When we founded Peregrine in 2018, my co-founders and I had just spent years working on self-driving car initiatives across Silicon Valley and Europe. Looking at the broader mobility landscape, we noticed a glaring disconnect: commercial vehicles were being outfitted with cameras at a rapid pace, yet those cameras were fundamentally “dumb.” They were defined by hardware limitations and lacked the cutting-edge machine learning required to actually understand the world around them.

Today, most fleet operators still look at a commercial dashcam and see a necessary expense: a classic cost center justified only by the money it saves when exonerating a driver or lowering an insurance premium.

But what if your cameras could actively generate revenue when things go right?

We are in the midst of a massive paradigm shift in mobility and logistics. The conversation is moving rapidly away from basic event recording and toward Data-as-a-Service (DaaS). By leveraging Edge AI and Visual SLAM (Simultaneous Localization and Mapping), forward-thinking telematics providers and fleet operators are transforming their vehicles into roaming data-collection engines—and they are monetizing the results.

Here is how the transition from cost center to revenue generator is happening, and why Visual SLAM is the underlying engine making it possible.

The Problem with the Status Quo

Historically, trying to extract broader value from fleet video has been a logistical and financial nightmare.

Standard telematics rely on GPS and G-force sensors. They tell you where a vehicle was and if it braked hard, but they lack visual context. To get that context, traditional systems rely on sending massive amounts of raw video data to the cloud. This approach is fundamentally broken for three reasons:

• Prohibitive Cellular Costs: Streaming hours of high-definition video over LTE/5G destroys profit margins. For asset-light 3PLs operating on razor-thin margins, bloated data bills are a non-starter.
• High Latency: Processing data in the cloud means delayed insights, making real-time intervention impossible.
• Privacy Liabilities: Uploading raw, unredacted footage of pedestrians and license plates to central servers is a massive compliance risk under the GDPR and emerging EU Data Acts.
  

To monetize data, you need rich, contextual information at scale. You cannot achieve that if you are paying exorbitant fees just to transmit heavy, high-risk data.

The Catalyst: Visual SLAM and Hardware-Agnostic Edge AI

The solution isn’t building better cloud infrastructure; it is bringing the intelligence directly to the camera. This is where Edge AI and Visual SLAM change the game.

Visual SLAM is a computer vision technique that allows an AI to map its environment and understand its exact location within that environment simultaneously, using only camera inputs. Instead of recording a dumb video file, our AI analyzes the scene on the device in real-time.

Through the continuous R&D happening at Peregrine Labs, we have built hardware-agnostic models that can run multi-task neural networks on the low-power embedded devices already installed in your fleets. The AI identifies road signs, detects lane markings, spots surface degradation, and measures traffic density natively.

Instead of sending gigabytes of raw video to the cloud, the camera sends a few kilobytes of highly structured, contextual metadata.

Entering the Telematics DaaS Market

Once your fleet is generating lightweight, structured environmental data rather than heavy video files, you have officially entered the DaaS ecosystem. Your delivery vans, taxis, and long-haul trucks are driving the same routes every day, acting as an automated, self-updating sensor network.

The global smart city infrastructure market is currently valued at over $170 billion, driven heavily by the need for accurate GIS (Geographic Information System) data. So, who wants to buy the data your fleet is collecting?

• Municipalities and Urban Planners: City governments spend millions on manual road infrastructure campaigning and mobile LiDAR surveys. Fleets equipped with our Data Services architecture can automatically detect and log broken traffic lights, missing stop signs, and potholing, selling this real-time GIS data directly to road authorities at a fraction of traditional survey costs.
• Dynamic Map Providers: Companies building next-generation navigation and autonomous driving systems need constant, localized updates on lane closures, temporary construction zones, and speed limit changes.
• Insurtech and Traffic Modellers: Hyper-local data on traffic density, weather conditions, and near-miss intersections is incredibly valuable for predictive risk modeling.
  

The Privacy Prerequisite: Anonymization at the Edge

I have to be clear about one thing: You cannot monetize fleet data if you are violating privacy laws. If you attempt to sell or share urban data that contains unredacted faces or license plates, you will face severe regulatory backlash. This is why petabyte-scale video anonymization must happen at the edge.

Before any image or data point leaves the vehicle, the AI must automatically blur personally identifiable information (PII). By ensuring absolute privacy compliance natively on the device, our Peregrine Vision technology allows fleets to confidently participate in the DaaS economy. They can provide indisputable, context-aware proof of road conditions without inheriting the massive liability of managing raw public surveillance data.

The Road Ahead

We are moving past the era of the dumb camera. We started Peregrine because we believed vehicle sensors generating visual context should be the spark for new value creation.

The future of fleet management belongs to those who understand that every mile driven is an opportunity to harvest valuable, structured data. By upgrading to contextual vision and embracing the DaaS model, telematics providers can finally flip the script. Your fleet is already out there mapping the world every single day. It is time you started getting paid for it.

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We’ve all seen the prototype vehicles outfitted with spinning laser arrays on their roofs. They are engineering marvels, spinning tens of thousands of dollars’ worth of delicate hardware to generate precise, dense point clouds of their environment. In a controlled R&D setting with unlimited budgets, LiDAR is spectacular.

But the real world doesn’t operate on unlimited budgets.

Over the past few years, speaking with leaders in logistics, telematics, and smart cities, I’ve heard the same frustration repeatedly. They want the intelligence that comes with 3D spatial awareness—knowing exactly where a vehicle is in a tunnel, mapping loading dock obstructions in real-time, or quantifying road degradation—but they cannot afford the “hardware tax” of LiDAR.

You cannot put a $5,000 sensor on a delivery van that operates on razor-thin margins. You cannot scale a technology that requires constant re-calibration if a driver hits a pothole too hard.

At Peregrine, we made a contrarian bet early on. We bet that eventually, sophisticated software running on standard, inexpensive cameras would outperform specialized, expensive hardware.

That bet has paid off. The future of spatial intelligence at scale isn’t lasers; it’s Visual SLAM powered by Edge AI.

The Magic Trick: Getting 3D Data from 2D Images

If we want machines to navigate the world like humans do, we should look at how humans do it. We don’t shoot laser beams out of our eyes to measure distance. We use passive sensors (our eyes) to take in 2D information, and a highly efficient neural network (our brain) to instantaneously translate that into a 3D understanding of the scene.

This is the essence of Visual SLAM (Simultaneous Localization and Mapping).

In simple terms, vSLAM uses camera feed(s) to map an unknown environment while simultaneously keeping track of the camera’s location within it.

Historically, this was incredibly difficult for computers. Early vSLAM struggled with varied lighting, featureless walls, or dynamic movements. But recent breakthroughs in deep learning and neural networks have fundamentally changed the game.

• Image Suggestion: A screenshot or GIF of the Peregrine “SLAM in motion” visualization (from your homepage), showing the camera path being traced through a 3D point cloud of a city street.
• Alt Text: Visual SLAM technology mapping a city street in real-time using only camera data. By training our AI models on vast amounts of diverse driving data, we have taught our software to perform tasks that previously required active sensors:
• Monocular Depth Estimation: Our AI can look at a flat, 2D image from a single standard dashcam and accurately predict the depth of every pixel in the scene, understanding relative distances just by analyzing visual context, lighting, and shadows.
• Visual Odometry: By tracking thousands of distinct “features” (like the corner of a building or a road sign) across consecutive frames, the software calculates precisely how the vehicle has moved in 3D space—even without a GPS signal.
• Dense Mapping: Instead of just sparse points, we can reconstruct dense, semantic 3D maps of the environment in real-time.
  

The Catalyst: Why This is Possible Now

If the math for vSLAM has existed for a while, why is it only taking off now?

The missing link was computational power at the edge.

Until recently, running complex deep learning models to turn 2D video into 3D maps required racks of servers. You couldn’t do it live in a vehicle. But the explosive improvement in the efficiency of neural networks, combined with powerful, low-energy edge processors, has closed the gap.

This is Peregrine’s core expertise. We don’t just build AI models; we optimize them ruthlessly to run on the edge. We aren’t streaming terabytes of video to the cloud to figure out where a curb is located. That processing happens in milliseconds, right on the device, inside the vehicle.

Moving Beyond Robotaxis: Real-World Applications

The beauty of shifting spatial intelligence from hardware (LiDAR) to software (vSLAM) is democratization. Suddenly, advanced perception isn’t just for million-dollar robotaxi prototypes. It’s available for the hundreds of millions of commercial vehicles already on the road.

What does this unlock today?

1. GPS-Denied Navigation

Logistics fleets operating in urban canyons, tunnels, or massive warehouse interiors often lose GPS signals. Our vSLAM tech takes over seamlessly, providing precise localization based solely on visual surroundings, ensuring asset tracking never goes dark.

2. Automated Infrastructure Auditing

Cities currently spend fortunes sending crews to manually inspect roads. A garbage truck equipped with a standard camera and Peregrine’s software can automatically generate a 3D map of potholes, cracked pavement, and faded lane markings as it drives its normal route.

3. Next-Gen Telematics

We are moving beyond simple “hard braking” alerts. By understanding the 3D spatial context of a near-miss, we can tell a fleet manager why it happened, distinguishing between risky driving and defensive maneuvers.

“The future of seeing isn’t about adding more expensive eyes. It’s about building a smarter brain.”

The Verdict

LiDAR will always have niche applications in highly specialized environments. But for mass-market mobility, the war is over. The combination of cheap, reliable CMOS image sensors and increasingly brilliant Edge AI software is the winning formula.

Ready to see Visual SLAM in action?

Don’t rely on outdated hardware. Discover how Peregrine Vision transforms standard video into deep spatial insights.

Explore Peregrine Vision >

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Landshut, Germany – [November 2024] – Peregrine.ai, a pioneering force in AI-powered vision solutions, has deployed its state-of-the-art 5-SAFE system to revolutionize traffic safety in school zones. Designed and developed in collaboration with T-Systems, Landshut University of Applied Science, and the City of Landshut, and backed by Germany’s 5G Initiative and the Federal Ministry for Digital and Transport (BMDV), this transformative project harnesses the power of AI and 5G to set a new benchmark in urban safety.

Smart, Adaptive, and Privacy-Centric Safety in Real-Time

At the core of 5-SAFE is Peregrine.ai’s sophisticated edge-based computer vision technology, meticulously engineered to identify and respond to critical safety risks in real time. Leveraging 5G-enabled sensors and dynamic, adaptive traffic signage, the system proactively monitors pedestrian and vehicular activity, triggering alerts only when genuine risks are detected. This dynamic response reduces driver desensitization to warnings, enhancing attention precisely when it’s needed most.

In a groundbreaking enhancement, 5-SAFE includes a 24/7 cyclist protection feature that alerts right-turning vehicles to approaching cyclists—a safety innovation that extends beyond traditional school hours to safeguard vulnerable road users around the clock.

“Our mission is to redefine urban safety standards through AI and edge computing,” said Philip Meier, Head of Project Delivery and Partnerships at Peregrine.ai. “With 5-SAFE, we’re pushing the boundaries of what’s possible in real-time safety, providing precision intelligence that’s adaptive, scalable, and inherently privacy-first.”

Engineered for Privacy and Agility

Peregrine.ai’s 5-SAFE system is designed with a relentless focus on privacy and hardware agility. Utilizing NVIDIA’s TensorRT for high-speed, on-edge processing, all data is anonymized directly within the camera, ensuring that no personal information is stored or transmitted offsite. This approach not only guarantees GDPR compliance but also removes latency issues, making 5-SAFE a lightning-fast, privacy-secure solution for modern urban environments.

The system’s hardware-agnostic architecture is another major innovation, allowing seamless integration across a range of platforms—from mobile dash cams to stationary infrastructure. This adaptability means that 5-SAFE can be deployed in diverse settings, from school zones to high-traffic intersections, without the need for costly infrastructure overhauls.

A Model of Cross-Sector Collaboration for the Future of Smart Cities

The success of 5-SAFE is a testament to the power of collaboration. T-Systems’ expertise in 5G infrastructure, combined with Peregrine.ai’s AI innovations and academic support from Landshut University, has created a model of forward-thinking public-private partnership. This project underscores the potential of cross-sector collaboration to drive impactful change in urban safety.

“This partnership exemplifies how technology and collaboration can elevate urban safety to new heights,” said a project team member. “Together, we’ve created a system that’s not only transformative for school zones but has the potential to redefine safety in smart cities worldwide.”

The Future of 5-SAFE: Expanding AI Safety Solutions Across Urban Landscapes

While 5-SAFE is tailored for school zones, Peregrine.ai envisions a broader role for this technology within smart cities. With its hardware-agnostic, edge-based design, 5-SAFE is primed for applications in urban traffic flow optimization, pedestrian safety enhancement, and intelligent response systems. This AI-powered platform is built to evolve, enabling cities to adapt to emerging safety challenges without compromising privacy or agility.

“5-SAFE is more than a school zone safety tool—it’s a strategic blueprint for the next generation of urban infrastructure,” added Philip. “As cities look to create safer, more adaptive environments, Peregrine.ai stands ready to lead with intelligent, privacy-focused solutions that redefine what’s possible.”

About Peregrine.ai

Headquartered in Berlin, Peregrine.ai is at the forefront of AI-driven vision technology, delivering sophisticated solutions that blend real-time intelligence with unmatched privacy standards. By integrating edge computing and hardware-agnostic design, Peregrine.ai is enabling cities and fleets to achieve transformative safety outcomes and set new standards for urban innovation.

For further information on Peregrine.ai and the 5-SAFE system, please visit peregrine.ai/labs or contact:

Project / Partnerships Contact:
Philip Meier
Head of Project Delivery & Partnerships
Peregrine.ai
philip@peregrine.ai

The post Peregrine.ai and Partners Unveil 5-SAFE: An Advanced AI-Driven Traffic Safety System for Landshut’s School Zones appeared first on peregrine.ai.

Elevating Fleet Dashcams: The Next Generation of Commercial Fleet Management

Our advanced dashcam technology serves as the proactive eyes of commercial fleets, capturing detailed real-time video of the vehicle’s surroundings. These sophisticated cameras lay the groundwork for a system designed to significantly improve the safety of drivers, passengers, and pedestrians, while also providing fleet managers with the tools needed for effective fleet oversight.

Cutting-Edge Environmental Perception and Computer Vision

The cornerstone of our technology is its environmental perception, enabled by leading-edge computer vision algorithms. This allows our software to analyze dashcam footage, identifying and making sense of various driving scene elements. By equipping vehicles with the capability to accurately interpret their surroundings, we ensure unmatched real-time responsiveness, crucial for immediate risk assessment and decision-making.

IMU Data Fusion: Enhancing Accuracy at the Edge

Our integration of Inertial Measurement Units (IMUs) fine-tunes the environmental perception, providing detailed metrics on the vehicle’s movement through measurements of acceleration, rotation, and gravitational forces. This IMU data, combined with visual insights from the dashcams, processed at the edge, enhances our software’s ability to assess the vehicle’s behavior and interaction with its environment accurately.

AI-Driven Insights for Fleet Managers: Beyond Traditional Telematics

The standout feature of our solution is the cutting-edge environmental perception enabled at the edge of fleet dashcams, which significantly reduces false positive event detections and provides a permanent, real-time driving risk assessment. This advanced capability ensures that only genuine hazards and high-risk driving events trigger video recordings, enhancing the accuracy and relevance of the data provided to fleet managers. Rather than overwhelming operators with excessive information, our system smartly pinpoints critical incidents, streamlining the decision-making process. This focus on meaningful alerts, backed by the precision of our environmental perception technology, marks an advancement in managing and understanding fleet operations, ensuring that fleet managers are equipped with actionable insights to promote safer driving practices across their fleets.

Shaping Fleet Safety Technology with Advanced Driving Risk Assessment

Our solution goes beyond technology; it signals a new era of road safety. By equipping commercial fleets with the ability to understand and adapt to their surroundings, we enhance individual driver safety and elevate the safety standards of entire fleets. This advanced driving risk assessment tool gives fleet managers unparalleled insights, promoting safer driving practices and contributing to a significant reduction in accident rates.

In summary, our integration of fleet dashcams with state-of-the-art environmental perception, computer vision, and IMU data fusion—processed at the edge—marks a significant leap in fleet safety technology. Our system does more than just monitor; it comprehends the road, ensuring every journey is as secure as possible for all road users.

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Benefits for Telematics Service Providers

In the dynamic realm of Video Telematics, the integration of cloud-based Computer Vision solutions for fleet dashcams is a game-changer. This strategic combination is not just about technology; it’s about significantly reducing false positives and adding unparalleled value to the efficiency and reliability of fleet operations.

The foremost advantage lies in the precision of environmental perception. Cloud-based Computer Vision solutions for fleet dashcams can discern and interpret visual data with unprecedented accuracy. This sharpens the focus on real threats and crucial events, reducing the occurrence of false positives that can often plague traditional video telematics systems.

Cloud-based postprocessing plays a pivotal role in achieving this accuracy. By leveraging the immense processing power of the cloud, fleet dashcam videos undergo deep tech analysis, distinguishing between actual threats and benign events. This unparalleled decision-making capability minimizes false positives, ensuring that alerts and notifications for fleet managers are triggered only when genuine risks are identified.

Benefits for Fleet Operators

The impact on fleet management is profound. With reduced false positives, fleet operators can trust the alerts they receive, leading to quicker response times and more informed decision-making. This not only enhances overall safety but also streamlines operational efficiency by minimizing unnecessary interventions and disruptions.

Moreover, the cloud’s scalability ensures adaptability to varying workloads, optimizing the reduction of false positives without compromising system performance. Fleet dashcams, integrated with cloud-based solutions, become more responsive to nuanced driving scenarios, contributing to a safer and more reliable telematics ecosystem.

The financial implications are noteworthy as well. By minimizing false positives, the cost associated with unnecessary investigations, repairs, or maintenance can be significantly reduced. Fleet managers can allocate resources more effectively, focusing on genuine issues and proactive maintenance rather than reacting to false alarms.

In conclusion, the value added by cloud-based reduction of false positives for fleet dashcams with Computer Vision in Video Telematics is transformative. It goes beyond technology for the sake of innovation; it’s about creating a telematics ecosystem that enhances safety, optimizes operations, and delivers tangible economic benefits. As the synergy between cloud computing and Computer Vision continues to evolve, the future of Video Telematics promises not just advanced capabilities but a reliable and efficient solution that fleet operators can trust.

AI-powered Vision, for Smarter Cameras

As we explored the advantages of Peregrine.ai’s cutting-edge solutions in the realm of AI at the edge for camera devices, it’s essential to highlight the technology’s specific hardware requirements, notably a robust GPU for optimal performance. While we recognize the diverse range of well-established dashcams available, including reputable providers like Streamax, Lytx, Sensata, Teltonika, and Jimi, we can seamlessly integrate these devices with our EU-based cloud services to unlock a new dimension of fleet insights tailored to operators’ needs.

Our Computer Vision technology serves as a transformative layer, enhancing environmental perception, deciphering scene complexity, and fostering contextual awareness. This innovation allows us to filter through the amount of event videos generated by these dashcams, pinpointing those that truly contain relevant information for fleet operators. Furthermore, our commitment to GDPR compliance is unwavering, achieved through the application of anonymization algorithms. Faces and license plates are effectively blurred, ensuring data privacy and regulatory adherence.

We invite you to connect with us today and explore how Peregrine.ai can elevate event detection, optimizing the performance of your trusted dashcams in the market. Let’s engage in a conversation on tailoring solutions to meet the unique demands of your customers.

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Advantages of Video Telematics in Fleet Management

Enhanced Safety

Video telematics serves as a powerful tool for promoting driver safety. The recorded footage can be used to analyze driver behavior, identify risky practices, and provide targeted training to mitigate potential accidents. Real-time cabin alerts, triggered by object detection software, supports drivers in perceiving their environment and in taking actions to avoid incidents. These proactive approaches help reduce the frequency of collisions and enhance overall road safety.

Incident Investigation

In the event of an accident or dispute, video telematics becomes an invaluable asset. Accurate and time-stamped footage can be crucial in determining fault, streamlining the claims process, and protecting the fleet from fraudulent claims.

Operational Efficiency

By combining video footage with GPS and vehicle data, fleet managers gain valuable insights into routes, fuel efficiency, and overall operational performance. This information can be leveraged to optimize routes, reduce fuel consumption, and enhance overall fleet productivity.

Driver Training and Compliance

Video telematics allows for targeted driver training programs based on actual performance data. This personalized approach helps improve driver compliance with safety regulations and enhances their overall skills, contributing to a safer and more efficient fleet.

Concerns and Considerations

Privacy Concerns

The implementation of video telematics raises privacy considerations for drivers. Striking the right balance between monitoring for safety and respecting individual privacy is crucial. Establishing transparent communication and clear policies regarding data usage is essential to address these concerns.

Cost of Implementation

While the long-term benefits are significant, the initial cost of implementing video telematics systems can be a deterrent for some fleet operators. However, the potential savings from reduced accidents, improved fuel efficiency, and streamlined operations often outweigh the initial investment significantly.

Data Management Challenges

Managing and analyzing the vast amounts of data generated by video telematics systems can be a complex task. Fleet managers need robust systems and tools to efficiently process and derive actionable insights from the wealth of information collected.

The Role of AI at the Edge

AI at the edge holds immense promise in addressing concerns associated with video telematics. By leveraging artificial intelligence algorithms directly within the camera devices, privacy concerns can be mitigated through real-time video analysis. This edge computing approach allows for immediate identification of safety-related incidents without compromising individual privacy and by that, unlocks vision-based safety.

Moreover, AI at the edge streamlines data management by pre-processing information locally, reducing the burden on central systems. This not only enhances operational efficiency but also minimizes costs associated with data storage and transmission.

In conclusion, the integration of AI at the edge with video telematics heralds a new era for fleet management. As technology continues to advance, the seamless combination of video analytics and artificial intelligence at the edge promises to overcome existing challenges, making fleet operations safer, more efficient, and ultimately more sustainable.

AI-powered Vision, for Smarter Cameras

In the ever-evolving landscape of fleet management, Peregrine.ai stands out by bringing the transformative power of AI computing at the edge to life. This strategic choice not only aligns with our commitment to privacy but also amplifies the advantages of video telematics in real time.

Contextual Awareness for Vision-Based Safety

Our AI at the edge approach, rooted in contextual awareness, makes driving risk evaluation much more meaningful by adding real-time visual perception of traffic situations. This not only enhances safety measures but also empowers fleet managers with timely insights for proactive decision-making.

Data Storage and Bandwidth Optimization

Exemplified by Peregrine.ai, AI at the edge offers a game-changing advantage in data management. Edge processing minimizes latency, optimizes resource usage, and streamlines data transfer by conducting real-time analysis closer to the data source. This approach not only enhances operational efficiency but also addresses the challenges associated with centralized data processing, marking a significant step towards more agile and responsive fleet management solutions.

Privacy is Priority

Privacy is priority, and our dedication is reflected in our 100% GDPR-compliant approach to all video recordings. This ensures that the integration of video telematics into fleet management solutions is not just cutting-edge but also respectful of individual privacy rights.

Tailored Solutions

At Peregrine.ai, we recognize that every fleet is unique, and one size does not fit all. In line with this philosophy, we provide our customers with the freedom to tailor their video telematics solution according to their specific needs. Whether it’s implementing driver alerts, video triggers, or establishing a holistic driving behavior assessment, we offer a customizable framework, allowing our customers to shape their fleet management strategies in a way that best suits their operations. The power is in their hands to define and refine their fleet’s journey towards safety and efficiency.

In essence, Peregrine.ai’s integration of AI computing at the edge with video telematics not only propels fleet management into a new era with vision-based safety but also empowers our customers to actively participate in shaping a solution that aligns seamlessly with their unique operational requirements and priorities.

Outlook

During the next weeks, we will take you on a journey across many different features that are unlocked by processing AI at the edge. Stay tuned to read more about tailgating, stop sign violations, detection of vulnerable road users and many more…

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UBI policies will emerge as the driving force in reshaping the auto insurance sector. These policies usher in an era of more individualized and economically viable coverage choices for policyholders, thereby ensuring an elevated level of service quality.

It comes, however, with a significant element that cannot be ignored – the use of Computer Vision and AI generates amounts of data that are already hard to manage. Peregrine faces this challenge by identifying in real-time during the drive the events that are of interest to insurance providers. Only for these events, data will be uploaded and stored in the cloud. 

Adding a level of complexity to handling data, service providers are required to ensure that the data is stored securely and processed following data protection principles. Building a digital service which complies with the General Data Protection Regulation (GDPR) becomes indeed a challenge to the fast and coherent adoption of the next generation UBI in the EU. 

In the case of insurance policy, the use of vehicle cameras and dash cams as the main source of insights and the automatic evaluation through machine learning (ML) algorithms is novel in the sector. There are, in fact, two main aspects to focus on. 

First, insurance companies and their technology partners must obtain clear and informed consent from policyholders to collect and process their data. The policyholder is then contracting the technology partner to process and evaluate the data to be provided with an objective assessment of their drives, e.g. a driving score. 

Secondly, environmental data processed by the smart cameras installed in vehicles needs to be balanced. On the one hand, to be kept to a minimum: only information that is identified as absolutely necessary to provide the requested assessment may be processed and stored. On the other hand, all stored visual information with high relevance for the service offering must not contain any personal data. Given this market setting: How does Peregrine leverage AI in our technology development to ensure that the processing and storage of video data preserve the privacy of all road users at all times?

Peregrine offers the technology that does comply with GDPR requirements and, thus, solves the data privacy challenge. We do so by using next-generation ML algorithms to automatically identify all forms of car registration plates as well as any human from the front and the side – a visible in-car person, a walking pedestrian or even a cyclist. We then point out areas in each video frame that contain personal data and process these in a way that they will be irreversibly unrecognizable.

This is what we at Peregrine call anonymization. In particular, our anonymization solutions, once created for our own needs, have been proven so efficient and cost-effective that they are used today by automakers to fulfill their data privacy strategy. Our technology improves daily through the use of millions of data points. We name this approach “the data flywheel”, as the continuous inflow of new edge cases is making it highly-trained and consistently state-of-the-art. In this way, our camera-based data opens the door for fresh innovations in the insurance market, all without the burden of GDPR compliance concerns.

All in all, camera-based policies represent the future of the auto insurance industry, as they offer tailored, efficient, and cost-effective coverage options for policyholders. Prioritizing privacy and security in the data strategy is essential to add customer value and is at the heart of Peregrine’s work. 

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Mobility has been significantly enhanced by computer vision

One of the areas in which computer vision has been adopted rapidly is the mobility sector. Earlier vision systems were utilized for automated vehicle assembling. Ongoing technological advancements led to a wider scope in the industry to entail automotive driver systems and traffic management systems. 

Computer vision systems that enable autonomous driving and other mobility services and systems require a large amount of video data and are therefore accompanied by numerous privacy-related debates and court rulings, aiming to ensure a lawful handling of personal data. 

The algorithms which are used to detect the visual context of a vehicle’s surroundings are capable of more than automatically navigating through it. Within the last few years, the use of third-party dashcams in cars, motorcycles and bikes has increased. Visual data recorded during accidents and other prohibited traffic behavior can be admissible as court evidence. 

Data privacy and computer vision must go hand-in-hand

The EU Council of Ministers passed a general safety regulation that requires OEMs to build new cars with advanced safety systems. The mandatory safety systems include “warning of driver drowsiness and distraction, intelligent speed assistance, reversing safety with camera or sensors, and data recorder in case of an accident”. Cars and vans must also be equipped with lane-keeping assistance and advanced emergency braking. The commission expects that by 2038, 140,000 serious injuries will be avoided and 25,000 lives will be saved. 

To create such systems, it is inevitable to capture and process a wide variety of traffic situations as a representation of the reality in which vehicles operate. The indispensability of real data has been recognized and appreciated by lawmakers. The German Association of the Automotive Industry (VDA) and Germany’s federal and state data protection authorities have drafted a joint declaration to regulate the use of video data, derived from rides that are intended to collect data for scientific purposes and help to develop autonomous vehicles and increased safety systems.

The legitimate interest of companies to enhance driver assistance systems and autonomous driving serves as the legal basis for collecting and processing traffic video data. These developments are furthermore in public interest as they contribute to the increase of traffic safety. Of course, there are more regulations to be considered and taken care of, when collecting traffic data for the development of such systems.

The important message here is that regulators understand and appreciate the fact that the use of video data is a crucial factor for the future of mobility and transportation.

The collection of visual data is a great chance and a great challenge at the same time. Globally, private and public organizations have realized the importance of collecting visual data and the importance of collecting it in a privacy friendly way. Visual data is the fuel that is needed for technical solutions that are desirable for companies and governments alike: enhancing profitability, increasing safety and ecology, while ensuring public privacy protection.

At Peregrine we are committed to Vision Zero, while maintaining the highest standards of privacy by design.

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When it comes down to it, urban roads are primarily shared by a number of different road users. From mass and public transportation like busses and cabs, to trucks making deliveries, first responder vehicles, and of course, private automobiles, it’s clear to see just why accidents occur so frequently out on the road.

And even though COVID-19 has increased demand for alternative mobility concepts and delivery services, accident rates still remain quite high, especially taking into account pedestrians and cyclists. In fact, 9 out of every 10 accidents are caused by human driver error. And in the EU, 73% of accidents that cause life-changing injuries occur in cities and urban areas.

With that in mind, it’s clear that changes are in order.

The Future of Urban Driving

Commercial fleet managers and automotive insurers alike are looking for new approaches to the transportation of goods and delivery services to make our streets safer in cities across the EU. They seek visual context and a visual solution to help identify and understand the most high risk environments to prevent accidents on the road. Instead of using GPS-based telematics solutions, they’re turning to dashcam video and video analytics to gain greater insight into their routes and driving habits.

Intelligent video analytics could be the pathway forward. So far, research has shown that video analytics has the potential to help drivers avoid high risk events on the road, with approximately 80% of drivers improving their safe driving habits. Not to mention, video analytics can be used in driving instruction and teaching, ultimately customizing the software to meet the needs of drivers.

However, it’s important to remember that this type of technology isn’t just applicable to vehicles — it can be used on bikes, eScooters, and so much more. The potential uses for pedestrians is far reaching, and that’s why AI is poised to pave the way for the future of urban transportation.

AI-Equipped Mobility Solutions

Along with intelligent video analytics, commercial fleet managers and logistics companies are turning to AI-equipped smart dashcams to collect critical road data using computer vision and the latest breakthroughs in technology — also known as “Vision Zero.”

The Cities of Tomorrow

Along with these practical benefits for improving safety on the road, this type of technology also helps to paint a clearer image of the larger picture in terms of urban safety. Urban planning stands to benefit from the insights gained through these types of systems as they will allow planners and city designers to create safer roads and more effective infrastructure moving forward.

The Peregrine Difference

Here at Peregrine, we’re bringing the future of urban driving front and center. Our product offers ground-breaking video analytics through technologies like computer vision and AI to ensure that roads are safer for every road user in every city.

Peregrine’s fleet intelligence platform empowers fleets to score and train their professional drivers with technology at the root of their approach, so that we can pave the way for a safer city and a safer future.

With Peregrine — you can count less accidents, less cost, and fewer claims.

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In the wake of the COVID-19 pandemic, it’s clear to see that delivery services are being used by consumers more than ever before. From food, to packages, delivery services have quickly been thrust into the spotlight within the transportation and mobility industry. And with this new fame comes an increased market growth rate that is providing commercial fleets with new opportunities to scale, branch out, and take advantage of an expanded market.

With more delivery demand, there is an increased need for commercial fleets to provide extensive delivery services that continue to meet the needs and expectations of today’s consumers. With more trucks on the road, there also is an increased need for safety precautions that ensure the safety of drivers, pedestrians, and commercial fleet operators.

Remember, it’s not only commercial fleets that are feeling this — even car sharing, ride hailing, ride pooling, etc. are growing. How exactly can fleet operators ensure that they’re taking the right precautions?

Telematics & Fleet Management Software

To answer that question, most commercial fleets rely on their telematics service provider and their chosen fleet management software to keep their drivers and pedestrians safe. Their TSP will build upon things like GPS data, acceleration and deceleration values, and related insights to understand what’s going on during a delivery service. However, they don’t paint the whole picture.

More recently, commercial fleet operators have mandated the use of dash cams. And while these dash cams have been proven useful in cases that involve accidents and driver error, they still don’t provide commercial fleet operators with the detailed insights they need to really know how and why things are happening on the road.

The Peregrine Difference

Here at Peregrine, we’re providing commercial fleet operators with advanced, ground-breaking video analytics systems built on computer vision and AI technologies to make vehicle operations safer for all parties involved. Our system empowers fleets through a unique approach to score and train their professional drivers to ensure that our roads are safer than before — even when the demand for delivery service is at its highest.

We believe that the increasingly competitive environment for commercial fleets makes it necessary to have the very best tools at your disposal to make the most out of every ride. Through our state of the art AI-based solution, you can keep your fleet protected and ensure better business today, tomorrow, and well into the future.

Gain automated insights directly from our computer vision systems to help you better analyze your drivers’ decisions while on the road. From there, teach and train them, and equip them with the knowledge needed to pave the way for safer driving practices.

Remember, less accidents means less claims, less costs, and more satisfied customers.

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