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arXiv 2024年

作者： Dong, Yonghao Wang, Le Zhou, Sanping Hua, Gang Sun, Changyin Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China

Pedestrian trajectory prediction is a crucial component in computer vision and robotics, but remains challenging due to the domain shift problem. Previous studies have tried to tackle this problem by leveraging a portion of the trajectory data from the target domain to adapt the model. However, such domain adaptation methods are impractical in real-world scenarios, as it is infeasible to collect trajectory data from all potential target domains. In this paper, we study a task named generalized pedestrian trajectory prediction, with the aim of generalizing the model to unseen domains without accessing their trajectories. To tackle this task, we introduce a Recurrent Aligned Network (RAN) to minimize the domain gap through domain alignment. Specifically, we devise a recurrent alignment module to effectively align the trajectory feature spaces at both time-state and time-sequence levels by the recurrent alignment strategy. Furthermore, we introduce a pre-aligned representation module to combine social interactions with the recurrent alignment strategy, which aims to consider social interactions during the alignment process instead of just target trajectories. We extensively evaluate our method and compare it with state-of-the-art methods on three widely used benchmarks. The experimental results demonstrate the superior generalization capability of our method. Our work not only fills the gap in the generalization setting for practical pedestrian trajectory prediction but also sets strong baselines in this field. Copyright © 2024, The Authors. All rights reserved.

关键词： Trajectories

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IC-Mapper: Instance-Centric Spatio-Temporal Modeling for Online Vectorized Map Construction

arXiv

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arXiv 2025年

作者： Zhu, Jiangtong Yang, Zhao Shi, Yinan Fang, Jianwu Xue, Jianru Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

Online vector map construction based on visual data can bypass the processes of data collection, post-processing, and manual annotation required by traditional map construction, which significantly enhances map-building efficiency. However, existing work treats the online mapping task as a local range perception task, overlooking the spatial scalability required for map construction. We propose IC-Mapper, an instance-centric online mapping framework, which comprises two primary components: 1) Instance-centric temporal association module: For the detection queries of adjacent frames, we measure them in both feature and geometric dimensions to obtain the matching correspondence between instances across frames. 2) Instance-centric spatial fusion module: We perform point sampling on the historical global map from a spatial dimension and integrate it with the detection results of instances corresponding to the current frame to achieve real-time expansion and update of the map. Based on the nuScenes dataset, we evaluate our approach on detection, tracking, and global mapping metrics. Experimental results demonstrate the superiority of IC-Mapper against other state-of-the-art methods. Code will be released on https://***/Brickzhuantou/IC-Mapper. © 2025, CC BY-NC-SA.

关键词： Spatio-temporal data

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Dual-Hand Motion Capture by Using Biological Inspiration for Bionic Bimanual Robot Teleoperation

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Cyborg and Bionic Systems 2023年第1期4卷 93-105页

作者： Qing Gao Zhiwen Deng Zhaojie Ju Tianwei Zhang School of Electronics and Communication Engineering Shenzhen Campus of Sun Yat-sen UniversityShenzhenChina School of Computing University of PortsmouthPortsmouthUK Shenzhen Institute of Artificial Intelligence and Robotics for Society Chinese University of Hong KongShenzhenChina

Bionic bimanual robot teleoperation can transfer the grasping and manipulation skills of human dual hands to the bionic bimanual robots to realize natural and flexible *** motion capture of dual hands plays an important role in the *** motion information of dual hands can be captured through the hand detection,localization,and pose estimation and mapped to the bionic bimanual robots to realize the ***,although the motion capture technology has achieved great achievements in recent years,visual dual-hand motion capture is still a great ***,this work proposed a dual-hand detection method and a 3-dimensional(3D)hand pose estimation method based on body and hand biological inspiration to achieve convenient and accurate monocular dual-hand motion capture and bionic bimanual robot ***,a dual-hand detection method based on body structure constraints is proposed,which uses a parallel structure to combine hand and body relationship ***,a 3D hand pose estimation method with bone-constraint loss from single RGB images is ***,a bionic bimanual robot teleoperation method is designed by using the proposed hand detection and pose estimation *** results on public hand datasets show that the performances of the proposed hand detection and 3D hand pose estimation outperform state-of-the-art *** results on a bionic bimanual robot teleoperation platform shows the effectiveness of the proposed teleoperation method.

关键词： Capture Dual operation

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Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement 38

Diffusion Model with Cross Attention as an Inductive Bias fo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Tao Lan, Cuiling Lu, Yan Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China Microsoft Research Asia China

Disentangled representation learning strives to extract the intrinsic factors within the observed data. Factoring these representations in an unsupervised manner is notably challenging and usually requires tailored loss functions or specific structural designs. In this paper, we introduce a new perspective and framework, demonstrating that diffusion models with cross-attention itself can serve as a powerful inductive bias to facilitate the learning of disentangled representations. We propose to encode an image into a set of concept tokens and treat them as the condition of the latent diffusion model for image reconstruction, where cross attention over the concept tokens is used to bridge the encoder and the U-Net of the diffusion model. We analyze that the diffusion process inherently possesses the time-varying information bottlenecks. Such information bottlenecks and cross attention act as strong inductive biases for promoting disentanglement. Without any regularization term in the loss function, this framework achieves superior disentanglement performance on the benchmark datasets, surpassing all previous methods with intricate designs. We have conducted comprehensive ablation studies and visualization analyses, shedding a light on the functioning of this model. We anticipate that our findings will inspire more investigation on exploring diffusion model for disentangled representation learning towards more sophisticated data analysis and understanding. © 2024 Neural information processing systems foundation. All rights reserved.

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Quenched PVDF/PMMA Porous Matrix for Triboelectric Energy Harvesting and Sensing

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Energy & Environmental Materials 2025年第1期8卷 229-238页

作者： Assem Mubarak Bayandy Sarsembayev Yerzhigit Serik Abdirakhman Onabek Zhanat Kappassov Zhumabay Bakenov Kazuyoshi Tsuchiya Gulnur Kalimuldina Department of Mechanical and Aerospace Engineering School of Engineering and Digital Sciences Nazarbayev University Institute of Smart Systems and Artificial Intelligence Nazarbayev University Department of Robotics Engineering School of Engineering and Digital Sciences Nazarbayev University Department of Chemical and Materials Engineering School of Engineering and Digital Sciences Nazarbayev University Micro/Nano Technology Center Tokai University

The rapid development of nanotechnology has significantly revolutionized wearable electronics and expanded their *** introducing innovative solutions for energy harvesting and autonomous sensing,this research presents a cost-effective strategy to enhance the performance of triboelectric nanogenerators(TENGs).The TENG was fabricated from polyvinylidene fluoride(PVDF) and N,N'-poly(methyl methacrylate)(PMMA) blend with a porous structure via a novel optimized quenching method.

关键词： N,N′-poly(methyl methacrylate) (PMMA) robotic tactile sensors polyvinylidene fluoride (PVDF) quenching triboelectric nanogenerators (TENGs)

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CPL-CLIP: Compound Prompt Learning for Flexible-Modal Face Anti-Spoofing 18

CPL-CLIP: Compound Prompt Learning for Flexible-Modal Face A...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Zhang, Ying Zhu, Xiangyu Liu, Ajian Lin, Xun Wan, Jun Zhang, Jing Lei, Zhen Beihang University School of Computer Science and Engineering China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation China University of Chinese Academy of Sciences School of Artificial Intelligence China Macau University of Science and Technology School of Computer Science and Engineering China Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation China

ISBN: (纸本)9798350364132

Face anti-spoofing (FAS) is pivotal in safeguarding the integrity of face recognition systems. Flexible-modal FAS utilizes multi-modal data and trains a unified model adaptable to any single-modal testing scenario. This innovation addresses the shortcomings of conventional multi-modal FAS approaches, which typically demand separate model training and deployment for each modality. However, existing flexible-modal FAS approaches activate specific network branches based on the modality of the tested sample. This not only increases the model's parameters but also necessitates the provision of the image's modality for testing, thereby constraining deployment flexibility. To address the issue, we present Compound Prompt Learning CLIP (CPL-CLIP), a novel method for flexible-modal FAS. This approach capitalizes on a learned textual prompt that is nearly independent of modality, thus bolstering class-based classification across arbitrary modalities. Specifically, our CPL-CLIP introduces a Dual-Branch Prompt (DBP), consisting of class and modal prompts that describe and guide classification, where each prompt is composed of learnable vectors and fixed templates. To further render the class prompt as modality-agnostic as possible, a Cosine Similarity Loss (CSL) is proposed to facilitate the maximal separation of the class prompt from the modality prompt. With only the class prompt utilized during testing, CPL-CLIP enables deployment in diverse modal testing scenarios without the necessity of the test image's modality to be known. Extensive experiments demonstrate CPL-CLIP's superiority over existing methods on several flexible-modal FAS benchmarks. © 2024 IEEE.

关键词： Face recognition

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FT-TF: A 4D Long-Term Flight Trajectory Prediction Method Based on Transformer 42

FT-TF: A 4D Long-Term Flight Trajectory Prediction Method Ba...

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42nd Chinese Control Conference, CCC 2023

作者： Luo, Shijin Zhao, Minghui Zhao, Zhenjie Li, Lun Zhang, Shiyong Zhang, Xuebo Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300350 China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin300350 China

ISBN: (纸本)9789887581543

The accurate prediction of target aircraft trajectory in the process of air combat can significantly improve the ability of aircraft to gain air superiority. Most of the trajectory prediction methods currently applied in air combat are based on traditional time-series prediction algorithms such as LSTM with short prediction steps, which can not realize the demand for long-term prediction in air combat. To address the issue, a novel prediction method FT-TF is proposed in this paper. FT-TF is a multi-step 4D flight trajectory prediction method based on Transformer applied to further improve the accuracy and practicality of trajectory prediction problems. FT-TF uses the first 30 steps of aircraft historical trajectory data as input and the last 30 steps as output. Flight trajectory data sets of aircraft in air combat are used in the experiment to make the trained model closer to the real air combat scene. The experimental results show that the proposed FT-TF can achieve higher prediction accuracy than LSTM and BP neural network. Related Transformer variants of the algorithm are also added to the experiment for comparison, the results demonstrate that the proposed method achieves the smallest prediction error. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Aircraft

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A Deep Reinforcement Learning Method for Charging Station Management and Load Balancing

A Deep Reinforcement Learning Method for Charging Station Ma...

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2023 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2023

作者： Liu, Jie Liu, Zifan Tang, Xiaoying School of Science and Engineering Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Guangdong Provincial Key Laboratory of Future Networks of Intelligence China

ISBN: (纸本)9798350314274

Electric vehicles (EVs) and the corresponding renewable energy-based charging stations (CSs) are rapidly gaining popularity as an effective solution for environmental protection. However, the increasing adoption of EVs and CSs can lead to load fluctuations and pose safety risks to the grid. To address this issue and develop a practical management strategy for CSs, we propose a load-constraint hierarchical deep reinforcement learning approach. Our method aims to achieve two main objectives: 1) maximize the total profit and balance the load for CSs, taking into account the unpredictable behavior of EV users;and 2) separate pricing and charging scheduling actions to enhance training efficiency. We conducted experiments using real data, and the results validate the effectiveness of our algorithm in achieving load balancing and maximizing profits for CSs. © 2023 IEEE.

关键词： Renewable energy

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An Acceleration Inference Implementation of BEVFusion with MQBench on Xavier

An Acceleration Inference Implementation of BEVFusion with M...

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2023 China Automation Congress, CAC 2023

作者： Long, Yaqin Zan, Xin Xue, Jianru Mei, Kuizhi Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University Xi'an China School of Automation Science and Engineering Xi'An Jiaotong University Xi'an China

ISBN: (纸本)9798350303759

The integration of sensor technology and deep learning has made multi-sensor fusion algorithms an essential component of autonomous driving perception. Among these algorithms, BEVFusion performs well in 3D perception tasks. However, its high computing cost presents challenges for deployment and application on the hardware. In this work, we propose a way that can trade off efficiency and performance to compress and deploy it on Jetson Xavier NX. We first decouple the model into three parts and then use MQBench to quantize the Fusion and Decoder of BEV in various ways. After that, we accelerate it on the hardware with the parameters of those schemes by utilizing *** analyzing the mAP and inference time of the model in different quantization factors, we obtain the optimal schemes when using FP32 as scale, per-channel as granularity and EMAMinMax as calibration algorithm, whose accuracy only experiences a marginal drop of 0.12% while the execution speed witnesses a remarkable increase of 98.3% compared to the original model. The significant improvement makes the application of BEVFusion feasible for real-time autonomous driving perception. © 2023 IEEE.

关键词： Autonomous vehicles

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Dual Role AoI-based Incentive Mechanism for HD map Crowdsourcing 24

Dual Role AoI-based Incentive Mechanism for HD map Crowdsour...

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Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems

作者： Wentao Ye Bo Liu Yuan Luo Jianwei Huang Chinese University of Hong Kong Shenzhen & Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400704864

A high-quality fresh high-definition (HD) map is vital in enhancing transportation efficiency and safety in autonomous driving. Vehicle-based crowdsourcing offers a promising approach for updating HD maps. However, recruiting crowdsourcing vehicles involves making the challenging tradeoff between the HD map freshness and recruitment cost. Existing studies on HD map crowdsourcing often (1) prioritize maximizing spatial coverage, and (2) overlook the dual role of crowdsourcing vehicles in HD maps, as vehicles serve both as contributors and customers of HD maps. This motivates us to propose the Dual-Role Age of Information (AoI) based Incentive Mechanism (DRAIM) to address these issues. DRAIM aims to achieve the company's tradeoff between freshness and recruitment cost.

关键词： age of information crowdsourcing dual role of the crowdsourcing vehicle hd map incentive mechanism

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