Advances in flexible sensors for intelligent perception system enhanced by artificial intelligence

Intelligent perception means that with the assistance of artificial intelligence(AI)-motivated brain,flexible sensors achieve the ability of memory,learning,judgment,and reasoning about external information like the human brain.Due to the superiority of machine learning(ML)algorithms in data processing and intelligent recognition,intelligent perception systems possess the ability to match or even surpass human perception systems.However,the built-in flexible sensors in these systems need to work on dynamic and irregular surfaces,inevitably affecting the precision and fidelity of the acquired data.In recent years,the strategy of introducing the developed functional materials and innovative structures into flexible sensors has made some progress toward the above challenges,and with the blessing of ML algorithms,accurate perception and reasoning in various scenarios have been achieved.Here,the most representative functional materials and innovative structures for constructing flexible sensors are comprehensively reviewed,the research progress of intelligent perception systems based on flexible sensors and ML algorithms is further summarized,and the intersection of the two is expected to unlock new opportunities for next-stage AI development.

Research on Active Safety Methodologies for Intelligent Railway Systems

Safety is essential when building a strong transportation system.As a key development direction in the global railway system,the intelligent railway has safety at its core,making safety a top priority while pursuing the goals of efficiency,convenience,economy,and environmental friendliness.This paper describes the state of the art and proposes a system architecture for intelligent railway systems.It also focuses on the development of railway safety technology at home and abroad,and proposes the active safety method and technology system based on advanced theoretical methods such as the in-depth integration of cyber–physical systems(CPS),data-driven models,and intelligent computing.Finally,several typical applications are demonstrated to verify the advancement and feasibility of active safety technology in intelligent railway systems.

Forecasting Future Trajectories with an Improved Transformer Network

An increase in car ownership brings convenience to people’s life.However,it also leads to frequent traffic accidents.Precisely forecasting surrounding agents’future trajectories could effectively decrease vehicle-vehicle and vehicle-pedestrian collisions.Long-short-term memory(LSTM)network is often used for vehicle trajectory prediction,but it has some shortages such as gradient explosion and low efficiency.A trajectory prediction method based on an improved Transformer network is proposed to forecast agents’future trajectories in a complex traffic environment.It realizes the transformation from sequential step processing of LSTM to parallel processing of Transformer based on attentionmechanism.To performtrajectory predictionmore efficiently,a probabilistic sparse self-attention mechanism is introduced to reduce attention complexity by reducing the number of queried values in the attention mechanism.Activate or not(ACON)activation function is adopted to select whether to activate or not,hence improving model flexibility.The proposed method is evaluated on the publicly available benchmarks nextgeneration simulation(NGSIM)and ETH/UCY.The experimental results indicate that the proposed method can accurately and efficiently predict agents’trajectories.

面向智能视觉感知的神经形态传感器的原理和应用

处理非结构化的视觉数据不仅需要计算机执行高效的计算,还要求边缘视觉感知设备有效地获取和处理信息.因此,必须开发集感知、存储和运算功能于一体的低功耗神经形态光学传感器.它们可以通过将光感受器与常规的神经形态器件相结合,或者通过开发神经形态光电探测器来实现.在本文中,我们首先介绍了常规神经形态器件的工作机制和所需的基本参数.然后对神经形态光电探测器的分类、工作机理和应用进行了综述.神经形态器件已经在视觉感知方面显示出了巨大的潜力,但是仍有一些研究方向需要被进一步探索,如更微观的机制阐述、更稳定的多功能器件和更丰富的应用场景.