang Zhou

Video prediction, a fundamental task in computer vision, aims to enable models to generate sequences of future frames based on existing video content.

Video Prediction

Stackelberg Driver Model for Continual Policy Improvement in Scenario-Based Closed-Loop Autonomous Driving

1 code implementation25 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.

Autonomous Driving

Continual Driving Policy Optimization with Closed-Loop Individualized Curricula

1 code implementation25 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.

Autonomous Driving

H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps

no code implementations22 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.

Offline RL Reinforcement Learning (RL)

Synthetic Datasets for Autonomous Driving: A Survey

no code implementations24 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.

Autonomous Driving

SoccerNet 2022 Challenges Results

7 code implementations5 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.

Action Spotting Camera Calibration +3

When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning

1 code implementation27 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?

Offline RL reinforcement-learning +1

DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning

no code implementations25 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.

Autonomous Driving reinforcement-learning +1

Tactical Decision Making for Emergency Vehicles Based on A Combinational Learning Method

no code implementations9 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.

Decision Making

Cooperative Lane Changing via Deep Reinforcement Learning

no code implementations20 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.

Autonomous Vehicles reinforcement-learning +1

Person Re-Identification by Unsupervised Video Matching

no code implementations25 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.

Benchmarking Dynamic Time Warping +2

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