On the road, everyone knows what it looks like when safety fails: accidents, costly insurance claims, injuries, and, all too often in the case of commercial vehicles, severe or even fatal outcomes. Nearly anyone who’s navigated busy roads has felt that moment of unease.
Does the driver see me? Are they alert? Distracted? Fighting fatigue?
Fleets recognize the importance of safe driving, but have found it difficult to measure the ROI of safety programs with legacy crash-recording solutions. Conventional fleet safety platforms are seen as large cost centers—necessary for compliance and liability, but difficult to rationalize.
Netradyne’s AI-first platform is changing that.
By combining edge-based cameras with real-time behavior intelligence, Netradyne goes beyond passive recording to transform safety from a reactive, manual process into a proactive, automated one. Its scalable safety solution interprets, alerts, and enables in-the-moment audio coaching. For the first time, fleets can measure what safe driving looks like and build coaching programs that reduce the behaviors that cause accidents.
The evolution of fleet safety
Commercial fleets have long been a backbone of the modern economy, moving goods from cities, ports, and factories to even the most remote destinations. In the U.S. alone, commercial fleets carry roughly 73% of all domestic freight by weight, making this one of the nation’s most critical industries.1
While many trends have reshaped the industry over the decades, safety has remained a central priority since the beginning. In the earliest days of fleet operations (well before dashcams or telematics) safety was enforced manually. Drivers logged hours on paper, and post-incident investigations relied heavily on eyewitness accounts. In collisions, it often came down to the driver’s word against someone else’s, with little objective evidence to resolve disputes.
That began to change in the late 1990s and early 2000s, when the first wave of dashcams entered the market. These systems featured hardwired, forward-facing cameras that stored video locally on tapes, CDs, or SD cards. Footage had to be manually retrieved and reviewed, and events were typically only recorded during harsh braking or collisions. The primary goal was straightforward: exonerate drivers by proving they weren’t at fault.
By the early 2010s, the landscape began to shift. The maturation of cloud infrastructure, widespread adoption of telematics, and the rise of IoT connectivity brought fleets into a new era. Cameras could now stream footage to the cloud in near real time, and onboard sensors triggered automatic uploads for incidents like hard braking, aggressive turns, or sudden acceleration. This marked the beginning of the second generation of safety systems, which made it possible for managers to review events from remote and coach drivers more quickly. This was a critical turning point. Safety started to evolve from a passive, forensic tool into a more proactive operational function.
Second-generation platforms have fallen behind
While second-generation safety platforms benefited from the rise of cloud-based video review and the adoption of telematics, they also introduced a new set of limitations. The promise of better visibility and faster coaching is compelling, but, in practice, these systems are still relatively manual.
Fleets frequently struggle to convert driving data into measurable behavior change. Human review is necessary to parse through the vast volume of footage and alerts, delaying the process and often forcing teams to add headcount just to keep up. As a result, safety interventions are labor-intensive and difficult to scale, turning what was meant to be a modern solution into an even larger cost center.
Many companies using second-gen systems struggle with:
1. False positives and alert fatigue
Most second-gen systems rely on G-force sensors and basic motion triggers to detect risky behavior, generating thousands of alerts for minor or irrelevant events. Safety managers must sift through the noise, making it difficult to identify truly risky behavior or build effective coaching programs. And drivers (often rightly) dispute the accuracy of the footage during coaching sessions, which can erode the trust in the system over time. Companies like Lytx have responded by building large internal review teams to manually validate flagged events, but this adds cost, latency, and typically doesn’t scale well.
2. Coaching bottlenecks
Even with cloud access, reviewing and tagging incidents on second-gen platforms remains a manual process. Safety teams can spend hours combing through videos to find coachable moments. Without automation or high-precision filtering, this means they often spend more time reviewing video than actually coaching drivers. This is both inefficient and unsustainable, especially for large fleets.
3. Unclear ROI
With second-gen platforms incidents are typically reviewed long after they occur, so coaching may not be directly tied to the event. Drivers often receive delayed feedback that lacks context, which makes it more difficult to support behavior change. This disconnect also makes it more difficult to correlate coaching with real-world impact. Fleets invest heavily in hardware and software without clear improvements in driver performance, accidents, or insurance costs, so many struggle to justify the spend.
Additionally, litigators have shifted their focus from crashes to the broader safety culture of fleets, including driver engagement, preparedness, and the impact of behavioral change programs. In the event of a crash, companies using second-gen platforms can struggle to demonstrate the impact of their safety program.
Netradyne’s edge: Same starting point, different vision
Netradyne’s approach has been fundamentally different from the start. Co-founders Avneesh Agrawal and David Julian originally set out to build an autonomous trucking company, but quickly realized autonomy was many years away, limited by insufficient data and deep technical complexity. They pivoted to a challenge that was just as critical and far more immediate: improving human driver behavior at scale through AI.
From day one, Netradyne was built with AI at the core. The team focused on developing edge-based computer vision capable of processing 100% of driving time to eliminate the need for G-force triggers or manual video review. Their detection models help reduce false positives, deliver real-time in-cab alerts, and surface the events that truly matter. All of this data is also captured and organized in a way that enables safety managers to coach more effectively and influence behavior where it counts.
One of the most impactful features of the Netradyne platform is the GreenZone Score: a dynamic, behavior-based performance metric that helps fleets coach more effectively and reward safe driving. Unlike traditional systems that only penalize violations, GreenZone tracks both positive and negative behavior to deliver a more comprehensive picture of driver performance. Fleets can use it to benchmark performance, personalize coaching, and tie incentives to behavior, building a culture of accountability and continuous improvement.
Netradyne’s GreenZone Score directly links driver behavior to safety outcomes. The data shows that higher scores translate into fewer accidents: a 50-point increase in GreenZone Score corresponds to a 12–15% reduction in collision rates.2 This in turn lowers insurance claims and liability costs. For fleets, this provides a clear and measurable way to connect safety improvements to financial impact.
To date, Netradyne has analyzed over 25 billion miles of high-resolution commercial driving data and detected more than 2 billion safety-related events.2 This growing dataset fuels a flywheel, enhancing the accuracy of the platform’s AI models with every mile. Netradyne is now trusted by some of the most safety-conscious fleets in the world.
