RESUMO
Collaborative perception between a vehicle and the road has the potential to enhance the limited perception capability of autonomous driving technologies. With this background, self-powered vehicle-road integrated electronics (SVRIE) with a multilevel fractal structure is designed to play a dual role, including a SVRIE device integrated into vehicle tires and a SVRIE array embedded into a road surface. The pressure sensing capability and anti-crosstalk performance of the SVRIE array are characterized separately to validate the feasibility of applying the SVRIE in a cooperative vehicle-infrastructure system. It is demonstrated that the SVRIE based on the multi-layered fractal structure exhibits maximum performance in collaborative sensing and interaction between vehicles and road information, such as vehicle motion, road surface condition, and tire life cycle health monitoring. Traditional data analysis methods are often of questionable accuracy. Therefore, a convolutional neural network is used to classify the vehicle and road conditions with accuracy of at least 88.3%. The transfer learning model is constructed to enhance the road surface identification capabilities with 100% accuracy. The accuracies of the vehicle tire motion recognition and tire health monitoring are 97% and 99%, respectively. This work provides new ideas for collaborative perception between vehicles and roadsides.
RESUMO
The emergence of digital twins has ushered in a new era in civil engineering with a focus on achieving sustainable energy supply, real-time sensing, and rapid warning systems. These key development goals mean the arrival of Civil Engineering 4.0.The advent of triboelectric nanogenerators (TENGs) demonstrates the feasibility of energy harvesting and self-powered sensing. This review aims to provide a comprehensive analysis of the fundamental elements comprising civil infrastructure, encompassing various structures such as buildings, pavements, rail tracks, bridges, tunnels, and ports. First, an elaboration is provided on smart engineering structures with digital twins. Following that, the paper examines the impact of using TENG-enabled strategies on smart civil infrastructure through the integration of materials and structures. The various infrastructures provided by TENGs have been analyzed to identify the key research interest. These areas encompass a wide range of civil infrastructure characteristics, including safety, efficiency, energy conservation, and other related themes. The challenges and future perspectives of TENG-enabled smart civil infrastructure are briefly discussed in the final section. In conclusion, it is conceivable that in the near future, there will be a proliferation of smart civil infrastructure accompanied by sustainable and comprehensive smart services.
RESUMO
A key element to ensuring driving safety is to provide a sufficient braking distance. Inspired by the nature triply periodic minimal surface (TPMS), a gradient and multimodal triboelectric nanogenerator (GM-TENG) is proposed with high sensitivity and excellent multimodal monitoring. The gradient TPMS structure exhibits the multi-stage stress-strain properties of typical porous metamaterials. Significantly, the multimodal monitoring capability depends on the implicit function of the defined level constant c, which directly contributes to the multimodal driving safety monitoring. The mechanical and electrical responsive behavior of the GM-TENG is analyzed to identify the applied speed, load, and working mode. In addition, optimized peak open-circuit voltage (Voc) is demonstrated for self-awareness of the braking condition. The braking distance factor (L) is conceived to construct the self-aware equation of the friction coefficient based on the integration of Voc with respect to time. Importantly, R-squared up to 94.29 % can be obtained, which improves self-aware accuracy and real-time capabilities. This natural structure and self-aware device provide an effective strategy to improve driving safety, which contributes to the improvement of road safety and presents self-powered sensing with potential applications in an intelligent transportation system.
