Anyone working in the commercial vehicle sector will be all too aware of the increasing focus on AI-powered camera systems, with them now being seen as the default solution for fleet safety and compliance.
This is particularly stark in relation to compliance and standards such as the Direct Vision Standard’s Progressive Safety System, where operators are often told that AI detection is now essential.
However, it’s important to note that this is not strictly accurate. AI may be appropriate in certain operating environments, but compliance is determined by system performance and specification, not by whether equipment is described as “AI”.
Please note: References to DVS, FORS, CLOCS and Mission Zero requirements were correct to the best of our knowledge as of June 2026. Operators should always refer to the latest guidance published by the relevant organisation.
What Is a Standard Truck Camera System?
A conventional vehicle camera system typically provides a live video feed to an in-cab monitor and may also be integrated with DVR or MDVR recording equipment. The system does not analyse the video stream; instead, detection capability is achieved either through driver observation or external sensors where these have been integrated with the system.
When correctly specified and installed, such systems can form part of a compliant safety package under UK regulatory and accreditation schemes.
What Is an AI Camera System?
An AI-enabled vehicle camera incorporates onboard processing that analyses video in real time. Depending on configuration, it can:
- Detect pedestrians and cyclists
- Classify vulnerable road users
- Trigger audible or visual alerts automatically
- Record event-based incidents
Rather than relying solely on external sensors or driver interpretation, the system applies object recognition to the camera feed, providing an additional layer of detection capability in high-density urban environments.
Direct Vision Standard: What Is Actually Required?
Under the current Direct Vision Standard framework managed by TfL, HGVs over 12 tonnes must either achieve the required star rating or be fitted with a Progressive Safe System to operate within Greater London. The Progressive Safe System requires equipment capable of detecting vulnerable road users in defined blind spot areas and providing appropriate driver warnings.
Importantly, the specification focuses on functional performance; it does not mandate AI technology as a category.
AI-based visual detection systems are certainly an option, but compliance may also be achieved using standard camera systems combined with compliant proximity sensors and integrated detection and warning systems.
The critical issue is whether the installed system meets Transport for London’s technical performance requirements. Again, AI is one method of achieving this, but it is not the only method.
FORS, CLOCS and Mission Zero: Performance Over Product Type
FORS, CLOCS and Mission Zero similarly focus on effective risk management and demonstrable safety controls.
They require:
- Appropriate blind spot mitigation
- Effective warning systems
- Documented safety procedures
They do not prescribe AI detection specifically.
An AI system may support these objectives, particularly where fleets operate in higher-risk urban environments. However, a correctly specified and documented conventional system can also meet those same scheme requirements.
As with the DVS, compliance is determined by outcome and specification, not branding.
Key Technical Differences
| Feature | Standard Camera | AI Camera |
|---|---|---|
| Live video | Yes | Yes |
| Automatic VRU detection | No | Yes |
| Alert trigger | Sensor-based or driver-observed | Object recognition-based |
| Configuration complexity | Lower | Higher |
| Calibration requirements | Moderate | Higher |
| Upfront cost | Typically lower | Typically higher |
AI introduces automated visual detection. It also introduces additional configuration, calibration and environmental considerations.
When a Conventional System May Be Appropriate
A conventional camera and sensor combination may be appropriate where:
- Vehicles operate largely outside dense urban environments
- DVS compliance is achieved through sensor-based detection
- Exposure to vulnerable road users is limited
- Budget discipline is a primary consideration
- The primary objective is operational safety rather than compliance
- Stationary fixed obstacles are of greater risk than vulnerable road users
In any of these circumstances, a properly installed traditional system can be operationally effective and provide sufficient protection to both the vehicle and driver.
When AI May Be Suitable
AI systems may be suitable where:
- Vehicles operate extensively in London or similar dense urban environments
- Exposure to cyclists, pedestrians and other vulnerable road users is frequent
- Operators are seeking detection capability beyond minimum compliance
- Incident history indicates elevated vulnerable road user risk
In such cases, automated object recognition can provide a vital additional layer of warning.
Limitations and Operational Reality of AI
It is important to recognise that AI systems are not infallible.
Performance can be influenced by a number of factors, such as:
- Lighting and weather conditions
- Camera positioning
- Calibration accuracy
- Software configuration
- Alert sensitivity settings
False positives may occur if detection thresholds are overly aggressive. Excessive alerts can contribute to driver fatigue. Conversely, no detection technology removes the requirement for driver vigilance and appropriate training. Some AI systems are designed primarily to detect vulnerable road users and may therefore ignore certain stationary objects. Whilst this can help reduce unnecessary alerts, it can also create different operational considerations depending on the application.
AI should therefore be viewed as part of a broader safety strategy, not as a standalone solution.
Cost and Whole-Life Considerations
Compared to conventional camera systems, AI systems generally involve:
- Higher hardware costs
- Additional configuration time
- Potential software updates
- Increased diagnostic complexity
Whether this investment is justified depends on operational exposure and risk profile. For many fleets, the correct evaluation is not whether AI is “better”, but whether it is proportionate to the level of urban risk and compliance requirement, as well as the additional cost and ongoing maintenance.
Conclusion
AI truck cameras are not universally required for compliance with DVS, FORS, CLOCS or Mission Zero. Instead, what matters is whether the installed system meets the relevant technical and performance criteria.
AI introduces automated object detection, which may be appropriate in higher-risk urban environments and can provide an additional layer of warning beyond driver observation alone. Equally, conventional camera and sensor systems, when correctly specified and installed, can remain fully compliant and effective.
Ultimately, specification should be driven by operational risk and compliance requirements, not by terminology alone. As we’ve seen, AI may be one route to compliance, but it is not the only route.
