9 questions to ask about an AI video safety solution
June 3, 2025
June 3, 2025
When evaluating AI-based video safety solutions for your fleet, asking the right questions can help you distinguish between basic and intelligent systems. Here's what to ask—and why the answers matter for your safety program and business outcomes.
Human review is error-prone, subjective, and takes time (usually a 24-hour turnaround). AI-based machine review is more accurate (assuming the AI model is good), objective, and immediate when paired with edge computing. Additionally, the more footage the AI analyzes, the more accurate the results.
Be aware of the difference between miles recorded vs miles analyzed. AI is only trained on miles analyzed, which in trigger-based systems is typically less than 15%. This means it will take much longer for such a system to get smarter.
Real-time analysis by AI and edge computing produces immediate alerts, which helps to avoid accidents and correct behavior in the moment. This is a much more effective manner of behavior improvement than reviewing results later.
How Netradyne answers this:
Netradyne's Driver•i platform captures and analyzes 100% of drive time, providing complete and immediate visibility into all driving events The AI gets exponentially smarter as it analyzes various scenarios across tens of billions of miles. Along with edge computing, results are immediate. Humans review videos only for QA purposes.
Inertial events refer to sudden vehicle movements such as hard braking, rapid acceleration, sharp turns, or impacts. These events are o en the primary triggers for video recording in safety systems.
Many critical driving behaviors don't produce the physical forces needed to register as inertial events, such as distracted driving, drowsiness, rolling through stop signs, running red lights, or failing to wear seatbelts. These behaviors represent major safety risks despite not triggering inertial sensors.
Continuous video analysis is essential for capturing the complete risk profile of a fleet. When systems analyze all driving time, they can detect subtle patterns of unsafe behavior that serve as early warning signs before incidents occur. This approach transforms safety technology from documenting crashes to actively preventing them by identifying precursor behaviors.
How Netradyne answers this:
Netradyne's AI-based object detection combined with 100% drive time analysis identifies high-risk behaviors without requiring an inertial trigger, such as distraction, drowsiness, traffic violations, and seatbelt non-compliance. It also uniquely recognizes safe driving habits, giving managers the complete picture.
Edge computing means processing data directly on the device installed in the vehicle, while cloud processing requires sending data to remote servers.
Edge computing enables immediate data processing without transmission delays or connectivity dependencies. When video is processed in the vehicle, the system can analyze driving behaviors instantaneously, providing real-time alerts and feedback. This immediacy is critical for preventing incidents rather than simply documenting them.
Cloud-based processing introduces latency as data must be transmitted over cellular networks, which are unreliable in remote areas or during network congestion. These delays o en miss the window to intervene in dangerous situations.
How Netradyne answers this:
Netradyne processes video at the edge—in the vehicle—enabling real-time alerts and immediate driver feedback. This eliminates critical delays that could mean the di erence between preventing a crash and simply documenting one a er it's too late. Alert data and video are also served to the manager portal in the cloud.
Processing delay directly affects a system's ability to prevent incidents rather than just record them. The time between when an event occurs and when it's analyzed determines whether feedback can influence driver behavior in the moment.
Real-time processing enables immediate in-cab alerts for critical safety events like distraction or following too closely. These instantaneous notifications give drivers the opportunity to correct behaviors before they result in incidents.
If human review is part of the workflow, there may be further delays, resulting in receipt of data 2-3 days a er the event. By the time events are analyzed and alerts generated, the teaching moment has already passed.
How Netradyne answers this:
Netradyne delivers immediate in-cab alerts for critical safety events while also notifying managers within minutes. This dual-level approach ensures both immediate correction and timely management oversight, creating a stronger connection between behavior and consequence. Additionally, the driver’s GreenZone Score adjusts dynamically, so drivers can access their score and event summary via app upon finishing their shift, along with suggestions for improvement.
Compliance statistics are more meaningful when they refer to both the numerator (instances of non-compliance) and the denominator (total opportunities for compliance).
When compliance calculations include only triggered events, just the numerator is captured, and this presents a skewed picture of driver behavior. Accuracy requires visibility into all instances where compliance was required—every stop sign encountered, for example.
Without this complete denominator, compliance rates become potentially misleading and a manager must wonder if the driver is actually safe or just lucky thus far. A driver who runs two stop signs might appear to have the same risk profile as one who runs two out of fifty stop signs encountered, despite the la er having a 96% compliance rate.
How Netradyne answers this:
Due to its AI object detection and analysis of 100% drive time, Netradyne assesses risk with a true numerator/denominator approach. If two drivers both run five stop signs, a basic system rates them equally. Netradyne sees that one encountered 10 signs (50% violation rate) while another encountered 100 (5% violation rate), providing fair assessment based on actual compliance rates.
The most effective fleet safety programs balance risk identification with positive recognition, rather than focusing exclusively on violations. A punitive program o en leads to resistance and disengagement.
Positive behavior recognition acknowledges professional skills, reinforces desired behaviors, builds driver buy-in, and establishes safety technology as a fair observer rather than a "gotcha" system. A system that uses vision-based AI can most effectively identify and reward good driving. This balanced approach improves driver engagement and retention.
How Netradyne answers this:
Netradyne's GreenZone® scoring system automatically rewards safe driving habits alongside addressing risky behaviors. Drivers receive points for safe behavior such as creating space for vehicles on the shoulder or merging, and “streaks” or consistency for safe speed, stop sign compliance, alertness, etc. This balanced approach builds driver buy-in and improves morale by showing appreciation for their skills.
AI effectiveness depends on repetition and adaptation. As road conditions, vehicles, and driving environments evolve, AI models must keep pace to maintain accuracy and continually improve. The update process directly impacts detection accuracy, false alert rates, and the system's ability to recognize new risk factors.
Static AI systems based on fixed rules become less effective over time as they fail to adapt to new scenarios. They typically require manual updates when new risks emerge, creating operational challenges and inconsistent coverage.
Continuous machine learning allows systems to improve through ongoing exposure to diverse driving scenarios. Each mile analyzed provides additional training data, helping the AI be er distinguish between normal variations and genuine risk factors.
How Netradyne answers this:
Because Netradyne's AI analyzes billions of miles of driving data, the rate of machine learning is exponentially faster than systems that are only recording. This improves detection accuracy and adaptation to new scenarios.
Sources range from basic inertial sensors and GPS to sophisticated computer vision systems that interpret the visual environment as a human driver would.
Inertial sensors detect physical movements like acceleration and braking but miss critical context about why these movements occurred. GPS and map data can add location context but o en lack real-time accuracy for temporary conditions like construction zones. Computer vision represents a significant advancement by actively interpreting the visual environment, identifying objects, reading signs, and understanding road markings. This enables detection of subtle compliance issues like rolling stops and traffic signal violations that don’t register on inertial sensors.
How Netradyne answers this:
Netradyne uses a combination of the above data sources including AI-based object detection that identifies and interprets objects like traffic signals, pedestrians, temporary speed signs, and construction zones. Rather than relying on map data, it interprets the driving environment as a human would, ensuring the system understands not just what happened but the context in which it occurred.
Many collisions involve a combination of factors that together create exponentially higher risk than any single factor alone.
When multiple such events occur simultaneously—such as distraction combined with following too closely—the resulting risk isn't merely additive but multiplicative.
The ability to detect these compound risk scenarios represents a significant advancement in predictive safety. Traditional systems that identify individual behaviors separately may miss this critical risk amplification.
Compound alert detection requires sophisticated AI capable of recognizing relationships between simultaneously occurring behaviors. This contextual awareness enables more accurate risk assessment and more targeted coaching by identifying the specific combinations that create the highest risk.
How Netradyne answers this:
Netradyne's AI detects combined risk factors—like phone use while rolling through stop signs or drowsiness while following too closely—automatically assigning appropriate severity levels without requiring human review. This enables targeted coaching focused on the most dangerous behavior combinations that frequently lead to crashes.
* Individual results and conditions may vary.
** Based on customer data.
† Netradyne must receive written approval to share client data.
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