Trajectory similarity computation is essential for various downstream applications, such as anomaly route detection, order matching, and digital contact tracing. However, its effectiveness is confined within a single system due to privacy concerns associated with sharing raw trajectories across different systems. In this paper, we propose CrossSim, a novel framework designed to efficiently retrieve similar trajectories across all systems while preserving individual privacy. Our framework comprises three main components: i) a Trajectory Encoding Model that transforms trajectories into high-quality representations, where similarity relationships are reflected by their distances; ii) a two-stage optimization mechanism, including a Contrastive Similarity Learning stage and a Federated Similarity Learning stage, that alleviates the impact of heterogeneous similarity relationships across different systems on model training without aggregating raw trajectories; iii) a Similar Trajectory Retrieval procedure that obtains top-k similar trajectories from all systems without sharing raw trajectories. We conduct comprehensive experiments on three real-world datasets to evaluate the effectiveness of our proposed framework. The evaluation results demonstrate that CrossSim outperforms all existing schemees.

Massive urban-scale vehicle trajectories benefit various downstream applications. However, trajectories collected from existing sensing systems are often incomplete, necessitating the recovery of coarse-grained trajectories. Considering that mobility knowledge learned from a single system is less representative of all vehicles or covers only partial road segments, it becomes essential to combine diverse data from multiple systems to support trajectory recovery. Therefore, we learn the impacts of mobility intentions and dynamic traffic conditions on the movement of vehicles from trajectories aggregated across different systems to recover their travel routes on unobservable road intersections. Nonetheless, aggregating raw data across multiple systems raises privacy concerns. This data isolation compounds challenges in acquiring comprehensive mobility intentions and traffic conditions, thereby impairing recovery performance. In this paper, we proposeCrossTrace, a two-stage framework for privacy-aware cross-system trajectory recovery: in theTraffic Condition Inferencestage, a Split Learning pipeline with a multi-view graph neural network is utilized to infer complete traffic conditions for all road segments; in theTrajectory Recoverystage, a Federated Learning pipeline with dedicated modules is utilized to recover missing points by fusing inferred traffic conditions and mobility intentions. Extensive experiments on two large-scale trajectory datasets demonstrate thatCrossTraceoutperforms all alternative schemes.

Obtaining urban-scale vehicle trajectories is essential to understand urban mobility and benefits various downstream applications. The mobility knowledge obtained from existing vehicle trajectory sensing techniques is typically incomplete. To fill the gap, we propose$F^{3}VeTrac$, an efficient deep-learning-based vehicle trajectory recovery system that utilizes complementary characteristics of the Camera Surveillance System and the Vehicle Tracking System to obtain fine-grained, fully-road-covered, and fully-individual-penetrative ($F^{3}$) trajectories.$F^{3}VeTrac$utilizes five well-designed modules to model the co-occurrence relationships hidden in both coarse-grained and fine-grained trajectories from the two complementary sensing systems and fuse them to recover the coarse-grained trajectories. We implement and evaluate$F^{3}VeTrac$with two real-world datasets from over 100 million regular vehicle trajectories and 16 million commercial vehicle trajectories in two cities of China, together with an on-field case study based on 251 regular vehicle trajectories collected by 17 volunteers, demonstrating its great advantages over six state-of-the-art alternative schemes. Moreover, we present a downstream application of$F^{3}VeTrac$for traffic condition estimation, which obtains obvious performance gains.

Neonatal Endotracheal Intubation (ETI) is a critical resuscitation skill that requires tremendous practice of trainees before clinical exposure. However, current manikin-based training regimen is ineffective in providing satisfactory real-time procedural guidance for accurate assessment due to the lack of see-through visualization within the manikin. The training efficiency is further reduced by the limited availability of expert instructors, which inevitably results in a long learning curve for trainees. To this end, we propose an intelligent Augmented Reality (AR) training framework that provides trainees with a complete visualization of the ETI procedure for real-time guidance and assessment. Specifically, the proposed framework is capable of capturing the motions of the laryngoscope and the manikin and offer 3D see-through visualization rendered to the head-mounted display (HMD). Furthermore, an attention-based Convolutional Neural Network (CNN) model is developed to automatically assess the ETI performance from the captured motions as well as identify regions of motions that significantly contribute to the performance evaluation. Lastly, augmented user-friendly feedback is delivered with interpretable results with the ETI scoring rubric through the color-coded motion trajectory that classifies highlighted regions that need more practice. The classification accuracy of our machine learning model is 84.6%.

We propose an innovative approach to enhance welding operations by using a cyber-physical system (CPS) with layered architecture and enabling a robot to be effectively operated by its commanding human. This article focuses on the recognition of the commanding human's intention that should be executed by the robot. To this end, a virtual reality (VR) system based on the HTC Vive is used to create a remote virtual welding environment. Human hand movement speed data is collected and used to train a hidden Markov model (HMM) using the Baum-Welch algorithm. The Bayesian information criterion (BIC) is applied to determine the number of hidden states. The state occupancy probability distribution (the probability of each state at a given time) is estimated based on the human hand movement speed sequence using the forward algorithm. The human intention, defined as the intended movement in this article, is then estimated as the statistical expectation of the observable variables. Using the proposed human intention estimation algorithm, the intended movement recognized from the raw movement data is smoother, which is preferable in welding tasks. A 6-DoF industrial robot, UR-5 with a custom gas tungsten arc welding (GTAW) torch installed, works as the final performer of the welding jobs. The robot receives the intended movement data from the HMM and uses this to assist the human welding operators. Welding experiments have been conducted both with and without the proposed human intention recognition (IR) algorithm. The results show that the robot can help the operators complete welding tasks with better performance using the proposed IR system, supporting the effectiveness of the proposed VR robot-assisted welding system. Note to Practitioners-Welding is not only labor-intensive but also requires real-time adaption to the process, which is challenging for robots/machines but relatively straightforward for humans. Using a human commanded robot to perform welding can liberate humans from laborious operations and hazardous environments. To this end, a virtual reality (VR) system is used to create a virtual welding environment for a human to view the process remotely and for a human to pass his/her resultant adaptation to the robot through hand movements. However, hand movements do not always fully represent the intended adaptation of the commanding human, and the recognition of such human intention is fundamental in such a proposed method. This article established the mathematical framework for the recognition of the human intention and, thus, the foundation for effective assistance of robots to humans.