Harnessing the power and possibilities of artificial intelligence (AI) and machine learning (ML) and applying these emerging capabilities to the Department of Homeland Security (DHS) mission has been, and will continue to be, a high priority for the Science and Technology Directorate (S&T). One way S&T is demonstrating this commitment to applying emerging technologies to pressing national threats is by investing in the development of AI/ML technologies. Specifically in this case, the funding is directed at AI/ML that could soon be used to identify dangerous compounds, like those found in explosives and narcotics.

When the DHS Small Business Innovation Research (SBIR) Program released a solicitation back in FY2020, under the topic “Machine Learning Module for Detection Technologies,” the goal was to develop innovative solutions that would ultimately provide DHS operational components with an enhanced ability to identify new threats at aviation checkpoints. In the spring of 2021, following their 6-month Phase I awards to demonstrate concept feasibility, Physical Sciences Inc. (PSI) and Alakai Defense Systems, Inc. (Alakai) were each awarded a $1 million, 24-month SBIR Phase II contract. These awards further lean into the ultimate goal of developing advanced AI/ML-based detection algorithms that can shorten the timeline for deployment of capabilities able to identify threats in the field. The research and development (R&D) being done is important because it addresses a capability gap in the detection of certain types of new threats. S&T believes that AI/ML solutions can help close that gap.

According to Thoi Nguyen, program manager for S&T’s Next Generation Explosives Trace Detection Program, “When the intel, special ops, or law enforcement communities find a new threat, maybe a new explosive compound, the threat is validated and prioritized according to urgency levels. DHS S&T is then tasked to develop an R&D solution to detect and identify the threat. Once the solution is tested, evaluated, and verified that it meets DHS detection requirements, DHS Components go through a lengthy DOTMLPF (Doctrine, Organization, Training, Materiel, Leadership and Education, Personnel and Facilities) process to acquire and deploy the solution. At the end of this process, the chemical ‘signature’ of the threat is uploaded to DHS equipment at airport checkpoints.”

However, adding a new compound to the existing identification library of threat compounds historically has been a slow, meticulous, and labor-intensive process. This can result in a capability gap for updating the database.

The challenge S&T posed with this funding award is to see if an AI/ML solution can significantly expedite the process of updating a detection library, without the intensive human labor.

One of the ways that dangerous compounds are identified at checkpoints is with Raman Spectroscopy. This chemical analytical technique fires a laser at a vaporized and ionized sample that was swabbed from a traveler, or into an object like a closed bottle of liquid. The laser will excite the molecules it encounters in the target, causing them to vibrate. Every type of molecule has its own distinct vibrational frequency. The spectrometer will detect those vibrational frequencies and chart them on a graph. The chemical signature is determined by where specific peaks are found on the graph and the intensity, height, and width of those peaks. Then the system searches the chemical signature library to find a match. If the sample matches an explosive in the database, the alarm is sounded.

So, what’s the problem? “The bottleneck is not in the intel process, the bottleneck is in the R&D process and how to add that new threat intel, the new chemical signature, into the library so we can catch the bad guys,” said Nguyen. “That’s where the AI/ML that our small business partners are developing fits into the equation.”

“We love small businesses because they’re innovative and nimble,” said SBIR Program Director Dusty Lang. “The SBIR program allows us to absorb the risk by funding multiple Phase I proposals to explore feasibility, then move forward to Phase II with the best solutions for DHS needs.”

Traditionally, when a new threat compound is introduced into the library, scientists and contractors are brought in to manually create a new classification or channel for it. At that point, the tedious work to enter all the spectrographic characteristics of the chemical into the library begins. The programing of the chemical traits for the channel must be extremely precise to ensure they get the highest Probability of Detection (PD) and the lowest Probability of False Alarm (PFA) when the library is queried with a sample at a checkpoint.

One of the complicating factors for achieving high PD and low PFA is that the software analyzing the compound must be able to see through the background noise in the sample and identify the compound for what it really is.

“For example, pure TNT from a lab may appear different from TNT in a real-world scenario because there may be additives to the TNT, or there may be other environmental interference. So, even though it might have spectrographic peaks at the right places, they might be somewhat obscured by these other excited molecules and their signatures. If you’re creating a TNT channel, we would have to account for myriad factors. That’s what takes so much time and that’s where accuracy is so important. It has to be calibrated perfectly. What we’re trying to do here with the AI and the ML is that we want to bypass that slow process.”

The first part of that bypass is training the AI to recognize a specific compound. However, the AI can’t teach itself. It still needs to be taught how to do it. The ML-based detection algorithm starts as a blank sheet, and it must be taught which peaks on the graph represent which chemicals. “It’s like teaching a child what sugar tastes like,” said Nguyen. “When you taste this, that is sugar. That’s what we call sweet. And this is sugar with a little bit of lemon. You taste the sour lemon, but it’s still sugar. It’s the same thing with teaching the AI to not get confused by the background noise.”

In Nguyen’s example, the important thing for the child to understand is that the sample is still sugar, and the lemon is just an additive. In the explosive detection world, that lemon might be a fuel added to TNT to make it more powerful. Making sure that the explosives detection algorithm is smart enough to determine that the TNT is mixed with another fuel compound is incredibly important.

That brings us to the second part, which is validation. Once the AI is taught the signature characteristics of the compound, and potential noise distractions have been accounted for, the AI is evaluated for accuracy by running tests designed to trick it. Chemicals are added to the original compound in attempts to shield or mask the spectrographic signature behind other noise.

Nguyen emphasizes the importance of this part, adding that, “We don’t just trust AI completely. We say, ‘trust, but verify,’ to see whether or not the alarm that was just triggered complies with our understanding of how the vibration of the molecules we are testing should present themselves.”

For a limited set of explosives, S&T demonstrated that the AI/ML solution identified explosives with very high PD, yet low PFA—a major success by itself. Even more remarkable is the way that this AI/ML solution has closed the critical time capability gap.

“What traditionally can take as many as one to two years, the AI/ML that our partners developed can now learn, classify, and upload new threats to the library in a matter of days or weeks,” said Nguyen. “That has significant real-world impact. And I want to make sure that we give credit to SBIR, because without their collaboration, funding and support, this project would never have happened.”

SBIR’s Lang added, “These two companies, PSI and Alakai, demonstrate the impact small business can have and why we are always working to strengthen the SBIR reach and support. It is very rewarding to be able to work with program managers like Thoi to facilitate the connections of ideas and needs.”

This round of Phase II funding from the SBIR Program resulted in confirmation that AI/ML has a place in the future of explosive detection. The shortened deployment cycle to chemical libraries in the field, coupled with maintaining the high PD and low PFA, is something that human hands can’t match. That’s the power of trustworthy AI/ML and that’s what S&T is looking to leverage to further secure the nation.

In terms of looking back on the work that has been developed under the program, Nguyen finished up stating, “It was a success beyond our imagination.”

In the future, AI/ML modules will be tested and evaluated at the U.S. Army’s Chemical Biological Center. The goal there will be to determine compatibility between three types of Raman Spectrometers and their interoperative capabilities.

Read the article here: Leveraging Artificial Intelligence Is Smart for Explosive Detection

Today’s customers demand more personalization, whilst manufacturers demand higher productivity and faster responses to market changes. Product quality and worker safety are also becoming a much higher priority. Competitors are popping up all over the globe, and new technologies such as 3D printing are redefining who is a ‘producer.’ Manufacturers still experience a significant amount of annual downtime, 30% of it unexpected. In addition, as skilled labour pools age, there is a chance that two million positions in the US alone will go unfilled.

Manufacturing is changing at a very rapid pace. There is a new type of connected, data-driven, and architecturally open factory emerging in response to these demands, led by the Industrial Internet of Things (IIoT). Along with increased machine automation, other characteristics of this new smart factory include hyper-agility, autonomous production, and data utilization as a tool for business.

Research conducted by Accenture for the World Economic Forum showed that 73% of the C-level executives interviewed were convinced that the IIoT would fundamentally change their industry. But just 20% had a strategy for harnessing it. Companies that want to succeed in the future must master the radical digital transition headed our way by opening themselves to a journey that will change their organization models beyond recognition – the alternative being a catastrophic loss of market share and profitability.

Smart, connected and data-driven

Smart manufacturing is now seen as a natural progression of the “digital convergence” already underway between information technology (IT) and operational technology (OT). There are four essential characteristics that set it apart.

Firstly, the smart factory uses data and IIoT connectivity to easily control all aspects of operations in near real-time, with near full automation across all locations. IoT and digital investment is the foundation for proactive, self-aware factory operations, maintenance and innovation.

Sensor-equipped machines, inter-operable systems and reliable real-time computing are connecting machines across the smart factory. Product, raw materials, equipment, and control systems all have the potential to collect and share data. This data can be analysed in context and in real time to equip workers with actionable information.

Throughout the factory, end-to-end security at both hardware and software levels helps reduce vulnerabilities as more machines are connected. With more than 40 years of experience in both OT and IT solutions, Advantech is uniquely qualified to address the issues of OT-IT convergence which are fundamental to migration from yesterday’s ‘islands of automation’ to tomorrow’s smart factories.

A self-managing “Systems of Systems”

Secondly, the smart factory is based on multiple interconnected systems, each with a high level of flexibility, efficiency, and autonomy. Future factories will eventually become one large system comprising hundreds of smaller systems independently working toward the same goal. From production and maintenance to supply chain and security, each system and subsystem uses AI, machine vision, deep learning, and edge analytics to control everything on the factory floor.

This environment of machine-to-machine communication improves operational efficiencies and reduces unplanned downtimes. Production becomes so responsive to custom requests and material variations that the factory essentially operates at “economies of one” to compete with today’s economies of scale.

Self-monitoring equipment using sensors such as Advantech’s LoRaWAN smart vibration sensor can detect when quality could suffer due to degradation and then schedule its own service. Materials follow the most efficient path, and workloads are consolidated at all architecture layers to provide the flexibility to respond to fast-changing demands. Orchestration of applications and services across hardware enables data aggregation and control to provide new levels of performance.

Human-machine collaboration

A third aspect of the smart factory is its emphasis on machine-to-human collaborations, allowing employees to work more safely and empowering them to make faster, more educated, innovative responses to business needs. As smart factories reduce the number of humans on the floor, workers are helped by collaborative ‘co-bots’ on complex tasks, while repetitive, injurious work is handled by robots.

Workers use augmented reality and data visualisation to overlay information about production, maintenance and product status. A digital culture encourages the use of data for daily work, freeing employees to respond with greater creativity to resolve issues and support business success. A younger workforce is attracted through updated technology, safer work environments, and roles better suited to their generation.

Autonomous and self-adapting

Through autonomy and adaptability, the smart factory enables manufacturers to expand IIoT’s application and value to support changing business strategies.

The factory is becoming smarter and more autonomous over time, using data to optimise resource allocation and transform businesses. As more machines and systems are connected, manufacturing matures into an intelligent factory model in which OT and IT converge and strategically engage in business decisions.

AI and deep learning produce increasingly detailed, accurate and meaningful digital models of equipment and processes, enabling data-driven decision-making; and devices will grow more intelligent over time and respond to events more efficiently. Production controls become self-running, and new business approaches emerge.

Aided by the insights from data, the main manufacturing drivers have expanded from efficiency and product quality to also include production flexibility. Amid this continually evolving environment, the factory systems become increasingly intelligent and autonomous with systems beyond themselves.

Key trends shaping the future of manufacturing

In a smart factory environment, “data collection” refers to the process of gathering information from the manufacturing processes, equipment, and systems involved in production. Data collection in a smart factory is typically facilitated by various sensors, IoT devices, and automation systems that continuously monitor and record relevant information in real-time. This data is then aggregated, processed, and analysed using advanced analytics, machine learning algorithms, and other tools to gain insights, optimize processes, improve efficiency, and make data-driven decisions.

EdgeLink, a versatile IoT gateway software, is engineered to connect with over 200 edge devices and diverse platforms. It adeptly supports multiple protocols, unifying data sources, optimizing data processing, and publishing data to mainstream platforms or other automation systems through cellular, 4G, 5G, Wi-Fi networks, and VPN connections.

“Edge AI” and “Edge Computing” refer to the processing and analysis of data at or near the ‘edge’ of the source of data generation, rather than relying on centralized cloud computing resources. In the context of an intelligent or smart factory, edge computing and AI technologies are employed to perform data processing, analysis, and decision-making tasks directly within the factory environment. They play a crucial role in enabling the responsiveness required in modern manufacturing environments, contributing to the concept of Industry 4.0 and the evolution of smart factories.

MES (Manufacturing Execution System) and ERP (Enterprise Resource Planning) are software systems used to manage manufacturing operations and overall business processes, while OEE (Overall Equipment Effectiveness) is a performance metric used to assess the efficiency of equipment or processes. These concepts are all essential components of modern manufacturing management practices.

In the context of a smart factory, “Analytics AI” and “Machine Learning” (ML) are critical components that enable data-driven decision-making, optimization, and automation. They play crucial roles in leveraging the vast amount of data generated in a smart factory environment to drive insights, improve decision-making, optimize processes, and ultimately enhance productivity and profitability. They enable the transformation of data into actionable intelligence, empowering manufacturers to adapt quickly to changing conditions and stay competitive in today’s fast-paced business environment.

Industrial connectivity refers to the network infrastructure and technologies used to connect various devices, systems, and components within an industrial environment. It enables seamless communication and data exchange between machines and other equipment, facilitating industrial processes. Industrial connectivity is a foundational element of Industry 4.0 and the concept of the IIoT.

Overall, industrial connectivity is essential for enabling automation, data-driven decision-making, and optimization in industrial environments. It forms the foundation for Industry 4.0 initiatives aimed at improving efficiency, productivity, and competitiveness in industrial sectors.

Advantech’s Role in Driving Smart Manufacturing

Software tools play a crucial role in the optimization of smart factories. These tools encompass a wide range of applications and platforms designed to support manufacturing planning, execution, monitoring, analysis, and optimization. Advantech’s WebAccess and other IoT software products provide a complete solution for device and data management to enable edge intelligence.

Advantech has implemented Industry 4.0 in its manufacturing centres, including a ‘situation room’ as the factory’s central brain where data is collected, analysed and visualised for real-time management. The equipment connectivity solution – consisting of an edge data gateway and distributed digital I/O – facilitates machine connections without replacing existing equipment while also collecting data. The industrial computer with Advantech software enables data transfer between production and management systems.

The process visualisation solution enables production monitoring, data integration with MES, and visualisation on the situation room dashboards. This allows production optimization and data-driven decision making. The Advantech WebAccess app gives push notifications of unexpected downtime, allowing immediate action to be taken.

The fundamentals of smart manufacturing aren’t slowing down. When combined together, they usher a new Industrial Internet of Things (IIoT) era where machine automation, hyper-agility, autonomous production, and data utilization deliver transform business processes.

Read the article here: The Fundamentals of Smart Manufacturing

The agriculture sector is changing, and the revolution is as a result of technological development. The concept of smart farming involving the use of advanced technologies such as AI, IoT, and big data analytics is defining the future of agricultural practices and is currently generating a tidal wave of opportunities for agritech startups. These startups are well placed to meet the growing demand for food around the world while at the same time offering efficient, sustainable farming solutions.

Here are six emerging trends and the opportunities that they present for agritech startups:

1. Precision Agriculture: Data-Driven Farming for Maximum Efficiency

Precision agriculture is an important pillar of smart farming, which includes the use of data in enhancing farming methods. Precision agriculture helps to make real-time assessments of factors like soil, water, and crop health. The use of satellite imaging, GPS, and sensory technology enables the farmer to apply water, fertilizers, and pesticides appropriately, increasing production and minimizing wastage.

For agritech startups, this presents the opportunity to create systems that collect data and present this to farmers so they can make better decisions. Such sensors, drones, or software solutions can be created by startups, and this real-time data can help farmers to work most efficiently, with the lowest operational expenses and with reduced emission of pollutants to the environment. The possibility of customization of these solutions depends on the type of crops and geographic location, which only adds value to these solutions.

2. AI-Driven Crop Management: Enhancing Productivity and Decision-Making

Artificial intelligence (AI) is rapidly transforming crop management, where it performs tasks which were previously handled manually and also carries out risk assessment. Automated crop management employs big data analytics by relying on machine learning (ML) algorithms to predict climate changes, disease risks, and the optimum time for harvesting. This makes it possible for the farmers to prevent anything that might hinder the growth of the crop and also improve the yield of the crop.

Agritech startups can leverage artificial intelligence to create apps that will assist farmers in managing the growth of crops, pinpointing the onset of diseases, and assisting in informed decision-making. Their products might also ensure optimal use of materials and manpower, reduce costs on labor, and boost the management of the supply chain.

If the startups manage to tackle such essential problems as crop losses and resource wastage, they will have a great potential for enhancing food safety and environmentally friendly technologies.

3. IoT Integration: Connected Devices for Smarter Farms

The arrival of the Internet of Things (IoT) in the agtech space is making everything more connected by using devices such as smart devices, automatic irrigation machines, and drones. These IoT devices help farmers monitor and manage their farms even from far away by providing real-time information about the soil, weather, and the performance of the equipment on the farm.

When it comes to agritech startups, IoT offers themselves a grand avenue to grow solutions that address the multiplicities of the farming value chain. Startups can develop software that optimizes irrigation based on forecasts or work on drones, which will help to control the state of huge fields and identify if crops are stressed.

4. Robotics and Automation: Redefining Labor and Productivity

Robotics and automation convergence in agriculture is mitigating the growing shortage of labor force and increasing efficiency. In terms of reliability, autonomous machines do relieve people of responsibilities in activities such as planting, weeding, and harvesting among other responsibilities.

From an innovation perspective, technologists in startups can identify the low-hanging fruit of using robots in the agricultural industry regarding repetitive duties that require human intervention in undertaking the task.

Automation technology offers scalability, enabling farmers to manage larger areas of land without compromising productivity. With advancements in AI and machine learning, these robots can adapt to different environments and crops, making them a versatile solution for diverse farming needs.

5. Blockchain for Traceability and Transparency

A trend that recent studies have identified is the application of that technology in the food supply chain which primarily guarantees the traceability of products. With the use of blockchain technology, all the information relating to farming, such as what seeds were planted, how the farm was cared for, and how the crops were transported, is stored in a secure, tamper-proof mode. This kind of transparency is not only appealing to the consumer who wishes to trace the sources of the delicacy but also helps the growers in defending their earnings and minimizing swindles.

Such agritech startups can easily leverage blockchain to create ecosystems that will guarantee proper quality assurance at every level of food production to combat and prevent any violations of the quality standards or environmental laws. It will increase the level of transparency, and assist in trusting farmers in terms of their ability to meet consumer needs and be competitive in the market.

6. Sustainability and Climate Resilience: Future-Ready Farming

However, because climate change is an ongoing process that impacts agriculture in a negative way, climate-friendly and sustainable farming practices are required more than ever before. There are increased uses of practices like regenerative agriculture farming and efficient water use, while innovations such as artificial intelligence and the internet of things are making farming more efficient.

The major opportunity that presents itself to agritech startups is to design solutions that ensure farmers make minimal negative impacts on the environment and are sustainable. This ranges from developing tools that help in the conservation of water, reducing the use of chemicals, and generally improving the ground. Climate-smart agriculture is one of the key areas that start-up companies will be able to address in a short time, hence becoming profitable ventures, especially due to the increasing demand for environmentally friendly farming practices.

Seizing the Opportunities: The Role of Agritech Startups

With the advent of artificial intelligence, IoT, robotics, and data analytics in the agricultural industry, there is a conducive environment for agritech startups to thrive. As those startups come up with solutions that address some of these major challenges in farming, such as food production, sustainability, and resources, they will also shape the future of farming while occupying a large niche in the global agritech market.

It will be startups that are innovative, scalable, and sustainable that will drive the change in traditional farming practices. Working hand in hand with farmers, scientists, and governments, agritech startups have the potential to widely implement smart farming tools and establish a better, more productive farming industry.

To summarize the above thoughts, the future of agriculture will belong to people creating better technological solutions for farming and optimizing farming for the benefit of both people and the environment. This is the moment for agritech startups to come up with solutions and capture the market. Agricultural startups have a unique opportunity to look for a solution for current problems and shape the future of agriculture with a global trend for food security and new sustainable agricultural practices.

Read the article here: Smart Farming: 6 Emerging Trends for Agritech Startups to Watch

In an era where digital transformation is reshaping industries, the convergence of smart cities and e-health is redefining urban living. Smart cities are no longer just about optimizing transportation, utilities and governance. They are evolving into intelligent ecosystems where digital healthcare is becoming a core element of urban planning. As cities grow and technology advances, integrating healthcare into urban infrastructure is reshaping how medical services are delivered, accessed and managed.

The convergence of smart cities and e-health is about reimagining healthcare delivery in a way that is more accessible, efficient and responsive. Cities are moving beyond traditional models of care by leveraging AI, IoT and real-time data analytics to improve patient outcomes and healthcare accessibility.

Specifically, the increasing adoption of agentic AI systems provides sophisticated and real-time monitoring and decision making for enhanced services. Agentic AIs are capable of taking autonomous actions to achieve specific goals or objectives, typically based on predefined rules, learned patterns or programmed behaviors. The shift toward technology-driven healthcare solutions is setting new standards for urban well-being and quality of life.

Smart Cities As Enablers Of Digital Healthcare

The United Nations Department of Economic and Social Affairs estimates that 68% of the global population will live in urban areas by 2050. Many of these cities will be built using smart infrastructure principles that rely on real-time data, AI and IoT-driven systems to improve efficiency and sustainability.

By integrating e-health solutions into urban infrastructure, cities can make healthcare services more connected, data-driven and patient-centric. Digital transformation is ensuring that healthcare is no longer confined to hospitals and clinics.

A study by Fardin Quazi (2024) on “eHealth Services in Comprehensive Smart Environments” highlights the role of urban infrastructure in supporting digital healthcare and the seamless interaction between them. The research emphasizes seamless interactions between smart environments in enhancing patient care and streamlining healthcare operations through advanced digital technologies.

How Smart City Infrastructure Supports E-Health

The global e-health market is currently valued at $274.35 billion and is expected to reach $576.73 billion by 2030. The U.S. remains at the forefront of this growth, driven by the increasing demand for smart, technology-enabled healthcare solutions. Urban infrastructure plays a crucial role in supporting this transformation in several ways.

Real-Time Health Monitoring And IoT Connectivity

In a smart city, healthcare is no longer limited to in-person visits. Wearable devices, home-based health monitoring systems and IoT-powered medical sensors provide real-time data on patients’ vital signs, such as heart rate, oxygen levels and glucose levels. Agentic AI complements this by analyzing the data in real time and triggering actions without waiting for manual input.

This data is transmitted securely to healthcare providers, enabling remote monitoring and timely medical intervention. By integrating real-time health tracking with urban data systems, cities can create more proactive healthcare models that focus on preventive care rather than reactive treatment. This shift reduces hospital overcrowding and enhances medical efficiency.

Emergency Response Optimization

Cities with AI-powered emergency response systems and real-time traffic monitoring are improving the efficiency of medical services. By leveraging real-time GPS data, ambulances can navigate faster routes, bypass congested areas and reduce response times in critical situations.

Additionally, AI-assisted surveillance systems can detect accidents, medical emergencies or sudden health incidents in public spaces, triggering automatic alerts to emergency responders. These innovations are particularly valuable in large urban centers where delays in emergency response can have life-threatening consequences.

The increasing use of agentic AI in smart traffic management is aiding in better monitoring and real-time response. For instance, if a pedestrian meets with an accident or faces a health emergency in a public space, agentic AI surveillance can identify the incident, alert emergency responders and analyze environmental factors such as air quality or crowd density to determine potential causes.

Telemedicine And Virtual Healthcare Services

Telemedicine is becoming a mainstream mode of healthcare delivery rather than an alternative to traditional consultations. With 5G connectivity, cloud-based healthcare platforms and AI-powered diagnostics, patients can now consult doctors remotely without visiting a hospital. This transformation is particularly beneficial for elderly residents, individuals with mobility challenges and underserved communities.

Smart city infrastructure is also facilitating the deployment of virtual health kiosks, allowing residents to access medical consultations and conduct basic health screenings conveniently. Beyond facilitating virtual consultations, agentic AI systems can autonomously schedule follow-ups, analyze symptoms during video calls and recommend diagnostic tests based on patient data.

Recognizing the growing impact of digital healthcare, the U.S. Department of Health and Human Services (HHS), through HRSA, allocated $55 million to 29 health centers to expand access to telehealth, remote patient monitoring and AI-driven health technologies. These investments are reinforcing the role of smart infrastructure in supporting accessible healthcare.

Challenges In Smart Healthcare Integration

Despite its potential, integrating e-health into smart cities presents significant challenges. Data privacy and security remain primary concerns, as healthcare data must be securely transmitted and protected from cyber threats. Additionally, ensuring interoperability between different healthcare platforms, IoT networks and urban systems is an ongoing challenge that requires industry-wide standardization.

Another critical issue is bridging the digital divide. While smart healthcare solutions are advancing, not all urban residents have equal access to digital health services. Investments in affordable digital literacy programs, healthcare technology accessibility and public-private collaborations will be necessary to ensure inclusivity.

Collaboration between healthcare providers, technology developers and policymakers is essential to overcoming challenges in digital healthcare integration.

The Future Of Smart Cities And Healthcare

As cities continue to evolve into data-driven, intelligent environments, healthcare will become an even more central component of urban planning. Future innovations in AI-driven personalized medicine, blockchain-secured health records and 5G-enabled smart hospitals will further revolutionize how cities manage healthcare services.

Public-private partnerships will play a key role in scaling digital healthcare initiatives, bringing together tech companies, government agencies and healthcare providers to create sustainable solutions.

Healthcare is no longer just a standalone service—it is an intrinsic part of modern urban infrastructure. The cities of the future will not only be smarter and more efficient but also healthier and more resilient.

Read the article here: Smart Cities And E-Health: The Convergence Of Urban Infrastructure And Digital Healthcare

Technology has seeped into every part of our daily lives, including our homes. Over the last decade, the number of homes with at least one smart device has steadily increased. Current estimates show that around half of the homes in the United States now feature at least one smart device. While the majority of these devices are deployed by homeowners, renters also have a strong desire for smart home technology. Surveys indicate that approximately 82 percent of renters want smart home devices.

Until recently, rental owners and property managers have lagged behind homeowners in adopting these technologies. Many did not consider smart technology worth the expense, especially because many renters will often install certain consumer goods like smart speakers, locks, and thermostats themself. However, things are changing as rental managers begin to recognize the profitability and benefits that smart devices offer.

Enhancing the Resident Experience

One significant benefit occurs even before the renter moves in. Smart locks and access control features enable self-guided tours, reducing the workload for leasing teams and allowing potential renters to tour apartments during off-hours when leasing staff might not be available. This technology also creates a positive first impression for renters who expect a smooth, hassle-free experience. The same smart devices that facilitate self-guided tours can be used for keyless and mobile entry, an increasingly popular feature among renters. These enhancements not only improve operational efficiency but also allow property owners to justify premium rental prices, thereby increasing revenue. By offering cutting-edge technology, apartment communities can attract higher-paying tenants willing to pay more for the convenience and security that smart devices provide.

Another highly desired smart home feature being deployed in apartments is smart thermostats. These devices help renters save on their energy bills without sacrificing comfort. Landlords have hesitated to install these often costly devices in properties where renters are responsible for their utility bills. But the demand from renters for both the sustainable and cost-saving benefits that smart thermostats provide now outweighs the initial installation costs. More cities are starting to enact laws regulating and even fine buildings that use excessive power. Smart thermostats are one of the easiest ways to streamline reporting and decrease energy consumption, leading to lower utility expenses for property owners and improved profit margins. By promoting energy efficiency, property managers can market their buildings as environmentally friendly, attracting eco-conscious renters and potentially qualifying for green building incentives or rebates.

Connectivity and Added Value

One of the less talked-about smart apartment technologies is community Wi-Fi. By bundling Wi-Fi services, buildings can help reduce renters’ expenses and make their units more valuable. Community Wi-Fi also allows renters to easily connect to reliable internet throughout the entire property, an increasingly important feature in the work-from-home era. For property owners, offering bundled Wi-Fi can be a competitive advantage that attracts higher-quality tenants and reduces vacancy rates, directly impacting profitability. Providing community Wi-Fi can also open up additional revenue streams through partnerships with internet service providers or by offering tiered internet packages, allowing property managers to generate extra income while enhancing the living experience for tenants.

Pleasing renters is not the only reason for a rental property to deploy smart building features. Maintenance becomes much more efficient with smart apartments and smart building technologies. Residents can easily submit and track work orders through online portals and mobile apps. Since poor maintenance experiences are one of the most common renter complaints, improving this process can significantly increase tenant retention and limit negative reviews. Quick and effective maintenance responses lead to happier tenants who are more likely to renew their leases and recommend the property to others. Workers and contractors can automatically access necessary areas and easily find important information like service records and owner manuals. Efficient maintenance operations reduce costs and downtime, enhancing the overall profitability of the property by minimizing vacancy periods and avoiding costly emergency repairs.

Management also benefits from smart devices. Data about every facet of a building can be centralized, making it easy to track performance and compare multiple properties within large portfolios. Increased tenant satisfaction, reduced vacancy rates, and decreased operational costs all contribute to a more profitable building, far outweighing the expenses of installing and maintaining smart apartment features. The ability to analyze data effectively allows property managers to make informed decisions that optimize revenue streams and minimize expenses. For example, energy usage data can highlight areas where costs can be cut, while occupancy data can help in adjusting rental prices dynamically based on demand. Predictive maintenance powered by smart devices can prevent major issues before they arise, saving significant amounts of money in the long run.

Smart apartments also offer enhanced security features, which are highly valued by tenants. Integrated security systems with cameras, motion detectors, and real-time alerts provide peace of mind to residents. For property owners, enhanced security can reduce the risk of theft and vandalism, lowering insurance premiums and minimizing liability. Secure buildings are more attractive to prospective renters, allowing property managers to maintain higher occupancy rates and command higher rents. Advanced security systems can also deter crime, creating a safer community environment that fosters long-term tenant loyalty and stability.

Streamlined Maintenance and Data-Driven Management

Another area where smart technology boosts profitability is in marketing and leasing. Smart apartments can showcase their technological advancements as unique selling points, differentiating them from competitors in a crowded rental market. High-quality photos and virtual tours featuring smart devices can attract more interest online, leading to faster leasing times and reduced marketing costs. The ability to offer virtual tours and online leasing processes appeals to tech-savvy renters who prefer to handle transactions digitally, streamlining the leasing process and reducing the need for in-person interactions.

Smart lighting systems are another valuable addition to smart apartments. These systems can be controlled remotely or set to adjust automatically based on occupancy and time of day, enhancing both convenience and energy efficiency. For renters, smart lighting provides a customizable living environment that can be tailored to their preferences. For property owners, smart lighting reduces electricity costs and extends the lifespan of bulbs, leading to lower maintenance expenses. The modern, high-tech appeal of smart lighting also makes the apartments more attractive to potential tenants, supporting higher rental rates and occupancy levels.

As more properties adopt smart technology, it becomes increasingly challenging for competitors to stay relevant without similar investments. Early adopters of smart apartment technologies can position themselves as leaders in the market, attracting tenants who prioritize modern amenities and technological convenience. This competitive edge not only drives higher occupancy rates but also allows property managers to implement premium pricing strategies that enhance overall profitability. In the long run, the integration of smart technologies is not just a trend but a standard that defines the future of profitable apartment communities.

Smart apartments are finally proving to be profitable for community owners and managers. From enhancing the tenant experience with convenient features like smart locks, thermostats, and community Wi-Fi to streamlining maintenance and operational efficiencies, the benefits are clear. Smart technologies not only attract and retain high-quality tenants but also reduce costs and open up new revenue streams. As the demand for smart home devices continues to grow among renters, property owners and managers who invest in these technologies will see significant returns on their investments. The shift towards smart apartments represents a smart business move, ensuring long-term profitability and sustainability in the competitive rental market.

Read the article here: Smart Apartments Are Finally Profitable

America leads the world in innovation. The United States has the highest-valued startups, the most prestigious universities, the most prolific researchers, the best AI companies, and the most venture capital funding. American cities, however, are the exception. In the latest ranking of smart cities by the World Competitiveness Centre, no American city made the top 30 — and only New York, Boston, and Washington, D.C. made the top 50.

There are a number of reasons why government — and local government in particular — operates differently than businesses. Three years ago, we started to work with the United States Conference of Mayors to understand which of these reasons matter most. In particular, we tried to find the main obstacles standing in the way of AI adoption at the local level.

In interviews with more than 150 local leaders across dozens of U.S. cities, respondents consistently reported three problems:

• Sclerotic and siloed bureaucracies (83%), or the way in which local departments tend to work in isolation from one another, with few incentives to reform themselves.
• Burdensome regulations (44%), or the web of rules that prevent cities from acting boldly, including purchasing requirements, building codes, and other regulations that make it practically impossible for cities to work with startups.
• Risk aversion (31%), or the sheer fear that often animates local leaders when it comes to new technologies — significantly more than in business leadership. Mayors know that technology can help their cities, but they remain afraid of experimenting with tools about which they know relatively little.

Combined, these three roadblocks have made local governments remarkably stagnant in an otherwise dynamic and entrepreneurial country.

The explosion of AI capabilities, however, could be a turning point. In this article, we look at the ways in which cities can use AI and provide a framework for local leaders looking to transform their cities.

How Cities Can Use AI

Broadly speaking, cities are pursuing AI projects in three broad categories: automating tasks (mentioned by 76% of respondents), making better decisions with data (41%), and engaging the community (23%). In what follows, we explore some of the main use cases in each category and explain how these capabilities have the potential to make cities both more efficient and more responsive to their residents’ needs.

Automating tasks

Today in Los Angeles, a small construction company needs an average of 14 procedures, 105 days, and $85,841 to obtain construction permits. In San Francisco, it needs an average of 19 procedures, 184 days, and $108,063 to obtain the same permits. Entrepreneurs looking to open a restaurant, salon, or shop in San Francisco must navigate over 25 different requirements spanning building codes, fire codes, zoning ordinances, ADA rules, and more. Just applying for these permits costs on average thousands of dollars and takes more than six months before they can break ground.

Tomorrow, AI could automate most of these processes away — and hundreds more. Every permit application could be filled or reviewed in a matter of minutes. Every Excel spreadsheet could be analyzed and updated in real time. Data could be shared from one agency to another without human supervision. In the cities we’ve studied, bureaucratic automation was by far the most common use of AI. And for good reasons: Of all capabilities, it remains the least expensive and easiest to implement.

For example, in Honolulu, Hawaii, the Department of Planning and Permitting has cut the time to complete residential permits by 70% with AI. The head of the department is now expanding the initiative into a new platform “not unlike TurboTax” that will “ensure permit applications meet all necessary requirements before being transferred for [automatic] completion.” If successful, the project could automate hundreds of tasks, save thousands of hours, and save millions of dollars in the long run. The initial project cost $200,000 — about one-tenth of the benefits expected in the first five years of implementation.

Some might fear that cities will replace their employees with algorithms, but in practice we’ve seen that AI makes day-to-day operations more efficient without removing the need for human input. Instead of filling out forms or spreadsheets, civil servants can dedicate their time to bigger-picture and larger-impact projects for their cities. As Reno’s mayor Hillary Shieve told us, “We’re not using AI to replace our people but to make them focus on what matters.”

Making better decisions with data

Cities already collect enormous amounts of data on the urban environment; with sensors and cameras, they monitor air quality, noise, utility consumption, traffic density, parking violations, construction activities, and environmental conditions. The problem is, they seldom take advantage of this wealth of information. The vast majority of our interview respondents (over 87%) admitted that their departments are sitting on data that they lack the skills or resources to process, let alone leverage in real time.

That’s what AI brings to the table. It can handle data on a much larger scale, at a much lower cost, than human beings. It can integrate diverse streams of data — cameras, sensors, surveys, language, etc. — and analyze, predict, simulate, and forecast trends in real time. It can also get better with time, detecting hidden patterns and adapting to changing circumstances. In short, it can help cities make smarter decisions about and provide enhanced services in every domain of urban life. These include:

Transportation

• Make traffic signals more responsive
• Implement dynamic pricing on roads and parking
• Re-route transit services in real time
• Communicate predicted bottlenecks to residents

Infrastructure

• Predict maintenance or upgrades
• Optimize resource allocation to extend asset lifetimes
• Forecast costs, timelines, and impacts of capital projects
• Optimize rollout plans to minimize disruptions

Crisis response

• Forecast potential crisis scenarios to take preemptive measures
• Model impact, timing, and scale to guide the allocation of supplies
• Automate communication strategies with at-risk populations

Social services

• Identify intervention points
• Target the provision of preemptive measures
• Simulate resource constraints against evolving demand
• Adjust eligibility requirements in real time

Urban planning

• Model decade-long scenarios
• Simulate the impact of potential policies and investments
• Evaluate alternative paths of development

Innovative cities are already launching pilot programs in each of these areas. In transportation, Seattle has partnered with Google Research’s Green Light initiative, which uses AI to manage signal timing to improve the flow of traffic. In a few months, the city achieved $10,000 of delay savings in eight locations. In infrastructure, Deloitte has found that AI-driven maintenance reduces infrastructure repair costs by 25% in more than a dozen cities. In crisis response, California has successfully used AI to monitor over 1,000 cameras to detect wildfires. Within the first four months alone, AI detected 77 wildfires and proved so successful that TIME magazine recognized the program as one of its “best inventions of the year” in 2023. In social services, the city of Allentown, Pennsylvania has saved an estimated $1 million in taxpayer dollars by streamlining incident investigations across 21 departments with AI. And in urban planning, researchers at Tsinghua University in China have just developed the first AI planning system capable of outperforming human architects.

In all of these examples, the city in question was already collecting the right data — and already had systems in place to do so — but could not process it. AI just gave them the opportunity to harness masses of data that they would otherwise leave untouched. And this kind of intervention has the potential to reinvent urban decision-making for the better.

Engaging the community

The final capability that AI brings to cities has to do with the local government’s relationship to residents. In recent years, the “smart city” paradigm has — rightly — been criticized for its technocratic bent. Advocates of smart cities often push for top-down innovation, neglecting the populations that new technologies are here to serve. Fortunately, AI can help make cities more, not less, democratic by simplifying access to both information and services. For example:

• Answering questions: In Raleigh, North Carolina, AI chatbots are able to manage 90% of calls to administrative agencies, which frees up time for operators to answer more complicated or time-sensitive inquiries.
• Filling out documents: In more than a dozen American cities, AI chatbots are helping residents fill out hundreds of documents. The bot asks simple questions and fills the form.
• Translation: AI translation companies like Unbabel can translate emails and web pages into more than 20 languages. With a team of human editors to verify for accuracy, they complete this service at $0.02 per chat, a much cheaper rate than traditional translation services.
• Interactive modeling: The city of New Rochelle, New York, has built a platform that combines visual computing and AI to model changes to the built environment. Citizens can evaluate proposed changes, make suggestions of their own, and see what their ideas would look like in practice.
• Dynamic services: The MIT Media Lab has worked on a platform that adapts zoning laws to the real-time preferences and needs of residents. The platform surveys locals on their preferences, collects data on living costs and other relevant variables, and updates zoning practices to evolving circumstances. The Lab has worked with Hamburg on an experimental project with this platform, which accelerated the construction of more than a thousand houses by more than a year.

The last two use cases are more experimental, but they have the potential to alter the way in which cities interact with their residents. In the not-so-distant future, we could imagine residents collaborating on all sorts of projects, with AI aggregating their contributions into coherent recommendations for the city. We could also envision dynamic public services — not just zoning laws, but also social programs, policing practices, or building codes — adapting to the real-time preferences and evolving needs of residents. If generalized, these capabilities would make cities altogether more responsive to popular input, providing a democratic counterpoint to the technocratic excesses of the “smart city” paradigm.

Key Success Factors

The question then becomes: How should cities think about integrating AI into their operations? Across our case studies, four success factors stand out:

Vision and strategy

First, a successful AI strategy fits into a broader vision for the city, with a clear set of priorities. In a lot of our interviews (about 70%), local leaders admitted that they experimented with AI without a clear goal. They launched pilot projects here and there, hoping that some would prove successful and scale. This approach costs more and delivers less.

Successful cities begin by identifying their most pressing needs and focus their use of new technologies accordingly. In other words, AI should not be implemented for its own sake — it is not a shiny object but a toolkit to solve specific problems. The question is not “How can we use AI?” but “What problem are we trying to solve, why, and how could AI help?” If AI is not the best means to tackle a given challenge, its implementation should not be forced. Conversely, if AI does prove useful, the development process should follow four simple steps:

1. Align on vision: Identify challenges and design a strategy with clear priorities.
2. Pilot: Test value and feasibility with limited users in a controlled environment.
3. Refine: Expand use to additional users, optimizing the technology along the way.
4. Scale: Roll out the technology to capture all the value.

The first step is often the most important. In every case, local leaders must begin by understanding their own context. What are the city’s needs and challenges? What will the city look like in 10 years? In New York City, for instance, former Sidewalk Labs CEO Dan Doctoroff and Robin Hood CEO Richard R. Buery, Jr. have advised the city to 1) automate citizen requests, 2) democratize access to information with chatbots, and 3) optimize traffic to tackle congestion because these were three consistent sources of frustration in citizen surveys, standing in the way of future-focused growth. In Las Vegas, the city created an “innovation district” for experimentation with autonomous vehicles to cement its position as a rising hub for transportation technology. Other cities that face a housing shortage might similarly prioritize AI-powered planning to accelerate construction and/or zoning.

Overall, developing an AI strategy is not about having a document called “an AI strategy.” It’s about establishing clear goals and priorities for the city, and then seeing how AI can help turn that picture into a reality.

Systematic de-bottlenecking

AI can improve city operations across every domain of urban life. But burdensome regulations and perverse incentives are often standing in the way. In our interviews, we consistently found that local bureaucracies resist transformation, either because current processes do not allow radical change or because the people in place have every reason to fight change. Cities must transform themselves before technology can transform them in turn.

In practice, this means:

• Including an innovation team in every city agency
• Creating incentives for bureaucracies to reform themselves
• Building an Office of Civic Innovation that rewards bold ideas with hackathons, prizes, and pilot programs
• Changing procurement processes to let startups compete for public contracts

In short, it means ensuring that the spirit of innovation thrives at every level of government.

For instance, until a few years ago, New York City could not partner with startups because its procurement process favored older, larger companies. Two years ago, the city changed purchasing requirements to encourage pilot programs, and the results speak for themselves: In 2023, over 600 companies applied to pilot products and over 50 pilots were deployed — 10 times more than in prior years. This is but one example, but it illustrates the kind of change that cities can bring to their operations. Across the board, local leaders should identify and remove the obstacles standing in the way of innovation.

Public-private partnerships (PPPs)

PPPs foster collaboration between city governments, private companies, academic institutions, and nonprofits. These partnerships facilitate the co-development of urban solutions, enable knowledge and resource sharing, and encourage experimentation with private initiative and public backing. For AI, a technology that researchers and startups understand a lot better than local governments, PPPs are crucial to success, because each side brings complementary qualities to the table.

In our case studies, we noticed that the most successful cities formed long-term partnerships rather than project-specific, short-term contracts, formalizing their arrangements in new institutions such as “advisory boards” or “smart city initiatives.” For instance, the city of Columbus, Ohio, has established the “Smart Columbus Initiative,” which involves the city, technology companies, universities, and community organizations. In five years, the initiative has developed AI-powered mobility solutions, logistics systems, and data-driven services.

Cities can also partner with venture capitalists to secure funding and support for their ventures. Along these lines, Toronto, Canada, has established the Toronto Innovation Acceleration Partners program, which connects startups with public-sector partners and university researchers to accelerate the development of civic-minded innovations. In the last few years, the program has not only turned Toronto into Canada’s new startup hub, but also led to dozens of AI projects in local government. Here as in most of our cases, all parties benefit from each other’s involvement.

Governance principles

Lastly, every step of the way, cities must enforce ethical AI principles.

First, municipalities should establish AI oversight boards to audit data sources and algorithms for accuracy and bias. Every agency will need clear processes to make sure that the datasets used to train AI are representative and don’t reinforce historical inequities related to demographics like race, gender, age, or ability status.

Second, every AI system interfacing with residents must be tested to ensure accessibility and inclusiveness across languages, dialects, and cultures. And any AI decision-support system must remain under human oversight for local leaders to retain full responsibility for their decisions.

Third, cities will need to obtain consent and protect the privacy of their citizens. Privacy protection should be engineered from the ground up rather than tacked on. Citizens deserve transparency into what data is collected, how it is secured, and how it is used.

Fourth, cities need to get citizen input along the way. At a minimum, they should organize grassroots campaigns to demystify AI, foster technological literacy, and understand popular concerns. At best, cities will also bring citizens into the development and deployment process – drawing on their feedback to ensure that the technology serves the community on its own terms.

With all this in place, cities can build sustainable models that inspire confidence rather than fear or skepticism. Only by putting AI ethics at the center of their approach can cities prove that they can be “smart” and humane at once.

A Call to Action

AI has the potential to reduce the size of bureaucracies and reinvent the way in which local governments make decisions, deliver services, and serve their citizens. But only with the right strategy can this potential translate into real solutions.

In some cities, the change will be evolutionary: Local leaders will seek incremental improvements, building AI on top of existing structures to deliver value sooner rather than later. In other cities, the change will be revolutionary: Local leaders will reimagine existing structures as if they started from scratch. Either way, AI can inject a culture of innovation within city hall that spills over far beyond city hall. If American cities heed this call, they will make their way back to the top of the smart cities rankings.

Ultimately, only with the right kind of leadership can cities harness the capabilities of AI. If technical acumen can open new doors, only local leadership can ensure that these doors lead to a better place. With more imagination, and less fear of innovation, cities can act as the architects of a new social contract between citizens and their communities.

Read the article here: How U.S. Cities Are Using AI to Solve Common Problems

The city resembles any number of urban centers – that’s the point. It has blocks of residential houses and commercial businesses, landscaped parks, roads, roundabouts, and traffic lights. There is a variety of vehicles, even a few police cruisers that have pulled over speeding motorists.

And it all fits in a single room in the basement of Hollister Hall.

Mini drones fly over the scaled city and broadcast positioning information, a tactic that could help coordinate mixed traffic, enhance safety and would be particularly valuable for protecting military convoys.

Welcome to the Information and Decision Science Laboratory. Here, a 20-by-20-foot “smart” city shrunk to 1:25 scale and its fleet of custom-built cars, drones, cameras and virtual reality technology are helping researchers design a better – and safer – future for transportation.

Specifically, the scaled smart city seeks to improve the performance of emerging mobility systems, such as connected and automated vehicles, by enabling small motorized cars to interact with their environment as well as other vehicles, some of which are piloted remotely by humans. Because the city is a controlled environment, experiments can be repeated and results verified. And yet, unpredictability is encouraged.

“If you don’t have an experimental testbed, you use simulation. And simulations are doomed to succeed. They’re always perfect,” said the lab’s director, Andreas Malikopoulos, professor of civil and environmental engineering in Cornell Engineering. “But in the real environment, you have miscommunication, errors, delays, unexpected events. This testbed can give us the opportunity to collect data and extrapolate information, something that we couldn’t do in the real world with real cars, because of safety concerns and the need for resources and space.”

The origins of the scaled city go back more than a decade, when Malikopoulos, previously a researcher at General Motors, was working at Oak Ridge National Laboratory. He was thinking about ways to improve energy efficiency in transportation via connected and automated vehicles.

“I said, OK, well, we can do all this,” he said. “But we need to have some hardware.”

Bad drivers wanted

In 2014, he received a grant for the efficiency project from the U.S. Department of Energy (DOE), and he pitched the creation of a scaled robotic testbed, but was told there wasn’t a need for one. Three years later, he left government research for a faculty position at the University of Delaware, where he was finally able to realize his “dream” of building the testbed. When Malikopoulos moved to Cornell in fall 2023, he scaled up – not in size, but in sophistication – by adding new features and upgrading his motorized fleet.

The lab is now on its fifth generation of the vehicles. The team members, many of them undergraduates, take off-the-shelf remote-control cars, gut their RC components and install custom electronics, sensors and Wi-Fi that connects to a mainframe computer. Each vehicle has a unique visual marker so it can be tracked by the testbed’s eight cameras and GPS system. Over the summer, the motor pool expanded to 75 cars and 15 mini drones. The aerial element allows for multiple drones to follow a vehicle and broadcast positioning information, a tactic that could help coordinate mixed traffic, enhance safety and would be particularly valuable for protecting military convoys.

The students don’t only work on hardware and software. They also get to be in the driver’s seat: The lab has six driver emulators where anyone can sit and “drive” through the scaled city, navigating from their car’s perspective and interacting with other vehicles on the road. The students can even communicate with those driverless vehicles through lab-designed modules that incorporate large language models.

The combination of automated and human-driven vehicles – also known as a cyber-physical system – introduces a level of uncertainty that can help the researchers better understand the ways connected and automated vehicles react in real time. The researchers can then design controllers and algorithms that will improve such responses.

Lab members also have the option of donning a VR headset to drive in a computer-simulated city and experience different traffic scenarios and conditions. The VR system can produce a digital twin of the testbed for more interactive driving.

“The benefit of VR is cost effectiveness, time efficiency and most importantly safety,” said Simon Tian, M.S. ’24 in systems engineering, the project lead of the VR testbed. “Experimenting with real vehicles is very expensive and very time consuming. Each experiment, you have to reset everything involving the vehicle, the people in the places, objects. If you want a specific experiment on a specific road, you have to build that road. If you want to do testing in Times Square, there’s no way you can have that place for yourself to test yet. But you can do it in VR. The technology is quite feasible.”

Tian and his team of six built their entire VR system from scratch using an open-source autonomous driving simulator called CARLA and their own Python coding.

Now the lab is the one place where bad driving is not only tolerated – it is required.

“We basically use machine learning to learn to model how humans make decisions,” said postdoctoral researcher Heeseung Bang, Ph.D. ’24. “In order to do that, you need a different kind of human driver’s data. But to collect those data, we need VR, so that people can actually just crash, and we see how aggressive drivers actually drive. Once we have the dataset, we can implement it on the city and see how they react.”

The vehicles introduce plenty of uncertainty of their own.

“This is a very complex system, and even if you repeat the same scenario, cars can make a different decision based on their different conditions, like, depending on their speed and time of entering the intersection,” Bang said.

Smarter vehicles, safer roads, greater equity

While the lab is focused on transportation, there is a parallel interest in social impact, and Malikopoulos is keen to explore ways to increase equity in the cyber-physical systems space. He is currently working with the Federal Highway Administration to make the testbed accessible as an educational module to other institutions, and he plans to create an online platform so researchers can log in, enter their algorithms and use the testbed to run experiments of their own.

In October, Malikopoulos received a collaborative $800,000 grant from the National Science Foundation’s Safe Learning-Enabled Systems program to establish a framework for smart autonomous systems to be deployed in complex operating environments while ensuring the systems can handle extreme events and monitoring them for irregular and unsafe behavior. The mathematical computing software company MathWorks recently provided a grant to use the scaled city testbed to develop real-time control toolboxes of autonomous driving systems.

The lab also recently completed a $4.3 million project from the DOE’s Advanced Research Projects Agency – Energy, in collaboration with the University of Michigan, Boston University, the Oak Ridge lab and Robert Bosch GmbH that improved the efficiency of Audi’s A3 plug-in hybrid by 25%.

The scaled city, located in the basement of Hollister Hall, seeks to improve the performance of emerging mobility systems, such as connected and automated vehicles, by enabling small motorized cars to interact with their environment as well as other vehicles, some of which are piloted remotely by humans.

“We used the scaled city to develop all algorithms and address technical challenges,” Malikopoulos said. “So when we went for field testing with the real Audi, we were ready.”

In addition to informing the design of the next generation of connected and automated vehicles, the scaled city is helping Malikopoulos train the next generation of engineers who will be working with these technologies. It turns out that a miniature city is a great recruitment tool.

That was the case for Shanting Wang, M.S. ’24, a doctoral student in systems engineering, who was invited last year by one of the lab members to check out the testbed. Wang was duly impressed, both by the lab’s technical innovations and its practical applications for civil engineering, and she joined the team.

“Transportation is about planning, but before, people did the planning based on historical data. For example, on Monday morning, there will be more traffic flow. But they just approximate,” she said. “Now, we can get the real-time data from the sensors, and from the real data we improve our algorithm and improve our controller. It can make traffic less congested. This is a place where I can find the solution, and it can be applied to the real world.”

The lab is expanding into an adjacent room to accommodate a second testbed. Malikopoulos has even toyed with the idea of increasing the scale of the city. He estimates he could possibly go as large as 1:10.

Sometimes smaller really is smarter.

For now, Malikopoulos has his dream city, which includes, as it must, a dream car.

In the real world, Malikopoulos drives a Fiat 500C.

“That’s something that my grandpa used to drive back in Greece – the original,” he said. “But if I could afford it, I would most likely have this.”

He pointed to a small, shiny car parked between miniature buildings on a fake lawn.

It was a red Ferrari.

Read the article here: Mini smart city drives design of safer automated transportation