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MOIS AI Camera

Intelligent Forward Detection for Safer Moving Off

SCC’s MOIS AI Camera uses advanced image recognition to detect pedestrians, cyclists and other vulnerable road users directly in front of the vehicle. Designed for professional fleets, it forms a key part of a Moving Off Information System (MOIS) under UNECE Regulation 159 and supports compliance with DVS Progressive Safe System (PSS) requirements, helping drivers move off safely in urban or congested conditions.

  • AI-enabled forward-facing camera for use in MOIS applications, supporting DVS PSS compliance
  • Detects pedestrians and cyclists entering the area directly in front of the vehicle
  • Provides clear visual and audible alerts via SCC’s 7″ AI Monitor
  • Wide detection area with intelligent filtering to reduce false alerts
  • Durable, weather-sealed design for reliable fleet operation in all conditions
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AI-Powered Protection for Vulnerable Road Users

The MOIS AI Camera uses AI-based image detection to identify cyclists, pedestrians and other vulnerable road users directly ahead of the vehicle during low-speed or moving-off manoeuvres. When a potential hazard is detected, it issues a clear visual and audible warning through the in-cab AI Monitor, giving drivers the awareness needed to prevent accidents when moving off.

Integrated as part of SCC’s intelligent safety suite, the system supports MOIS applications and helps operators meet DVS Progressive Safe System requirements. Its adaptive algorithms minimise false alerts and ensure consistent performance in all lighting and weather conditions.

Built for demanding commercial environments, the MOIS AI Camera is fully compatible with SCC’s AI Monitor, as well as approved third-party hardware.

SCC Warranty


This product comes with 2 Years warranty. Please see our SCC Warranty section for more information.

Who is this for?

The MOIS AI Camera is ideal for fleets operating in urban or site-based environments where vulnerable road users may be present directly in front of the vehicle, and for those upgrading to meet DVS Progressive Safe System or other regulatory requirements.

Typical applications include:

  • Hauliers and logistics fleets operating HGVs in city environments
  • Construction and utility vehicles working on busy or enclosed sites
  • Municipal and service fleets operating in pedestrian-heavy areas
  • Operators implementing the DVS PSS upgrade to facilitate entry into London
  • Fleets seeking alignment with UNECE Regulation 159

What compliance does this help with?

SCC’s MOIS AI Camera supports compliance with the Direct Vision Standard (DVS) Progressive Safe System (PSS) and UNECE Regulation 159 (Moving Off Information System). By detecting pedestrians, cyclists and other vulnerable road users directly in front of the vehicle, it ensures operators meet the latest safety and visibility requirements for urban and on-site operation.

Working seamlessly with SCC’s 7″ AI Monitor, the MOIS AI Camera provides clear, real-time visual and audible alerts whenever a vulnerable road user is detected in the forward danger zone. Its adaptive AI filtering prevents false alarms from static objects, delivering precise, reliable performance that aligns with the DVS and MOIS compliance framework. Rugged and weather-sealed, it provides dependable safety assurance in all fleet operating conditions.

Specifications

Resolution: 720p or 1080p (system dependent)
IP Rating: IP69K (manufacturer rated)
Output: Analogue AV with alarm output
Connectors: 6-pin to 4-pin AV with alarm output
Power: 10V–32V DC
Operating Temperature: -20°C to +70°C
Viewing Angle: Up to 170°

What problems does it solve?

Starting or moving off in congested environments poses a serious safety risk for large vehicles. Pedestrians or cyclists positioned directly in front of the cab are often completely hidden from view, leaving even experienced drivers vulnerable to collisions and compliance breaches. Traditional camera systems can improve visibility, but they rely on constant driver monitoring and cannot actively detect or alert against immediate hazards.

SCC’s MOIS AI Camera eliminates this risk by using intelligent image recognition to detect moving pedestrians and cyclists directly ahead of the vehicle. When an object is identified within the defined detection area, it issues instant visual and audible warnings through the in-cab AI Monitor, giving drivers critical awareness before moving off. By focusing only on moving objects, the system avoids false triggers from street furniture or static obstacles, ensuring accurate and timely alerts without distraction.

Built for demanding fleet environments, the MOIS AI Camera combines advanced detection with everyday reliability, helping operators prevent front-blind-spot incidents and maintain compliance with evolving safety standards.

Get a Quick Price Estimate

Wondering how much it will cost to equip your vehicles with SCC Advanced Fleet Safety equipment?

Our Price Estimator makes it easy to estimate costs based on the type and number of vehicles in your fleet. Whether you’re retrofitting a few rigid trucks, or rolling out a full upgrade across articulated lorries, you’ll get a tailored ballpark figure in seconds — no sales call required.

Got Questions?

We know fitting safety equipment to your vehicles isn’t always straightforward – especially when it comes to compatibility, installation and compliance. Below are answers to some of the most common questions we get about this product. Click to reveal the answers.

What Should Fleet Operators Look For In An MDVR System?

The Short Answer

The best MDVR (Mobile Digital Video Recorder) system is not simply the one with the highest specification or the greatest number of features.

Fleet operators should look for a system that provides reliable recording, delivers clear evidence when it is needed, integrates with other vehicle safety technologies and remains simple for drivers and fleet managers to use every day.

A well-chosen MDVR should improve safety, support incident investigations and continue performing consistently throughout the vehicle’s working life.

Although many MDVR units look very similar, their capabilities can vary considerably. Choosing the wrong specification can prove costly when multiplied across an entire fleet, making it important to understand both current operational requirements and any future expansion plans before making a decision.

Choose The Right Number Of Camera Channels

One of the first considerations is how many cameras your vehicles need, both today and in the future.

Commercial vehicle MDVR systems are commonly available as four-channel, eight-channel and sixteen-channel units. An eight-channel recorder is the most common choice for larger commercial vehicles, providing sufficient capacity to record multiple camera views simultaneously whilst still allowing room for future expansion.

When assessing camera capacity, think beyond your current installation. A vehicle fitted with four cameras today may require additional cameras later as operational requirements change or new safety technologies are introduced.

If your fleet already uses a mixture of standard-definition (SD) and high-definition (HD) cameras, it is also worth checking that the MDVR can record different resolutions across separate channels. This can simplify future upgrades and avoid replacing otherwise serviceable equipment.

Reliable Recording Is More Important Than Resolution Alone

Higher-resolution cameras can produce clearer images, but image quality alone should not determine which MDVR system to choose.

The positioning of cameras, the consistency of recording and the ability to capture usable footage in day and night conditions often have a greater impact than resolution alone.

A well-designed multi-camera system frequently provides more practical value than a higher-resolution system with poor overall coverage.

Consider How Much Footage You Need To Store

Recording capacity affects how long footage can be retained before older recordings are overwritten.

Most MDVR systems support one or more storage media, including SD cards, hard disk drives (HDDs) and solid-state drives (SSDs), with storage capacities typically ranging from around 500GB to 2TB.

The amount of footage stored depends on several factors, including:

  • the number of cameras installed
  • camera resolution
  • recording quality
  • frame rate
  • vehicle operating hours

For example, a 1TB SSD may provide approximately four weeks of recordings for a vehicle operating around 40 hours per week with four standard-definition cameras. Higher-resolution cameras or additional recording channels will naturally reduce overall storage time.

Consider How Footage Will Be Retrieved

Capturing footage is only part of the process. Fleet operators should also consider how quickly recordings can be accessed following an incident.

Depending on operational requirements, footage may be retrieved using:

  • removable storage
  • Wi-Fi
  • 4G or 5G connectivity
  • cloud-connected platforms

Not every fleet requires live remote viewing. However, operators managing large fleets or vehicles working across multiple locations may benefit from being able to access recordings remotely without waiting for vehicles to return to base.

Fleet operators should also consider the ongoing costs. Remote access typically requires both a SIM card and a software platform, with some providers charging additional subscription fees for cloud-based services or proprietary management platforms.

Make Sure Critical Footage Is Not Lost

Most MDVR systems continuously record, automatically overwriting the oldest footage as storage fills.

Many systems can also protect important recordings by automatically saving footage when an event occurs, such as harsh braking, an impact or another configured trigger. Some systems also allow drivers to manually protect recordings when required.

These features can make incident investigations quicker by ensuring important footage is not accidentally overwritten before it is reviewed.

Integration Can Improve Overall Fleet Safety

Modern MDVR systems often work alongside other vehicle safety technologies. For example, recordings may be linked with:

  • visibility camera systems
  • ultrasonic sensor systems
  • radar detection systems
  • AI pedestrian detection
  • vehicle telematics
  • GPS location data

Integrating these technologies can provide additional context during incident investigations whilst reducing the need for multiple independent systems.

Ongoing Software Support Matters

An MDVR is likely to remain in service for many years, so it is worth considering the level of software support available after installation.

Firmware updates may improve system performance, introduce new functionality or maintain compatibility as vehicle technology and fleet requirements continue to evolve.

Fleet operators should also consider the level of technical support provided by the supplier, how software updates are delivered and whether the platform has a clear route for future development.

Where remote viewing or cloud connectivity is used, it is also worth understanding how user access is managed and how ongoing software updates are maintained.

Choosing a well-supported system can help maximise the life of the investment whilst reducing the likelihood of compatibility issues in the future.

Ease Of Use Matters

The most advanced system offers little value if it is difficult to operate.

Drivers should not need to remember complicated procedures before every journey, and fleet managers should be able to retrieve footage quickly without unnecessary delays.

Simple operation encourages consistent use and reduces the likelihood of important recordings being missed.

Reliability Is Essential In Commercial Environments

Commercial vehicles operate in demanding conditions.

Vibration, temperature changes, moisture and long operating hours all place significant demands on electronic equipment.

Fleet operators should therefore look for systems designed specifically for commercial vehicle use rather than products intended for lighter-duty applications.

Long-term reliability often delivers greater value than selecting equipment based solely on initial purchase price.

Think Beyond Today’s Requirements

Fleet requirements rarely remain static.

Vehicles may change roles, legislation may evolve and additional safety technologies may be introduced over time.

Choosing an MDVR system with sufficient recording capacity, camera channels and integration capability can reduce replacement costs and simplify future upgrades as operational requirements develop.

What Fleet Operators Should Consider

When comparing MDVR systems, ask:

  • How many cameras do our vehicles require today?
  • Will we need to add more cameras in the future?
  • Can the recorder support different camera resolutions if required?
  • How much footage do we need to retain?
  • How will recordings be retrieved after an incident?
  • Do we need remote viewing, and what are the ongoing costs?
  • Does the system integrate with our existing safety technologies?
  • Is it simple for drivers and fleet managers to use?
  • Is it designed for long-term commercial vehicle operation?
  • What ongoing support, software updates and warranty are available?

Selecting the right MDVR is about far more than recording video. It is about choosing a system that supports safe, efficient fleet operation for many years.

Final Thoughts

An MDVR is one of the most valuable sources of information available following an incident, but its effectiveness depends on choosing the right system for the application.

Rather than focusing on specifications alone, fleet operators should consider reliability, storage capacity, ease of use, future flexibility, integration and long-term performance.

The best MDVR systems are those that deliver dependable recording every day whilst fitting naturally into a fleet’s wider safety strategy.

How Many Cameras Does A Commercial Vehicle Need?

The Short Answer

There is no single answer to how many cameras a commercial vehicle needs. Whilst many vehicles are fitted with four cameras as a typical starting point, the right number ultimately depends on the vehicle, how it is used and the visibility challenges it faces.

The aim is not to install as many cameras as possible, but to ensure drivers have the visibility and recorded footage they need to operate safely, confidently and efficiently.

Is There A Standard Number Of Cameras?

Many people assume there is a standard number of cameras for every commercial vehicle, but this is not necessarily the case.

A typical multi-camera system may include:

  • one forward-facing recording camera
  • one rear visibility camera
  • one nearside visibility camera
  • one offside visibility camera

This four-camera configuration provides broad coverage for many commercial vehicle applications and is commonly used across a wide range of fleets.

However, it should not be viewed as a universal standard. Some vehicles may require fewer cameras, whilst others benefit from additional coverage depending on their operation.

Commercial Vehicle Cameras Can Perform Different Roles

Not every camera fitted to a commercial vehicle serves the same purpose.

Some cameras are designed primarily to improve the driver’s visibility during manoeuvres, helping monitor blind spots or areas that are difficult to see using mirrors alone.

Others are fitted primarily to record events, providing valuable footage in the event of a collision, to support insurance claims or for driver training.

Increasingly, many camera systems perform both functions, providing live visibility whilst simultaneously recording footage.

This means the number of cameras fitted to a vehicle is not always a direct indication of how many blind spots are being monitored.

Why There Is No Standard Number

Commercial vehicles vary enormously.

A rigid refuse collection vehicle operating in busy residential streets faces very different operational challenges from an articulated lorry spending most of its time on motorways. Likewise, a tipper working on construction sites will have different requirements to a delivery vehicle making frequent urban drops.

For this reason, camera systems are typically designed around the operational risks of each vehicle rather than a fixed number of cameras.

What Influences The Number Of Cameras?

Several factors help determine how many cameras may be appropriate.

Vehicle Size And Layout

Larger vehicles naturally create larger blind spots.

Longer vehicles, higher cabs, trailers and specialist bodywork can all restrict the driver’s view and create areas that are difficult to see using mirrors alone.

Additional visibility cameras can help monitor these areas where appropriate.

Operating Environment

Where a vehicle operates is often just as important as the vehicle itself. Different operating environments create different visibility challenges. These may include:

  • urban deliveries
  • construction sites
  • waste collection
  • distribution centres
  • ports
  • public highways

Each presents different visibility challenges, meaning the most suitable camera configuration may vary considerably.

Manoeuvring Requirements

Vehicles that regularly reverse, turn in confined spaces or operate around pedestrians, cyclists or other vulnerable road users often benefit from additional visibility cameras.

Rather than relying on a single rear camera, operators may choose to monitor several areas around the vehicle simultaneously.

 

Legislation And Industry Standards

In some cases, the number and positioning of cameras is influenced by legislation, industry standards or customer requirements.

For example, vehicles operating in London under the Progressive Safe System (PSS) require:

  • a Moving Off Information System (MOIS) to detect vulnerable road users in front of the vehicle
  • a Blind Spot Information System (BSIS) to monitor the nearside blind spot

Similarly, industry standards and accreditation schemes such as CLOCS and FORS may require vehicles to meet specific visibility requirements. These commonly include a nearside camera and, where practical, a rear camera, alongside other safety technologies.

Fleet operators should always ensure their camera systems meet any relevant customer, contractual or regulatory requirements.

More Cameras Are Not Always Better

Installing additional cameras does not automatically improve safety. Drivers must still be able to interpret the information presented to them without becoming distracted or overloaded.

For many fleets, the objective is to provide clear, relevant visibility and recorded footage that supports safe operation, rather than simply fitting the highest number of cameras possible.

This is why camera positioning, monitor layout and overall system design are just as important as the number of cameras installed.

What About Driver-Facing Cameras?

Some fleets also choose to install driver-facing cameras.

These can support driver training, incident investigation and fleet management, but they also introduce additional responsibilities around privacy and data protection.

Where driver-facing or continuously recording camera systems are used, operators should ensure they comply with applicable data protection legislation and implement appropriate privacy measures, including carrying out a Data Protection Impact Assessment (DPIA) where required.

Choosing The Right Camera System

When assessing camera requirements, it is worth considering questions such as:

  • Where are the vehicle’s largest blind spots?
  • Is the priority improved visibility, incident recording or both?
  • How is the vehicle used throughout the day?
  • Does it regularly reverse or manoeuvre in confined spaces?
  • Does it operate around pedestrians or cyclists?
  • Are there any customer, insurance or legislative requirements?
  • Will trailers or specialist equipment be changed regularly?

Answering these questions usually provides a clearer indication of the most appropriate camera configuration than simply asking how many cameras a vehicle should have.

Final Thoughts

There is no ideal number of cameras for every commercial vehicle.

Whilst a four-camera system provides an effective solution for many vehicles, the most appropriate configuration depends on the vehicle, its operating environment and the role each camera is expected to perform.

Rather than aiming to install as many cameras as possible, the focus should be on providing drivers with the visibility and recorded footage they need to operate safely and confidently.

Do AI Truck Cameras Reduce Accidents?

AI-powered truck cameras are often promoted as accident-reduction technology, particularly in relation to cyclist and pedestrian detection. But do they actually reduce collisions?

Whilst they can certainly contribute to risk reduction in certain operating environments, it’s important to recognise that they do not guarantee it.

Ultimately, the more useful question is not whether AI cameras reduce accidents in every situation, but under what circumstances they are most likely to deliver meaningful safety benefits.

What AI Cameras Change

AI systems analyse video in real time and can detect vulnerable road users automatically. When detection criteria are met, they trigger audible or visual alerts to the driver.

This increases the likelihood that a hazard is identified earlier than it might be through mirrors or standard camera observation alone. However, detection capability is not the same as accident prevention.

Why AI Does Not Automatically Prevent Collisions

An alert only reduces risk if:

  • The system detects accurately
  • The alert is clear
  • The driver reacts appropriately
  • The vehicle can physically avoid the hazard

AI doesn’t control the vehicle. It doesn’t override driver input and it doesn’t eliminate blind spots. It provides an additional warning layer for drivers to react to.

It’s worth remembering that excessive nuisance alerts can also reduce effectiveness. If drivers receive frequent warnings that do not correspond to genuine hazards, confidence in the system may decline. This is why detection quality, calibration and alert management remain important considerations.

Where AI May Have the Greatest Impact

AI detection is most likely to contribute to collision reduction where:

  • Vehicles operate extensively in dense urban areas
  • Exposure to cyclists and pedestrians is high
  • Drivers are trained to respond correctly to alerts
  • Systems are correctly installed and calibrated

In these environments, AI detection is more likely to contribute to reductions in certain low-speed urban incident categories.

AI Works Best as Part of a Wider Safety Strategy

AI cameras are most effective when combined with driver training, appropriate warning systems, regular maintenance and other vehicle safety technologies. In many cases, the greatest safety benefits come not from a single technology, but from multiple systems working together to support driver awareness and decision-making.

This is one reason why many fleets continue to deploy layered safety solutions that combine cameras, sensors, mirrors and driver training programmes rather than relying on a single technology alone.

Where Expectations Should Be Realistic

AI systems do not:

  • Prevent motorway collisions
  • Replace disciplined driver behaviour
  • Remove the need for proper mirror setup and sensors

Technology supports risk management; it does not replace it.

Even the most accurate detection systems in the world cannot eliminate every risk. Drivers may still fail to respond to warnings, hazards may develop too quickly for avoidance action, and environmental conditions can affect system performance. As with any safety technology, real-world effectiveness depends on how the system is integrated into wider fleet operations.

Measuring Safety Improvements

Determining whether a safety system has reduced accidents is not always straightforward.

Many of the potential benefits of AI systems occur before an accident takes place. For example, a driver may receive an earlier warning, become aware of a cyclist or pedestrian, and take avoiding action before a collision occurs.

In these situations, no accident is recorded and no incident may ever be formally reported. However, the system may still have contributed to a safer outcome.

This is one reason why fleets often assess effectiveness using a combination of:

  • Incident data
  • Near-miss reporting
  • Driver feedback
  • Insurance claims trends
  • Video review and event analysis

Serious collisions are relatively infrequent events, which can make it difficult to attribute changes to a single technology.

AI camera systems may contribute to earlier hazard recognition and improved driver awareness, particularly in urban environments where interactions with cyclists and pedestrians are more frequent. However, outcomes will still depend on factors such as driver behaviour, installation quality, maintenance and wider fleet safety practices.

The Commercial Reality

For fleets with high urban exposure or a history of vulnerable road user incidents, AI detection may represent proportionate risk mitigation.

For some fleets, the decision to adopt AI may also be influenced by wider safety and compliance objectives. AI-based detection systems can support schemes such as DVS, FORS, CLOCS and Mission Zero, although they are not the only means of achieving compliance. As discussed in our article Do You Need AI for DVS, FORS, CLOCS and Mission Zero Compliance?, technology selection should be driven by operational requirements rather than compliance assumptions.

For others, a well-specified conventional camera and sensor system may deliver the required safety, compliance and operational performance without the additional cost and complexity.

Conclusion

AI truck cameras can contribute to accident reduction, particularly in high-risk urban environments, but they are not a standalone solution. Collision reduction depends on driver behaviour, installation quality, operational exposure and wider safety management.

AI is a tool within a broader fleet safety strategy, not a guarantee of accident prevention.

How Accurate Are AI Truck Cameras?

False Alerts, Calibration and Real-World Performance

AI-powered truck cameras are designed to detect pedestrians, cyclists and other potential hazards automatically, and are often marketed as highly accurate detection systems. But how accurate are they in real-world fleet operations?

The answer is not as simple as quoting a detection percentage. Real-world performance depends on a combination of installation quality, calibration, operating environment and system configuration. In many cases, the difference between excellent performance and disappointing performance has less to do with the AI itself and more to do with how the system has been deployed and maintained.

What “Accuracy” Means in Practice

When discussing AI camera accuracy, there are three key measures:

  • Detection rate: how reliably the system identifies relevant hazards
  • False positive rate: how often the system triggers alerts unnecessarily
  • False negative rate: how often genuine hazards are not detected

A system can detect hazards frequently but still generate excessive nuisance alerts. Equally, a system that generates very few alerts may risk missing genuine hazards. True effectiveness therefore lies in balancing detection reliability with manageable alert frequency whilst minimising missed detections.

What Influences AI Camera Accuracy?

1. Installation Positioning

Camera angle, height and field of view significantly affect detection performance. Poor positioning can:

  • Reduce detection coverage
  • Increase missed detections
  • Trigger alerts outside intended zones

Accurate installation is critical to system performance.

2. Calibration and Configuration

AI systems require appropriate calibration to define detection zones and alert thresholds.

If sensitivity is set too high:

  • False positives increase
  • Drivers may become desensitised to warnings

If sensitivity is set too low:

  • False negatives increase
  • Genuine hazards may not trigger warnings

Correct configuration is therefore as important as hardware selection.

3. Environmental Conditions

Lighting, weather and road conditions influence performance.

Heavy rain, glare, low sun angles or obscured lenses can affect image clarity. Although modern systems are designed to compensate for varying conditions, no camera-based technology is immune to environmental limitations.

4. Operating Environment

Urban environments with high pedestrian density increase detection complexity. The system must distinguish relevant hazards from background movement.

In rural environments, detection demands may be lower but lighting variability can still affect performance.

False Positives

A false positive occurs when the system generates an alert despite there being no genuine hazard requiring driver action.

Typical triggers include:

  • Pedestrians on pavements outside risk zones
  • Stationary objects misclassified as hazards
  • Roadside furniture within detection areas

Well-configured systems minimise these occurrences, but excessive false positives can lead to driver frustration, and driver confidence in the system is essential. If alerts are perceived as unreliable, drivers may begin to ignore them.

In some cases, stationary road furniture may be intentionally filtered out by the AI system to minimise false positives. This can improve usability but also demonstrates why detection performance should always be assessed in the context of the intended application rather than assuming the system is designed to detect every object it can see.

False Negatives

A false negative occurs when a genuine hazard is not detected by the system.

Potential causes include:

  • Poor camera positioning
  • Incorrect calibration or configuration
  • Obscured camera lenses
  • Adverse weather or lighting conditions
  • Complex operating environments

Whilst modern AI systems are designed to minimise missed detections, no camera-based technology can guarantee perfect performance in every situation. For this reason, AI should be viewed as an additional layer of detection rather than a replacement for driver observation and safe driving practices.

Can AI Cameras Replace Proximity Sensors?

AI cameras provide visual detection through object recognition, whereas proximity sensors operate using different physical principles.

One important difference is how information is presented to the driver. Camera systems primarily provide visual information, whilst proximity sensors typically generate an audible warning when a hazard is detected. This means a sensor alert can still attract the driver’s attention even if they are not actively looking at a monitor at that moment.

In some installations, combining both technologies can provide layered detection, with AI cameras offering visual identification and sensors providing an additional warning mechanism. However, the effectiveness of any configuration depends on proper integration and tuning.

How Fleets Should Evaluate Accuracy

When assessing AI camera performance, fleets should consider:

  • Real-world alert frequency
  • Driver feedback
  • Missed detection incidents
  • Ongoing maintenance requirements

Short demonstrations may not reflect long-term operational performance. Evaluation should be based on sustained use.

The Practical Reality

Modern AI truck camera systems can achieve high levels of detection accuracy when:

  • Installed correctly
  • Properly calibrated
  • Maintained appropriately
  • Matched to the operating environment

However, accuracy should not be viewed as a fixed number. Performance can vary depending on operating conditions, vehicle configuration and ongoing maintenance. The most effective systems are not necessarily those claiming the highest headline accuracy figures, but those capable of delivering consistent and dependable performance throughout their operational life.

Conclusion

AI truck cameras can provide reliable detection capability in commercial fleet applications. Their real-world accuracy depends heavily on installation quality, calibration and operating conditions.

Whilst modern AI systems can achieve high levels of detection performance, no technology is infallible. Accuracy should therefore be evaluated in the context of long-term fleet operation rather than short demonstrations or headline marketing claims.

When specified, installed and maintained correctly, AI cameras can form a valuable part of a broader fleet safety strategy and provide dependable support for drivers operating in complex environments.

How does the system avoid false alerts from static objects?

Unlike traditional cameras, the MOIS AI Camera uses intelligent image recognition to focus on moving pedestrians and cyclists. Static objects such as street furniture or parked vehicles are filtered out, ensuring drivers receive accurate, meaningful alerts without unnecessary distraction.

How does the MOIS AI Camera help with DVS and UNECE compliance?

The MOIS AI Camera supports compliance with the Direct Vision Standard Progressive Safe System and UNECE Regulation 159. By actively detecting vulnerable road users in the forward danger zone and issuing visual and audible alerts, it meets the requirements for a Moving Off Information System without relying solely on driver observation.

What does the MOIS AI Camera detect and when does it activate?

The MOIS AI Camera detects pedestrians, cyclists, and other vulnerable road users positioned directly in front of the vehicle. It is designed to operate during low-speed and moving-off situations, providing alerts at the moment risk is highest, such as when pulling away in traffic or on busy sites.

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