Automated System Research Articles

6651 A Traceable Clinical Free Thyroxine Assay Based on Equilibrium Dialysis ID-LC/MS/MS

Abstract Disclosure: D. Spector: None. L. Zhang: None. A. Ribera: None. A. Doty: None. C. Tse: None. O. Sugahara: None. N. Vazquez: None. U. Danilenko: None. H.W. Vesper: None. Accurate diagnosis and effective treatment of thyroid diseases rely on reliable thyroid function tests. Serum free thyroxine (FT4) immunoassays (IAs) are most commonly used for FT4 measurements in clinical practice. However, FT4 IAs have not been standardized with large variations between different assay platforms. A reference measurement procedure (RMP) for FT4 based on well-defined equilibrium dialysis (ED) of serum has been established for CDC standardization program to support the standardization of clinical assays for patient care. In the current study, we aim to develop a routine clinical FT4 assay based on ED-LC/MS/MS that is traceable to the RMP. ED-LC/MS/MS analysis was performed using a commercially available micro-ED plate. FT4 in dialysate was extracted with a 96-well C18 SPE plate. FT4 in the samples was quantitated by using LC/MS/MS in positive ion mode. The assay was calibrated with the certified primary reference material. By using RMP as a reference, we compared the analysis of 40 single donor sera between the ED-LC/MS/MS assay and FT4 IA on Roche e401platform. ED-LC/MS/MS assay could resolve T4, T3, reverse triiodothyronine (rT3), and other interferences in the samples with C18 chromatography in 4 min. The linear range of the routine FT4 assay was from 0.5-100 pg/mL covering clinically relevant FT4 concentrations including hypo-, hyperthyroid patient samples. The intra and inter-run CV% of the assay in 20 days was 3.72 and 5.25%, respectively. The effect of minor changes to the method conditions was studied. It was determined that serum/buffer ratios from 1:1 to 1:2 in the ED inserts, speeds of shaker for ED, and ED incubation time, did not influence the FT4 measurement. However, elevated ED temperature at 38°C could result in a more than 10% increase in FT4 values. A microplate format for the procedures of ED and SPE is amiable to the application of an automation system which significantly improves the throughput of the procedure. Method comparison showed good agreement between ED-LC/MS/MS assay and RMP in Deming regression analysis with a slope close to 1, while there was significant bias of IA from RMP. ED-LC/MS/MS assay was also applied for pregnancy serum samples with minimal difference on the FT4 measurement from RMP. We have developed a routine clinical FT4 assay that is traceable to RMP. The accuracy, precision, and sensitivity of the assay are suitable for high-throughput FT4 measurements in clinical laboratories and large epidemiologic studies. We demonstrate that the developed method can provide accurate FT4 measurements in patients including pregnant women. Presentation: 6/3/2024

Methodology for identifying reference architectural and spatial solutions of universities using a graphic matrix

The present study considers architectural and spatial development of the existing universities and campuses as relevant issue. Administrative and academic buildings, libraries, laboratories, student dormitories and sports facilities, medical centers, pedestrian zones, green spaces, parks and sports grounds, green area recreations contribute to strengthening social ties between students and teachers, providing a beneficial effect on learning and development. The present paper analyzes examples of successful development of student campuses and identifies the factors of their effectiveness. The study findings may serve as a foundation for creating architectural and spatial solutions aimed at comprehensive development of a comfortable educational environment in modern universities of the Volga region. Furthermore, the results of the conducted analysis will assist in identifying priorities of architectural development tasks in areas at different levels — from urban planning to the installation of engineering and automation systems. Well-maintained green spaces, convenient areas for relaxation and socialization, as well as a variety of sports and cultural facilities, will contribute to the creation of comfortable and stimulating atmosphere, which, in turn, will enhance students' motivation for learning and develop their social skills. Effective planning, consideration of the needs and preferences of students, and their involvement in the decision-making process are indicated as the key factors for successful architectural and spatial solutions of university campuses. In addition, the maintenance and regular updating of green spaces and campus infrastructure are found significant.

Development and potential of robotisation and automation in Kazakhstan's automobile industry: a bibliographical and analytical review

The engineering sector, which includes automobile production, plays an important role in the national economy and industrial progress. It affects the development of production, scientific and technical progress, labor productivity, national security, quality of life. In order to increase productivity, reduce labor costs and improve product quality, industrial automation and robotization processes have been significantly developed in industrialized countries of the world. The global market for industrial robots is expected to grow 14.3% annually and reach $30.8 billion by 2027. However, Kazakhstan faces special difficulties in adopting these technologies due to the lack of development of automation infrastructure. This study aims to develop priorities and directions of advanced scientific and technological solutions for intelligent joint systems of robotics and automation, especially in the automotive industry of Kazakhstan. In this regard, the purpose of the study is to assess the current state of automation in the automotive industry of Kazakhstan, to identify challenges and opportunities associated with the introduction of robotics, and to provide a strategic basis for the advancement of these technologies. The research corresponds to the state programs of Kazakhstan on science, technology and digitization. The scientific and practical importance of this work is that it is aimed at providing solutions for increasing efficiency by adapting advanced robotic technologies to local production systems. Through the development of domestic innovations and the transfer of global technologies, research contributes to the field of industrial automation and helps Kazakhstan's industries to achieve global competitiveness. It is expected that the practical results of the research will contribute to the improvement of production quality, efficiency and productivity in the automotive sector of Kazakhstan, contributing to sustainable development in accordance with world trends. The value of the work lies in providing individual, technologically advanced solutions to meet the nation's urgent need for robotics automation and integration.

Measures to increase the reliability and efficiency of water supply and wastewater disposal systems

The reliability of water supply and wastewater disposal systems has a significant impact on the environment: therefore, it is essential to implement new methods and approaches that reduce the risk of emergency situations. Based on the experience in automation, maintenance and modernization of wastewater disposal and water supply systems, the study suggests introducing additional programmable modules into automated control systems that provide control and regulation directly on the actuator. For this purpose, the study involves OWEN Logic programming environment, which manages pumping stations through programmable relays. The paper presents functional diagrams that have been introduced into standard macros to increase the reliability of pumping units. In addition, the paper demonstrates that the implementation of multi-level management facilitates the improvement of existing water supply and wastewater disposal systems, thereby increasing their functional capabilities and ensuring the efficient operation of technological equipment. The conducted analysis shows that the adoption of this method for managing technological processes will enhance the reliability and durability of the equipment, ensuring uninterrupted operation of pumping stations. With a change in the performance of water supply and wastewater disposal systems, the installed devices will allow for modernization with minimal economic costs, and reprogramming the operation of the low-level automation without affecting the performance of other levels.

OPTIMIZING ENERGY EFFICIENCY: A COMPREHENSIVE ANALYSIS OF BUILDING DESIGN PARAMETERS

This study explores the critical role of financial incentives, energy performance certifications, government policies, and advanced technologies in driving the adoption of energy-efficient building practices. Financial incentives, such as tax credits, rebates, and grants, are shown to significantly reduce the upfront costs associated with energy-efficient technologies, making these innovations more accessible to developers and building owners. The research highlights the effectiveness of energy performance certifications, like LEED and Energy Star, in enhancing the market value and energy performance of buildings by providing a structured framework for evaluating sustainability. Government policies and building codes, including the International Energy Conservation Code (IECC) and ASHRAE standards, are identified as key drivers of energy efficiency, compelling developers to meet stringent performance standards. Additionally, the integration of renewable energy technologies, such as solar panels and geothermal heat pumps, alongside smart building automation systems, plays a vital role in reducing energy consumption and improving operational efficiency. However, regional disparities in the availability of financial incentives and enforcement of policies present challenges to widespread adoption. The study concludes that expanding financial support, strengthening policy enforcement, and increasing public and industry awareness of the long-term financial and environmental benefits of energy-efficient buildings are essential for accelerating the transition to sustainable building practices. By addressing these challenges, the construction industry can make significant strides toward reducing energy consumption, lowering greenhouse gas emissions, and achieving global sustainability goals.

Evaluating the use of grey-box system identification for digital twins in manufacturing automation

ABSTRACT A key element of any digital twin is the digital replica, or model, of its physical counterpart. When applied to industrial automation systems, it is important to consider the trade-off between model fidelity and computational complexity when developing this model. In this paper, we investigate the use of grey-box system identification as a means for producing digital twin models, as the approach reduces computational complexity while maintaining model fidelity. Using a three-link robotic manipulator, we evaluate the ability the digital twin’s system identification module to produce a high-fidelity model while under the influence of input disturbances. We then expand these results by evaluating the ability of the determined model to accurately emulate the robotic manipulator for tasks given to digital twins through the system simulation and monitoring modules. The results of this testing demonstrate that the grey-box system identification module is prone to error and sensitive to input disturbances; however, it produces models that are still able to accurately predict the dynamic response of the robotic manipulator. Furthermore, the tests demonstrate the determined models can be used by the digital twin for simulation and monitoring applications with certain limitations.

A-195 Combination of Lean Mindset, Robotics and Digitalization allowed us to manage a 2.5 times increase in workload in our central reference lab

Abstract Background Our Agilus Diagnostics, Gurugram, India is a Central Reference Laboratory and part of the pan-India network over 400 lab in India. Our Gurugram lab delivers 3,200 tubes in the serum work area in 17 hour operations daily and is ISO 15189 and CAP accredited.In Jan 2023, our lab started to support Govt PHC Program samples and our workload increased to 8000 tubes. This was a clear operational and clinical challenge for us with a 2.5 x jump in workload. To achieve this goal, a lean mindset with process improvement steps were to be applied required across the total testing process. This lean approach was supported by cobas ® Lab Automation systems managed by navify ® Lab Operations informatics automation solution. This poster explains our journey of planning, implementing, and delivering on our goal of predictable operational efficiency when faced with this challenge. Methods We took 4 major steps We redesigned lab processes using lean process improvement operational dashboards. This poster will show the metric we track which help eliminate waste in the system. We optimized our total lab automation solution with smart load balancing across cobas ® Pro integrated analyzers and staff friendly recursive workflow. Clear and strict use of off peak hours for maintenance and QC procedures and standardized predictable schedule of operation. This poster will share our time table with key milestones across the 24 hours We built on the Informatics solution using 5 layered Auto verification rules based decision framework. IQC, Systems Flags check, Sample Quality checks, Biological Reference check, custom Auto verification Range, Critical ranges Delta check Criteria, Clinical Correlation Rule are all part of the auto verification logic framework. Results We were able to deliver the following results.Lean process design led to 53% simplification. Number of process steps reduced to 29 from 62 steps across the total testing process. Total Lab Automation has helped improve 90 percentile production TAT by 46% from 250 mins to 135 mins Our 90 % TAT CV has significantly reduced making our production predictable. Over 17,500 results / day reported are subject to auto verification with 87 % of all results auto-verified and auto-approved. Not a single additional employee was added in the analytical or post analytical areas of the lab operations Conclusions Lean Mindset complemented by Total Laboratory Automation along with Decision Automation on an Informatics solution has enhanced lab performance in terms of TAT, Efficiency, and Staff Motivation. This poster will also demonstrate using incremental effectiveness analysis comparative data of key performance indicators, such predictability in production, and efficiency of workforce, senior staff time saved, before and after technology deployment. The challenge of increase in sample workload is real in India and this approach allows for a sustainable way to ensure patient impact with minimum burden on our workforce.

THE ROLE OF PROPERTY MANAGEMENT IN PROMOTING ENERGY-EFFICIENT SOLUTIONS FOR RENTALS

Real estate management plays a key role in promoting energy efficient solutions when renting out properties. The purpose of the study is to analyze the impact of management companies on the introduction of energy-efficient technologies to increase the competitiveness of facilities and reduce operating costs. The methodology is based on the analysis of data on the application of modern energy-efficient solutions, including lighting, heating and automation systems in buildings in the Czech Republic. The results showed that the use of such technologies helps to reduce utility costs by 20-40% and increases the attractiveness of facilities for tenants. In conclusion, property management aimed at energy efficiency ensures the achievement of sustainable development and economic benefits for owners and tenants. These measures increase the market value of the properties and extend the lease terms, which strengthens the position in the real estate rental market.

Sistem Otomatisasi Pemberian Pakan secara Terjadwal untuk Pembibitan Ikan Lele di Fardu Farm Pekanbaru menggunakan PLC dan IoT

<p><strong>With the increasing global population growth, the demand for fishery products, including catfish, continues to rise. Catfish seed production plays a crucial role in the fisheries production chain, as this stage ensures the availability of high-quality fish seeds. However, fish farmers still face challenges in increasing aquaculture production, especially regarding inefficient manual feeding practices. This research aims to develop an automated catfish feeding system on a schedule at Fardu Farm using a combination of Programmable Logic Controller (PLC) and Internet of Things (IoT) technologies. In this study, the researchers design and implement an automation system that enables scheduled feeding in catfish seedlings. The use of Outseal PLC connected to IoT allows for precise and efficient feeding control. This research conducts testing to verify the system's performance, including measuring the weight of the feed dispensed and testing feeding over specific time periods. The results of the study indicate that this automation system successfully provides timely feeding according to the established schedule. Additionally, manual control using the Blynk platform also yields good responsiveness. Thus, the developed system can enhance efficiency in feeding catfish seedlings at Fardu Farm.</strong></p><p><strong><em>Keywords</em></strong> - <em>Automation, Feeding, Catfish Breeding, PLC, IoT.</em></p>

A model-driven, metrics-based approach to assessing support for quality aspects in MLOps system architectures

In machine learning (ML) and machine learning operations (MLOps), automation serves as a fundamental pillar, streamlining the deployment of ML models and representing an architectural quality aspect. Support for automation is especially relevant when dealing with ML deployments characterised by the continuous delivery of ML models. Taking automation in MLOps systems as an example, we present novel metrics that offer reliable insights into support for this vital quality attribute, validated by ordinal regression analysis. Our method introduces novel, technology-agnostic metrics aligned with typical Architectural Design Decisions (ADDs) for automation in MLOps. Through systematic processes, we demonstrate the feasibility of our approach in evaluating automation-related ADDs and decision options. Our approach can itself be automated within continuous integration/continuous delivery pipelines. It can also be modified and extended to evaluate any relevant architectural quality aspects, thereby assisting in enhancing compliance with non-functional requirements and streamlining development, quality assurance and release cycles.

Automation in the Maritime Transport System – A Framework for Planning Resilient Operations

Abstract Maritime transport systems (MTS) are becoming increasingly automated, integrated and complex. New technology, automation and autonomous systems will contribute to safer, greener, and more efficient transport systems. However, the advancements pose new challenges, and it is a need to understand and manage the potential brittleness and risks. Uncertainties and the unknown should be prepared for when planning the implementation of new technology. Hence, this paper presents a framework to support the planning for resilient operations when introducing autonomy in a MTS. The framework is based on a resilient approach and is the first draft of a framework being developed within the research project Maritime Resilience Management of an Integrated Transport system (MARMAN). The approach is intended to facilitate a process where MTS actors work systematically to identify challenges, clarify potential future measures, and prepares for integrating automation and autonomy in a system. The method emphasizes the needs for cooperation, coordination and integrated planning across different transport modalities, organizational boundaries and stakeholders. But also between humans and technology, focusing on the different levels of planning (i.e. strategic, tactical and operational), where the main objectives will be to increase resilience by coping with potential critical events. In addition, continuous learning and commitment between actors and how to use the new technology are crucial for enhancing resilience.

Rancang Bangun Alat Otomatisasi Pengomposan dari Sampah Organik Berbasis Internet of Things

Organic waste management in Indonesia is currently suboptimal. Approximately 33.7% of waste is not properly managed, and 56% of the total waste consists of organic waste, most of which comes from households. Composting is an effective method for managing organic waste. However, many people are still reluctant to convert organic waste into compost due to the lengthy process, which typically takes 2 to 3 months. This study proposes an IoT-based composting automation system with the goal of accelerating the composting process without the use of additional microorganisms, producing both dry and liquid compost. The system method involves integrating Internet of Things (IoT) technology with sensors: DHT22 for temperature monitoring, soil moisture sensors for measuring soil moisture, MQ-4 sensors for detecting methane gas, pH sensors for measuring soil acidity, and water level sensors for detecting the height of liquid compost. Data collected from these sensors is used to automatically control the organic waste mixing process until all composting specifications are met. The results indicate that this system effectively accelerates the composting process to 24 days for dry compost and 19 days for liquid compost without the need for additional microorganisms, while producing compost of high quality according to the SNI-19-7030-2004 specifications.

IoT System for Floating Raft Hydroponics: Nutrient Monitoring and Automation

Abstract The utilization of hydroponic cultivation in agriculture is increasing due to its high productivity even in limited spaces. However, in practice, monitoring and controlling nutrients in hydroponic systems often require intensive human supervision and intervention. This issue can be addressed through technological advancements, particularly with the adoption of microcontrollers, sensors, and the Internet of Things (IoT). Previous research has developed IoT-based monitoring and control systems for DFT hydroponics using the Blynk platform. This study aims to develop an IoT-based nutrient monitoring and automation system for floating raft hydroponics. Floating raft hydroponics was chosen for its simplicity, lower installation costs, and space optimization. The system developed in this study uses the HC-SR04 ultrasonic sensor with an average error of 1.229%, the DS18B20 water temperature sensor with an error of 0.415%, and the TDS sensor with an error of 2.055%. The sensor and actuator measurement data are presented via the Antares Web. This research has the potential to minimize the necessity for manual intervention in hydroponic systems, thereby enhancing both efficiency and consistency in nutrient delivery.

Real-Time Home Automation System Using BCI Technology.

A Brain-Computer Interface (BCI) processes and converts brain signals to provide commands to output devices to carry out certain tasks. The main purpose of BCIs is to replace or restore the missing or damaged functions of disabled people, including in neuromuscular disorders like Amyotrophic Lateral Sclerosis (ALS), cerebral palsy, stroke, or spinal cord injury. Hence, a BCI does not use neuromuscular output pathways; it bypasses traditional neuromuscular pathways by directly interpreting brain signals to command devices. Scientists have used several techniques like electroencephalography (EEG) and intracortical and electrocorticographic (ECoG) techniques to collect brain signals that are used to control robotic arms, prosthetics, wheelchairs, and several other devices. A non-invasive method of EEG is used for collecting and monitoring the signals of the brain. Implementing EEG-based BCI technology in home automation systems may facilitate a wide range of tasks for people with disabilities. It is important to assist and empower individuals with paralysis to engage with existing home automation systems and gadgets in this particular situation. This paper proposes a home security system to control a door and a light using an EEG-based BCI. The system prototype consists of the EMOTIV Insight™ headset, Raspberry Pi 4, a servo motor to open/close the door, and an LED. The system can be very helpful for disabled people, including arm amputees who cannot close or open doors or use a remote control to turn on or turn off lights. The system includes an application made in Flutter to receive notifications on a smartphone related to the status of the door and the LEDs. The disabled person can control the door as well as the LED using his/her brain signals detected by the EMOTIV Insight™ headset.

Computer simulation of the operability of an electrical circuit in design automation systems

Computer simulation of the operability of an electrical circuit in design automation systems

Technology Focus: Offshore Drilling and Completion (October 2024)

The evolution of computational technology, coupled with the ability to digitize hardware and engage with end users, has enabled machine learning to enhance operational efficiency, safety, and cost-effectiveness in offshore oil and gas development. A key takeaway from recent literature is that, while automation tools have been present for years, their widespread adoption has been hindered by a lack of demonstrable financial benefits. To address this, the focus has shifted from mere deployment to achieving real value through significant cost savings, increased safety, and improved operational efficiencies (paper OTC 35137). Automation efforts in onshore operations prioritize efficiency, while offshore automation must focus on risk reduction and minimizing nonproductive time. For instance, a cutting-edge vessel-motion-prediction tool integrates advanced machine-learning techniques with metocean forecasts to enhance the reliability and safety of offshore drilling operations (paper OTC 35341). Paper OTC 35418 presents the importance of autonomous inflow-control devices in maximizing the production life of wells in deepwater West Africa, taking into account the various challenging reservoir conditions. Another critical insight from the research is the role of human intervention in the deployment of automation systems. Successful deployment requires iterative adjustments and feedback from operators to tailor systems to the specific needs of offshore environments. Collaboration among engineers, operators, and decision-makers is essential in refining these technologies to align them with operational goals and safety standards. Overall, these papers underscore the need for strategic adaptation of automation systems to the specific challenges of offshore environments. The success of predictive machine learning in this context depends on its ability to offer measurable financial benefits, improve safety, and drive operational efficiency. Recommended additional reading at OnePetro: www.onepetro.org. SPE 218473 Performance Optimization of Water-Injection Wells Using Autonomous Outflow Control Device Technology in Offshore Norway by G.F. Kvilaas, AkerBP, et al. SPE 218479 Use of Wired Drill Pipe, Along-String Measurements, and Advanced Logging-While-Drilling Imaging Enhances Wellbore-Condition Understanding and Improves Well Delivery Time, While Telemetry-Optimized Ultradeep Resistivity Measurements Enable Precise Geological Well Placement by Stephen Pink, NOV, et al. SPE 218360 Autonomous Restriction Navigation Through Wellbore in Wireline and Slickline Operations by S. DiPasquale, SLB, et al. OTC 35394 Intelligent Optimization of Ultradeepwater Oil-Transmission Risers Using an Enhanced Graph Neural Network by Hong Zhu, China University of Petroleum-Beijing, et al.

Papermaking-inspired sustainable triboelectric sensors for intelligent detecting system

Digitalization not only drives the intelligent transformation of industries while fostering cultural diversity and innovation. Cutting-edge triboelectric sensing technology inspired by traditional Chinese papermaking fabricates a bamboo paper-based triboelectric nanogenerator (BP-TENG). The addition of high-purity bacterial cellulose improves the structural integrity of the resulting paper. The proposed BP-TENG exhibits excellent electrical performance, generating an output voltage of 88 V and a maximum output power of 0.9 mW at a low-frequency motion of 2 Hz. Moreover, the BP-TENG is combined with machine learning algorithms to build a multi-scene intelligent detecting system, including real-time handwriting-to-speech conversion, personalized jump prediction and smart home automation systems. The machine learning algorithm can achieve a recognition accuracy of up to 99.8 %. This multi-scene intelligent detecting system boasts superior accuracy, environmental sustainability and affordability, ideal for communication security, sports health monitoring and smart building applications.

Role of Artificial Intelligence in the Computer Network: Review

A combination Artificial Intelligence (AI) with networks systems increases from efficiency and performance of the networks. Today routine life needs huge of data and applications shared between people that lead to a traffic load on networks, therefore intelligence networks produces are important abilities of AI in controlling and avoiding troubleshooting. Also AI improves from security and management of databases .Effective high-intelligent networks operate networks in automation systems .It is one of the most hopeful computer networks future. The development of artificial intelligence in the computer network relies on the importance of applications security and cloud computing. In recent years, a big development has occurred in Artificial Intelligence and majority of computer networks that leads to the of AI in the applications of network techniques. This paper reviews a role of combination of Artificial Intelligence with Computer Networks. Discussed two topics combination of AI in the computer network (Cloud Computing and Security). AI in cloud computing increases efficiency and significant networking. In addition, reduced the cost and minimized base of clouding. Artificial Intelligence controls huge amounts of databases in the cloud avoiding problems in heavy traffic networks. AI is a good solution in cyber-attacks, also solving most problems of security. Today more intelligent interaction with computer networks. Automate solutions in more application applying successful training and testing data sets. Artificial intelligence contributes to all fields of life and encourages the development of new technologies and uses. More robots, data automation, and many applications in the Internet of Things are expected.

Enhancing machine optimization through AI-driven data analysis and gathering: leveraging integrated systems and hybrid technology for industrial efficiency

Industrial automation has long been a driving force in enhancing manufacturing efficiency and productivity. However, traditional systems often rely heavily on human intervention, which can introduce errors and inefficiencies. This article explores the revolutionary potential of deep learning in transforming industrial automation by minimizing human involvement and optimizing operational performance. We present a comprehensive methodology for integrating deep learning models into automation systems, focusing on improving throughput and managing downtime and failures more effectively. The study employs advanced deep learning algorithms to analyse real-time data from industrial processes, enabling predictive maintenance and automated decision-making. Key findings reveal that incorporating deep learning significantly enhances system performance by reducing downtime, preventing failures, and increasing overall throughput. Additionally, the research highlights how minimizing human intervention can lead to more reliable and efficient automation systems. The implications of these findings suggest a paradigm shift in industrial automation, where intelligent algorithms drive process optimization and operational reliability. This shift promises to enhance manufacturing capabilities, reduce operational costs, and improve overall system resilience.

Thermal Cameras and Their Safety Implications for Pedestrian Protection: A Mixed Empirical and Simulation-Based Characterization

Multiple sensing technologies exist for advanced driver support and driving automation systems. Car manufacturers combine different solutions to increase the robustness and operational capabilities of driving automation technology perception systems. One sensing method that did not achieve high market penetration with respect to other solutions is the thermal camera. Thermal cameras can operate in low-light and high-contrast conditions as they do not need external sources of illumination. Instead, they are sensitive to the infrared radiation emitted by any surface above 0 K. Thus, thermal cameras can be a suitable candidate to compensate for the weaknesses of visible cameras. To establish the potential benefit of thermal cameras for pedestrian protection, this paper reports on an experiment using state-of-the-art thermal imaging sensors. The authors’ scientific effort includes a characterization of the infrared sensor capability to identify pedestrians when coupled with a detection algorithm and provides a simulation-based investigation of the potential safety benefits of thermal camera adoption. The analysis additionally includes a heuristic characterization of the detection capabilities of a passenger car equipped with visible cameras to provide a baseline comparison. Ultimately, the thermal sensors return noteworthy detection capability that could lead to a potentially significant improvement in road safety on rural roads provided that the sensor has sufficient resolution.