Zhuo Sun

The digital twin (DT) is a concept widely used in industry to create digital replicas of physical objects or systems. The dynamic, bi-directional link between the physical entity and its digital counterpart enables a real-time update of the digital entity. It can predict perturbations related to the physical object's function. The obvious applications of DTs in healthcare and medicine are extremely attractive prospects that have the potential to revolutionize patient diagnosis and treatment. However, challenges including technical obstacles, biological heterogeneity, and ethical considerations make it difficult to achieve the desired goal. Advances in multi-modal deep learning methods, embodied AI agents, and the metaverse may mitigate some difficulties. Here, we discuss the basic concepts underlying DTs, the requirements for implementing DTs in medicine, and their current and potential healthcare uses. We also provide our perspective on five hallmarks for a healthcare DT system to advance research in this field.

With the development of intelligent vehicle technology, many studies have been focused on developing human-like trajectory planning methods for automated driving systems. Although data-driven methods are widely used for human driver behavior learning, there have been fewer studies on realizing human-like trajectory planning by using the generation mechanism of driving behavior, especially under curve conditions, where the lane centerline has been denoted as a reference trajectory. In this paper, thirty-two skilled drivers were recruited to collect data under different curve conditions on a self-designed driver-in-the-loop system. The collected data are processed by dynamic time warping, trajectories with different lengths are warped and the abnormal data are removed. Based on the warped data, common characteristics and differences between left and right turning trajectories are compared and explored from the perspectives of drivers’ demand for turning performance and their visual attention mechanism. Then, by introducing the driver preview mechanism, two features with a strong ability to represent the generation mechanism of the driver’s curve driving behavior are introduced. Finally, the preview-based human-like trajectory planning model (PHTPM) is proposed, and it is verified and analyzed by comparative tests and generalizability tests. The results show that the introduction of the driver preview mechanism enables PHTPM to match the characteristics of skilled drivers accurately on left turnings and outperform them on right turnings.