Solar-powered asset trackers made their debut in the mid-2010s with a primary focus on unpowered assets that required extended monitoring periods with minimal service and maintenance support. Over the last decade, the variety of assets tracked using solar technology has expanded significantly. This expansion has been driven by solar efficiency gains and the use of long-lasting backup batteries, enabling these devices to continuously operate even during extended periods of limited sunlight.

Solar-powered asset trackers offer an innovative solution for trailer tracking, utilizing solar energy to provide near real-time data on trailer location and condition. By combining efficiency and sustainability, transportation and logistics providers stand to benefit greatly from the use of solar-powered trackers.

Benefits of using Solar-Powered Asset Trackers for Trailer Tracking

Using solar power for trailer tracking offers companies a cost-effective, sustainable, and versatile option, eliminating the need for frequent battery replacements and reducing environmental impact of using one-time use or high maintenance solutions. Solar-powered asset trackers ensure reliable and uninterrupted tracking, making them ideal for monitoring trailers.

Cost Efficiency	Sustainability	Uninterrupted Monitoring
Although the initial investment in solar asset tracking devices may be higher, they can yield superior long-term cost savings compared to battery-powered devices by eliminating the maintenance costs associated with battery replacements or recharging.	Solar-powered devices harness  a renewable and clean energy source. This reduces the environmental impact associated with battery-powered devices by eliminating maintenance trips to service these devices and the disposal of electronic waste due to battery replacements.	When sunlight is scarce, solar-powered devices use backup batteries to ensure uninterrupted tracking. This is especially important for long-term tracking use cases, as it ensures the tracking device remains operational.

Key Trailer Tracking Challenges

Monitoring trailers used in transportation presents several challenges, primarily stemming from the need for continuous monitoring and tight security. Ensuring the safety and security of goods in transit requires constant monitoring of trailer location, condition, and cargo integrity which can be hindered by limited visibility into key events such as a trailer door being unexpectedly opened while the trailer is stationary or a sudden temperature drop within the trailer.

Cargo theft remains a significant and escalating concern within the North American transportation & logistics sector. According to a CargoNet study, in 2023, cargo theft incidents increased 57% YoY resulting in a total of nearly $130 million of goods stolen.

Additionally, maintaining optimal trailer performance requires proactive monitoring of mileage to determine replacement cycles for tires and, if transporting temperature-sensitive goods, continuous monitoring of the trailer temperature to meet customer and regulatory requirements to prevent product spoilage.

Conclusion

Solar-powered asset trackers are transforming trailer tracking by combining efficiency and sustainability, offering numerous benefits such as continuous tracking capabilities, reduced environmental impact, and improved security and maintenance. These trackers offer transportation and logistics provide the unique opportunity for a more efficient and sustainable future in trailer tracking.

About Positioning Universal

Established in 2013, Positioning Universal is the leading global provider of off-the-shelf and customizable IoT devices, along with GPS-based monitoring solutions for vehicles and assets. Our Systems Integration (SI) services deliver turn-key solutions for smooth IoT implementations, leveraging our team’s extensive industry knowledge. With a deep understanding of IoT technologies, we guide companies in designing and deploying IoT solutions that meet their unique needs. Our comprehensive offerings, paired with best-in-class customer support, empower businesses with the essential business intelligence to sustain a competitive edge in rapidly evolving markets.

Positioning Universal’s Solar-Powered Asset Trackers

Positioning Universal launched its solar asset trackers, the TT600 and TT603¹, in 2019. Key competitive differentiators for the TT600 and TT603 are:

Positioning Universal continuously monitors relevant technological advancements, including emerging developments in solar technologies, in our constant quest to improve the quality of the telematics solutions we provide to our resellers and customers.

^{1 TT603 has a built-in connector to connect temperature sensors, door sensors, and to detect tractor power.}

Telematics systems are harnessing the power of machine learning to transform vehicle maintenance from reactive to predictive. By analyzing vast amounts of real-time data, machine learning algorithms can detect patterns and anomalies that may indicate potential vehicle issues. This enables fleet managers and maintenance teams to proactively address these issues, thus avoiding costly breakdowns and minimizing downtime. With predictive maintenance, vehicle servicing can be scheduled based on actual need rather than arbitrary intervals, resulting in significant cost savings and improved operational efficiency.

Moreover, machine learning-powered telematics systems constantly learn and adapt, becoming smarter over time. They can identify various factors that contribute to vehicle wear and tear, such as driving behavior and environmental conditions, to provide accurate predictions. This data-driven approach not only enhances vehicle reliability and safety but also helps optimize fleet operations.

What is Machine Learning and How Does It Relate to Telematics?

Machine learning is a subset of artificial intelligence (AI) that enables computers to learn and make predictions or decisions without being explicitly programmed. It involves the development of algorithms that can analyze and interpret data, identify patterns, and make informed decisions or predictions based on that analysis.

When machine learning is applied to telematics, it allows for the intelligent analysis of vehicle data to detect patterns and anomalies that may indicate potential issues. By continuously learning and adapting, machine learning algorithms can provide valuable insights and predictions for predictive vehicle maintenance, taking into account various factors such as driving performance and historical data.

How Machine Learning Improves Efficiency in Vehicle Maintenance

Machine learning brings significant improvements to efficiency in vehicle maintenance through its ability to analyze large volumes of data and make accurate predictions. By continuously learning from the data it receives, machine learning algorithms can identify patterns and anomalies that may indicate potential issues or failures.

Traditionally, vehicle maintenance has often been performed based on fixed schedules or reactive responses to unexpected breakdowns. This approach is costly and inefficient, as it does not take into account the actual condition of the vehicle. Machine learning in telematics changes this by enabling predictive maintenance. By analyzing real-time and historical data, machine learning algorithms can detect early warning signs of potential problems, allowing for proactive intervention.

Predictive maintenance not only reduces the risk of unexpected breakdowns but also improves the efficiency of maintenance operations. Instead of servicing vehicles based on arbitrary intervals, resources can be allocated based on actual need. This eliminates unnecessary maintenance tasks and reduces the overall cost of maintenance operations. Additionally, by addressing potential issues before they escalate, the downtime caused by breakdowns can be minimized, leading to improved operational efficiency.

Furthermore, machine learning algorithms can optimize the scheduling of maintenance tasks based on various factors such as engine health data, vehicle usage patterns, and historical maintenance data. By considering these factors, maintenance teams can prioritize vehicles that require attention, ensuring that resources are allocated efficiently. This not only improves the overall efficiency of maintenance operations but also extends the lifespan of vehicles by addressing issues before they lead to major failures.

In summary, machine learning improves efficiency in vehicle maintenance by enabling proactive and predictive maintenance, optimizing resource allocation, reducing unnecessary maintenance tasks, and minimizing downtime caused by breakdowns.

The Role of Data in Machine Learning for Telematics

Data plays a vital role in machine learning for telematics. The effectiveness of machine learning algorithms depends on the quality and quantity of data available for analysis. Telematics systems generate a vast amount of data from various sources, including vehicle sensors, GPS, and driver behavior monitoring.

This data is collected and transmitted to a central system, where it is processed and analyzed by machine learning algorithms. The algorithms identify patterns and anomalies in the data, which are then used to make predictions or decisions. The more data available for analysis, the better the accuracy and reliability of the predictions.

Data in machine learning for telematics can be categorized into two main types: real-time data and historical data. Real-time data refers to the data collected from vehicles in real-time, providing up-to-date information on vehicle performance, driving behavior, and environmental conditions. Historical data, on the other hand, refers to the data collected over a period of time, providing insights into long-term trends and patterns.

Both real-time and historical data are crucial for machine learning algorithms to make accurate predictions. Real-time data allows for immediate detection of anomalies and potential issues, enabling proactive intervention. Historical data provides insights into long-term trends and patterns, allowing for the identification of recurring issues and the development of more accurate predictions.

To ensure the quality of the data, it is important to have proper data collection and storage mechanisms in place. This includes ensuring data accuracy, completeness, and integrity. Additionally, data security and privacy measures should be implemented to protect sensitive information.

In conclusion, data plays a crucial role in machine learning for telematics. The availability of high-quality data is essential for the accurate analysis and prediction of potential vehicle issues, enabling proactive and predictive maintenance.

Implementing Machine Learning in Telematics: Challenges & Considerations

Implementing machine learning in telematics for predictive vehicle maintenance comes with its own set of challenges and considerations. While the benefits are significant, there are several factors that need to be taken into account to ensure successful implementation.

One of the key challenges is data quality and availability. Machine learning algorithms rely on large volumes of high-quality data for accurate predictions. However, ensuring data quality and availability can be a complex task. Data collected from different vehicles may vary in terms of quality, completeness, and accuracy. Additionally, data storage and retrieval processes need to be efficient to handle the large volumes of data generated by telematics systems. Telematics providers have addressed this challenge by offering cloud services with scalable data retrieval and storage from leading cloud solutions such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud.

Another challenge is the need for skilled personnel who can develop, implement, and maintain machine learning algorithms. Machine learning is a specialized field that requires expertise in data analysis, algorithm development, and system integration. Organizations either need to invest in training and hiring skilled professionals to successfully implement machine learning in telematics or partner with telematics providers that have this expertise.

Data security and privacy are also important considerations when implementing machine learning in telematics. Telematics systems collect and transmit sensitive data, including vehicle performance, driver behavior, and location information. Organizations or their telematics partners must have robust security and data privacy measures in place to protect this data from unauthorized access or breaches.

Finally, there is a need for ongoing monitoring and evaluation of machine learning algorithms. As the algorithms learn and adapt over time, their performance needs to be continuously monitored to ensure accuracy and reliability. Feedback loops should be established to incorporate new data and improve the algorithms based on real-world experiences.

Despite these challenges, the benefits of implementing machine learning in telematics for predictive vehicle maintenance outweigh the drawbacks. With careful planning, coordination, and investment, organizations can harness the power of machine learning to drive efficiency and improve maintenance operations.

Future Trends and Advancements in Machine Learning for Telematics

The field of machine learning for telematics is rapidly evolving, with new advancements and trends shaping the future of predictive vehicle maintenance. Here are some key areas to watch out for:

1. Advanced anomaly detection: Machine learning algorithms are becoming more sophisticated in detecting anomalies and potential issues in vehicle data. As algorithms learn from more data, they can identify subtle patterns and abnormalities that may indicate impending failures, allowing for even more accurate predictions and proactive maintenance.
2. Predictive parts management: Machine learning algorithms can be used not only to predict potential failures but also to optimize parts management. By analyzing historical data, algorithms can predict the lifespan of various vehicle components and recommend proactive replacement or maintenance. This can help organizations optimize parts inventory, reduce downtime, and improve cost-efficiency.
3. Enhanced driver behavior monitoring: Machine learning algorithms can analyze driver behavior data to identify patterns and trends that may impact vehicle performance and maintenance needs. By providing real-time feedback and recommendations, algorithms can help drivers adopt safer and more efficient driving habits, reducing vehicle wear and tear and improving overall fleet performance.

These future trends and advancements highlight the continued growth and potential of machine learning in telematics for predictive vehicle maintenance. As technology continues to evolve, organizations can expect even more efficient and cost-effective maintenance operations, leading to improved operational efficiency and enhanced vehicle availability and reliability.

Conclusion

Telematics systems are harnessing the power of machine learning to transition vehicle maintenance from reactive to predictive. By analyzing vast amounts of real-time and historical data, machine learning algorithms can detect patterns and anomalies that may indicate potential vehicle issues. This enables proactive and predictive maintenance, reducing the risk of unexpected breakdowns and optimizing resource allocation.

The benefits of machine learning in telematics for predictive vehicle maintenance are significant. It improves efficiency by enabling proactive intervention, optimizing vehicle servicing schedules, and reducing unnecessary maintenance tasks. It enhances vehicle reliability and safety by analyzing various factors that contribute to wear and tear. It also helps organizations optimize fleet operations and improve overall operational efficiency.

While there are challenges and considerations in implementing machine learning in telematics, organizations can overcome them with proper planning, coordination, and investment and by partnering with companies that are already using machine learning in their telematics solutions.

As the field continues to evolve, future trends and advancements in machine learning for telematics hold great promise. Advanced anomaly detection, predictive parts management, and enhanced driver behavior monitoring are just a few of the areas to monitor going forward.

About Positioning Universal

Established in 2013, Positioning Universal is the leading global provider of off-the-shelf and customizable IoT devices, along with GPS-based monitoring solutions for vehicles and assets. Our Systems Integration (SI) services deliver turn-key solutions for smooth IoT implementations, leveraging our team’s extensive industry knowledge. With a deep understanding of IoT technologies, we guide companies in designing and deploying IoT solutions that meet their unique needs. Our comprehensive offerings, paired with best-in-class customer support, empower businesses with essential business intelligence to sustain a competitive edge in rapidly evolving markets.

Non-terrestrial networks (NTNs) are communication systems that extend connectivity beyond the reach of traditional terrestrial networks to enable services to underserved communities and remote areas. By transcending the limitations of traditional terrestrial network infrastructure, non-terrestrial networks offer the potential to offer enhanced or previously unavailable services to various use cases including open ocean shipping container monitoring, remote monitoring of oil rigs, and assisting with search and rescue and emergency response.

Background

NTNs are networks or segments of networks that use satellite constellations (GEO/MEO/LEO), airborne vehicles (High-Altitude Platforms or HAPs), and drones to extend the coverage and capacity of terrestrial networks.

By extending coverage beyond terrestrial networks, NTNs provide services to previously underserved and remote areas such as rural communities and sparsely populated islands. NTNs will also be able to offer ubiquitous and reliable connectivity for IoT applications such as tracking vehicles in areas with limited cellular coverage, tracking cargo on ships at sea, and monitoring assets in remote areas.

NTNs also add an additional layer of resilience and redundancy to existing terrestrial networks. In the event of natural disasters or network outages, NTNs provide backup connectivity to ensure continued service for mission-critical communications and applications.

NTN Example #1: Low-Earth Orbit (LEO) Constellations

Companies with LEO satellite constellations, which operate at altitudes ranging from 500 to 1,200 kilometers, have emerged as early NTN leaders. LEO satellites offer faster data transmission, reduced latency, and improved coverage compared to traditional geostationary satellites (GEO). Key players include Iridium, Globalstar, Orbcomm, Starlink, OneWeb, Project Kuiper by Amazon, Telesat, and Planet Labs.

Iridium

The Iridium satellite constellation, which was initially launched in 1997, provides L band voice and data information coverage to satellite phones, satellite messenger communication devices and integrated transceivers, as well as two-way satellite messaging service to supported Android smartphones.

Iridium’s constellation consists of 66 active LEO satellites with additional spares to serve in case of failure. Iridium reached over 2 million subscribers at the start of 2023.

SpaceX’s Starlink

Starlink is a LEO satellite internet constellation which currently consists of over 4,500 satellites which communicate with designated ground transceivers. Starlink plans to deploy nearly 12,000 satellites with a possible later extension to 42,000. Starlink announced in May it had reached 1.5 million subscribers.

NTN Example #2: High-Altitude Platforms (HAPs)

High-Altitude Platforms (HAPs) are aerial systems positioned at altitudes between traditional aircraft and satellites, typically ranging from 20 to 50 kilometers above the Earth’s surface. HAPs serve as a middle ground between satellites and ground-based systems, providing advantages like cost-effectiveness and rapid deployment.

Key players include Airbus (Zephyr solar-powered aerial vehicles), Thales Alenia Space, Lockheed Martin’s HAPMobile subsidiary, AeroVironment, and Stratodynamics Aviation.

Positioning Universal’s NTN View

Positioning Universal continues to monitor NTN activities and has had discussions with numerous emerging IoT connectivity providers to explore ways to improve the performance and cost effectiveness of our services and to consider new services and markets.

Source: Award Solutions

High-precision GPS improves signal accuracy from meters to centimeters using augmentation systems, such as Real-Time Kinematics (RTK) receivers, and/or multi-frequency modules/chipsets.  High-precision GPS use cases include surveying, precision agriculture, drone navigation, advanced driver assistance, and lane-level driving navigation instructions for autonomous vehicles.

Background

The U.S. GPS system commits via its Positioning Service Performance Standard (SPS) to broadcasting GPS signals with a daily global average user range error (URE) of ≤2m (6.6 ft.) with 95% probability.  URE, however, is not the end user accuracy as this depends on factors such as signal blockage, atmospheric conditions, and the design/quality of GPS devices. GPS device accuracy typically ranges from 2 to 2.5 meters.

Augmentation Systems: Real-Time Kinematics (RTK) Receivers

An RTK-capable GPS receiver takes in the normal signals from Global Navigation Satellite Systems (GNSS) along with a Radio Technical Commission for Maritime Services (RTCM) correction stream to achieve ≤1cm positional accuracy.

RTK uses carrier-based ranging which relies on base stations to provide real-time corrections resulting in centimeter-level accuracy. Carrier-based ranging calculates the range by determining the number of carrier cycles between the satellite and the base station, and multiplying this number by the carrier wavelength, resulting in more precise positions. RTK is commonly used in applications that require high precision, such as surveying, precision agriculture, and drone navigation.

In traditional code-based positioning, a GPS device’s position is established by correlating with four or more satellites to determine their ranges. Using these ranges and the position of the satellite, the GPS device’s position can be established within several meters.

Multi-Frequency Receivers: Single-, Dual- and Triple-Frequency GPS

GPS operates on three L-band frequencies: L1, L2 and L5.

Single-Frequency

Single-frequency receivers use the L1 band. The primary limitation of this band is that it’s effected by multi-path errors. Multi-path errors are caused when a satellite signal reaches the receiver from two or more paths, one directly from the satellite and the others reflected from nearby buildings or other surfaces.

Dual-Frequency

Dual-frequency receivers use the L1 and L5 bands. The L5 band can detect unwanted reflected signals caused by multi-path errors. Receivers using the L1 and L5 bands identify these unwanted signals and ensure that they do not affect the receiver’s output information.

Dual-frequency GPS chipsets have primarily been used on smartphones and smart watches and for specific high-precision IoT use cases. The first dual-frequency smartphone was the Xiaomi Mi 8 which was launched in May 2018. Dual-frequency modules/chipsets have historically been more expensive given they require more processing power and special hardware such as antennas that are capable of tracking multiple frequencies. As the cost of dual-frequency modules/chipsets has come down, they are now been used by leading IoT companies to improve their device performance.

Triple-Frequency

Triple-frequency receivers can receive a multitude of signals from any GNSS system. Triple-frequency receivers using GPS can receive navigation signals from the L1, L2, and L5 bands.

Companies offer triple-frequency receivers/modules which can handle multiple constellations and frequencies including GPS, Galileo, BeiDou, and GLONASS. These solutions provide accuracy to the centimeter.

Given the minimal accuracy gain compared to a dual-frequency receiver, demand for triple-frequency receivers is not expected to take off until autonomous vehicles or vehicles fitted with advanced driver assistance systems begin to ship in larger quantities.

Positioning Universal’s High-Precision GPS Activities

Positioning Universal has been actively working to improve the GNSS accuracy of our devices. We already have product designs in place to move from the industry norm from 2 – 2.5 meters to 1-meter accuracy. This improved accuracy will offer our customers new capabilities such as being able to identify the specific lane a vehicle is traveling in on a highway and to locate a vehicle in a specific parking space on an auto dealer’s lot.

We are also launching products with dual-frequency modules/chipsets in Q3 2023, which coupled with new applications, will further improve our device accuracy from 1-meter to centimeters.

Source: Geospatial World

The current Bluetooth standard, introduced in December 2016, is Bluetooth 5.0.  Bluetooth 5.0 was updated in December 2019 with the releases of versions 5.1 and 5.2.

Bluetooth 5.1 introduced a significant feature called Direction-Finding, which enables more precise location tracking in telematics devices. This feature utilizes Angle of Arrival (AoA) and Angle of Departure (AoD) techniques to estimate the direction of a Bluetooth signal. AoA determines the angle at which a signal arrives at a receiving device, while AoD determines the angle at which a signal is transmitted from a transmitting device.

Bluetooth Use in Telematics

Bluetooth technology is primarily used in telematics for vehicle connectivity to establish wireless connections between smartphones and/or tablets and in-vehicle systems. This connectivity enables companies and their drivers to monitor vehicle data and to receive alerts for any issues that may need immediate attention.

Bluetooth at Positioning Universal

At Positioning Universal, we make sure that every mobile IoT device is offered with a Bluetooth chip component (sometimes optional). We use chips with the latest Bluetooth versions to ensure our customers have the advantages of the latest technologies.

Telematics solutions continue to quickly evolve driven by improvements in core technologies including Bluetooth. At Positioning Universal, we remain at the forefront of these advancements, enabling us to continually enhance the value we deliver to our customers.  We support advanced Bluetooth applications such as Bluetooth tag tracking and indoor location services using Bluetooth beacons.

As an example, using hard-wired devices in electric vehicles are troublesome for the following reasons:

• Electric vehicles’ electrical architecture is different from conventional vehicles so installing a telematics device without proper understanding or expertise can risk damaging their electrical components
• Electric vehicles sometimes don’t have available, or easy access to, a constant 12-volt source of power

While battery components are often overlooked while evaluating IoT device performance, poor performing batteries can hinder the performance and reliability of telematics systems by hampering their ability to gather and transmit the crucial data companies need to run their daily operations.

Background

Batteries are the heartbeat of our modern world, powering essential functions like how people connect and communicate, find information, transport people and goods, and power homes and buildings. Batteries are especially critical in powering a wide range of commercials applications. The most common battery technologies used for commercial applications include:

Lithium-ion (Li-ion) batteries, with their high energy density, long cycle life, lightweight design, and relatively low self-discharge rates, have emerged as the most dominant battery technology used for commercial applications.

Global Li-ion battery production capacity is forecast to increase eightfold by 2027. Li-ion battery production is dominated by China with 79% market share followed by U.S. with 6% market share.

Despite their increasing popularity, Li-ion batteries have several key noteworthy limitations including:

• Safety Concerns: While Li-ion batteries are generally safe, they can be prone to thermal runaway and overheating under certain conditions. Li-ion battery failures in EVs, smartphones, and e-bikes have led to fires which have made national news. Airlines also restrict bulk shipments of Li-ion batteries in cargo areas due to fire risk concerns.
• Geopolitical Concerns: China dominance of the global Li-ion market, which includes production control of most of the parts that make up a battery, are a risk given current global tensions.

Companies are working on new battery technologies to address these limitations. Examples of promising new battery technologies include solid-state, lithium sulfur, zinc–manganese oxide, and sodium-ion.

Battery Use in Telematics

Batteries play a crucial role in telematics serving as power sources for asset tracking devices and as backup batteries in devices hard-wired into vehicles or assets in the event of power loss. Recent advancements in battery technology have significantly enhanced the telematics market, improving the efficiency, reliability, and performance of connected devices.

Positioning Universal’s Smart Power^SM Technology

Positioning Universal is an established, recognized industry pioneer in providing innovative hardware, software and services. Positioning Universal’s SVR Tracking business was awarded patent 11482057 in 2022 for its method and system for battery management for mobile geofencing devices.

Our patented Smart Power^SM  technology delivers high-performance battery power management with the ability to automatically lower power consumption to preserve battery life during extreme weather conditions or in areas with poor cellular network connectivity or coverage. We use this patented technology in our self-powered wireless devices used in numerous market applications including vehicle finance, dealer lot management, equipment rental, and cargo & merchandise tracking.

The parameters used to train the model increased from 117 million in 2018 to a staggering 1 trillion in 2022 as shown in the table below:

AI has already had a transformative effect across many markets such as using chatbots for customer service, assisting credit card companies in identifying fraud, and improving logistics through optimized routes.

Companies with fleet operations not using AI-powered connected car technology solutions face higher accident risks and costs, increased operational expenses, the potential for unexpected downtime and expensive repairs which places them at a competitive disadvantage.

Background

AI is a field of computer science that is creating intelligent systems capable of performing tasks that typically require human intelligence. It involves developing algorithms and models that enable machines to learn, reason, and make decisions based on data and patterns. AI encompasses various subfields, including machine learning, natural language processing (NLP), computer vision, and robotics.

Early deployments of AI systems date back to the 1960s, with notable examples including the ELIZA program for conversation simulation and expert systems like DENDRAL for chemical analysis.

Generative AI is a subset of AI that specifically focuses on creating models and algorithms capable of generating new and original content, such as images, text, or audio. It uses techniques like deep learning, neural networks, and probabilistic models to produce outputs that mimic or create novel content based on patterns and examples from training data.

AI In Connected Car Technology

AI was initially used in connected car technology for route optimization by analyzing traffic data and suggesting optimal routes. AI is also being used to monitor driver behavior, analyzing factors like speed, acceleration, and braking patterns to identify risky driving behaviors and convert this data into driver scores. Additionally, AI is being deployed for predictive maintenance, using data on engine performance, fuel consumption, and usage patterns to anticipate maintenance needs and reduce downtime and repair costs.

Positioning Universal’s AI-Powered Dashcams

Positioning Universal’s AI-powered dashcams, coupled with our connected car technology devices and software, mitigate accident risks and associated costs, provide video evidence to help exonerate drivers from accident claims, optimize fuel consumption through route optimization, and enhance preventative maintenance activities to reduce downtime and expensive repairs.