For battery-powered Smart Road components deployed in locations without access to the electrical grid, limited energy availability represents a major challenge to long-term autonomous operation. While photovoltaic panels are the most commonly used energy-harvesting solution, their effectiveness depends strongly on environmental and climatic conditions and may be insufficient in shaded areas or in highly dynamic road environments. Road infrastructure, however, inherently provides additional and largely underutilized energy sources, among which thermoelectric energy generated by temperature gradients within the road structure is particularly promising. This review addresses the problem of identifying viable alternatives or complements to photovoltaic energy harvesting by focusing on thermoelectric transducers as a potential power source for Smart Road applications. The objective of the article is to provide a comprehensive overview of the physical principles underlying thermoelectric transducers, the different architectures of thermoelectric modules, and their practical applicability in road transportation systems. Particular attention is devoted to implementation approaches that do not interfere with traffic flow or compromise road safety, as well as to existing applications of thermoelectric energy harvesting in transportation infrastructure. In addition, the review discusses the potential and limitations of concentrated thermoelectric transducers for increasing power density. By synthesizing current research results, this work evaluates the feasibility, advantages, and challenges of thermoelectric energy harvesting to extend the operational lifetime of autonomous Smart Road components and identifies directions for future research.

In control applications, we often come into contact with systems that response at the change of control signal by oscillations on systems output. These vibrations are unwanted and for their elimination different techniques are used in practice. The most commonly used techniques include PID regulation, frequency filtering and input shaping. This paper is dedicated to a proposal of adaptive input shaper and modelling of its effect on the output of the controlled system.

This article describes the innovative use of an advanced annotation tool designed specifically for creating datasets tailored to MEMS (Micro-Electro-Mechanical Systems) sensor systems for the intelligent transportation domain. By optimizing the data annotation process, this tool significantly enhances the efficiency and accuracy of dataset development, which is critical for the optimal performance and reliability of MEMS-based applications. The tool was tested with a specialized sensor system based on magnetometers for traffic flow monitoring, demonstrating its practical applications and effectiveness in real-world scenarios. The proposed approach offered a clear improvement over manual labelling by reducing the time needed per event and increasing the number of events that could be processed, without compromising the consistency of the assigned labels. The discussion includes a detailed overview of the tool’s features, its integration into existing workflows, as well as the benefits it offers engineers and researchers in the field of sensor technology.