25 citations · 58 across the 22 of their papers we have counts for
22 papers
Policy Disruption in Reinforcement Learning:Adversarial Attack with Large Language Models and Critical State Identification
Junyong Jiang, Buwei Tian, Chenxing Xu +2
Reinforcement learning (RL) has achieved remarkable success in fields like robotics and autonomous driving, but adversarial attacks designed to mislead RL systems remain challengin…
Multi-agent Application System in Office Collaboration Scenarios
Songtao Sun, Jingyi Li, Yuanfei Dong +4
This paper introduces a multi-agent application system designed to enhance office collaboration efficiency and work quality. The system integrates artificial intelligence, machine…
ChatDyn: Language-Driven Multi-Actor Dynamics Generation in Street Scenes
Yuxi Wei, Jingbo Wang, Yuwen Du +6
Generating realistic and interactive dynamics of traffic participants according to specific instruction is critical for street scene simulation. However, there is currently a lack…
Language-Driven Interactive Traffic Trajectory Generation
Junkai Xia, Chenxin Xu, Qingyao Xu +3
Realistic trajectory generation with natural language control is pivotal for advancing autonomous vehicle technology. However, previous methods focus on individual traffic particip…
Ethical-Lens: Curbing Malicious Usages of Open-Source Text-to-Image Models
Yuzhu Cai, Sheng Yin, Yuxi Wei +5
The burgeoning landscape of text-to-image models, exemplified by innovations such as Midjourney and DALLE 3, has revolutionized content creation across diverse sectors. However, th…
Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End System
Genjia Liu, Yue Hu, Chenxin Xu +8
Vehicle-to-everything-aided autonomous driving (V2X-AD) has a huge potential to provide a safer driving solution. Despite extensive researches in transportation and communication t…
Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning
Shuo Tang, Rui Ye, Chenxin Xu +3
Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server, with each agent solving varied…
Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents
Yuxi Wei, Zi Wang, Yifan Lu +5
Scene simulation in autonomous driving has gained significant attention because of its huge potential for generating customized data. However, existing editable scene simulation ap…
Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals
Shaoheng Fang, Zuhong Liu, Mingyu Wang +3
Learning the dense bird's eye view (BEV) motion flow in a self-supervised manner is an emerging research for robotics and autonomous driving. Current self-supervised methods mainly…
Compatible Transformer for Irregularly Sampled Multivariate Time Series
Yuxi Wei, Juntong Peng, Tong He +4
To analyze multivariate time series, most previous methods assume regular subsampling of time series, where the interval between adjacent measurements and the number of samples rem…
Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Prediction
Chenxin Xu, Robby T. Tan, Yuhong Tan +3
Exploring spatial-temporal dependencies from observed motions is one of the core challenges of human motion prediction. Previous methods mainly focus on dedicated network structure…
Joint-Relation Transformer for Multi-Person Motion Prediction
Qingyao Xu, Weibo Mao, Jingze Gong +5
Multi-person motion prediction is a challenging problem due to the dependency of motion on both individual past movements and interactions with other people. Transformer-based meth…
FedDisco: Federated Learning with Discrepancy-Aware Collaboration
Rui Ye, Mingkai Xu, Jianyu Wang +3
This work considers the category distribution heterogeneity in federated learning. This issue is due to biased labeling preferences at multiple clients and is a typical setting of…
EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning
Chenxin Xu, Robby T. Tan, Yuhong Tan +4
Learning to predict agent motions with relationship reasoning is important for many applications. In motion prediction tasks, maintaining motion equivariance under Euclidean geomet…
Leapfrog Diffusion Model for Stochastic Trajectory Prediction
Weibo Mao, Chenxin Xu, Qi Zhu +2
To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a sophisticated multi-modal distribution of future trajectories. Emerging diffusion models…
FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning
Rui Ye, Zhenyang Ni, Chenxin Xu +3
One of the key challenges in federated learning (FL) is local data distribution heterogeneity across clients, which may cause inconsistent feature spaces across clients. To address…
Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting
Bohan Tang, Yiqi Zhong, Chenxin Xu +5
In multi-modal multi-agent trajectory forecasting, two major challenges have not been fully tackled: 1) how to measure the uncertainty brought by the interaction module that causes…
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning
Chenxin Xu, Yuxi Wei, Bohan Tang +3
Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works mainly c…
GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoning
Chenxin Xu, Maosen Li, Zhenyang Ni +2
Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works only con…
Remember Intentions: Retrospective-Memory-based Trajectory Prediction
Chenxin Xu, Weibo Mao, Wenjun Zhang +1
To realize trajectory prediction, most previous methods adopt the parameter-based approach, which encodes all the seen past-future instance pairs into model parameters. However, in…
Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks
Chenxin Xu, Rong Xia, Yong Xiao +3
With the fast growing demand on new services and applications as well as the increasing awareness of data protection, traditional centralized traffic classification approaches are…
Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation
Chenxin Xu, Siheng Chen, Maosen Li +1
We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learnin…