# How Trucking Companies Should Review AI Dashcam Alerts

> An AI dashcam only flags. Whether it makes your trucks safer or pushes your drivers out comes down to who looks at each flag, how soon, and what they can see that the camera can't.

By Vlad Shvets, Partner @ Qvery · Published 2026-11-09 · 12 min read
Canonical: https://qvery.ai/blog/ai-dashcams/

You put AI dashcams in the trucks for the crash record and for the insurer. What came with them is a queue.

The cameras save a short clip whenever their software thinks something risky happened (the industry calls each one an event), the trucks produce events every day, and someone in your office has to decide what each one means.

The camera is often wrong about the road, and drivers on Netradyne, Samsara and Lytx cameras describe the same misreads:

**What the event says, and what the road-facing clip shows (false reads drivers report)**

| The event says | The road-facing clip shows |
|---|---|
| Speeding | The truck at the posted 65; the camera read an access road's 30 sign |
| Ran a red light | A flagger (the worker in a vest who waves traffic through a work zone) waving the truck on |
| Following too closely | A car that cut in from the next lane |
| Drowsy driver | A glance down at the speedometer |
| No seatbelt | A coat over the belt |

Each of those is a question someone has to answer, and the answer decides whether a good driver gets a call about nothing.

Drivers say those calls are what make them leave, and a driver who leaves is expensive: a study for the Truckload Carriers Association put the full cost of replacing a truckload driver at **$8,234** in 2000, about **$16,100** in today's dollars.

> At Qvery, we help trucking companies keep their good drivers and give their safety staff back the hours they lose to the camera queue, with custom AI agents that check every event against the road clip, the hours log and the route, close the misreads with the reason on file, and bring your safety manager only the events a person needs to see.

Below: what the queue costs you, what our agents check on every event, and the rule that keeps a flag off a driver's record until a person has decided it.

## The Safety Gain Comes From Coaching on the Clips, Not the Camera

The cleanest test of where a camera's value comes from is a trial NIOSH (the federal institute that researches workplace safety) ran on **315 work vehicles** from 2012 to 2014, short-haul delivery box trucks among them.

In one period, the drivers in its test groups got in-cab warning lights whenever they drove harshly. In another, supervisors sat down with each of them over video of their own risky moments and coached them.

The lights alone made **no measurable difference** against a control group that got neither lights nor coaching. Risky driving fell when a supervisor went through the clips with the driver, and it fell significantly more than under the lights.

So what you bought is a job: someone has to look at each clip and talk to the driver about the ones that matter. The camera can't tell a risky driver from a misread road; it can only ask.

The camera companies know this and filter before you see anything, by their own account:

**What two camera vendors say their software does before you see an event**

| Vendor | What it says |
|---|---|
| Motive | Over **80%** of confirmed events reach managers unreviewed; 400-plus people check the rest |
| Samsara | Ranks events by risk, sends most drivers to self-review in its app, escalates the riskiest |

That makes the queue shorter. It doesn't answer what the clip can't show: whether the work zone had a flagger that day, how many hours into the shift the driver was, or whether this intersection has fooled the camera before. Your office holds those answers, and the vendor's software doesn't read them.

When the questions outrun the people answering them, a fleet ends up in one of three places, and drivers describe all three:

**Where a full event queue goes**

| What happens | What it costs |
|---|---|
| Events sit unread | False points stay on drivers' scores, and the record shows nobody looked |
| Events become write-ups | A driver gets counseled over a clip by someone who never watched the road side of it |
| Warnings get switched off | Following distance gets turned down until it misses real tailgating |

> A camera is a good witness and a bad manager. It records every hard brake and none of the reasons for it. The fleets that keep their drivers put a person between the clip and the driver, and make that person fast. Our agents give your safety manager the speed and the context. The verdict stays with your safety manager.
>
> Vlad Shvets, Partner @ Qvery

## What the Event Queue Costs You Today

Take a trucking company with a safety manager and a safety coordinator, the setup this example uses. Occupational health and safety specialists in truck transportation earned a mean **$73,600** in May 2025, by the Bureau of Labor Statistics. With benefits, which run 30% of private employers' compensation costs, each costs about **$105,100** a year.

Say 40% of their week goes to camera events and what follows them: watching clips, pulling up the context, clearing misreads, booking and logging coaching, answering drivers who dispute a flag. That's an assumption for the example; drivers' accounts include carriers where clearing false events became one person's whole job.

**What the camera queue costs the example company, a year**

| Line | Cost |
|---|---|
| Two safety staff, 40% of their week on events | $84,080 (1,664 hours) |
| One good driver who quits over a flag they didn't earn | about $16,100 to replace |
| A warning switched off to keep the peace | not priced: the camera stops catching what it was bought for |

Drivers already leave often.

Smaller truckload carriers (fleets that haul full trailer loads for customers) averaged **79.2%** driver turnover a year between 1995 and 2017 in the American Trucking Associations' survey, as the Bureau of Labor Statistics reported it.

I haven't found a study that measures how many of those departures a camera causes. What drivers say they quit over is the write-up from someone who didn't look.

## What Qvery's AI Agents Check Before a Clip Reaches Your Safety Manager

Our agents sit between the camera platform and your safety staff. They're connected to the systems the context already lives in: the camera platform, the electronic logging device (the ELD, the box that records each driver's hours of service), your dispatch notes, and each driver's safety record.

![Camera events, ELD hours and dispatch and driver records go to the AI agents, which check the road clip, add context and close a misread or prepare a real risk for the safety manager to decide.](https://qvery.ai/blog/ai-dashcams/illustration-event-review.webp)

*The agents check and prepare each event; your safety manager decides what reaches a driver.*

A red-light event lands from the camera platform. The agents open the road-facing clip and find a person in a vest holding a sign at the intersection, and the day's dispatch note mentions a lane closure on that route.

They close the event as a work-zone misread, write that reason on it, and add the intersection to your list of places where the camera misreads, so the next event there is checked against it and counted.

A drowsiness event from the driver-facing camera goes the other way. The agents pull the driver's hours from the ELD, see the event came late in a long driving day and that the camera flagged the same driver **twice this week**, and put it first on your safety manager's list with the clip, the hours and the history attached.

She watches it and decides, and the agents carry out what she decides:

- If she coaches, the agents book the conversation, remind until it happens and log that it did.
- If she clears the event, they record her reason and see that the points come off.
- If a driver disputes a flag, they assemble the same file for whoever answers it.

If your camera vendor already filters well, the misread pile is smaller and the agents' work is mostly the context, the follow-through and the record. That's the part no vendor's filter touches, because it lives in your systems, not theirs.

### What the Agents Hand Back, on Your Own Numbers

In the example, the agents take half of the queue work, and the two safety staff get back **832 hours** a year. After about $550 a month to run the agents, the company keeps **$35,440 a year**, and every $10,000 of build is earned back in about 3.4 months.

Put in your own team's numbers:

(An interactive calculator on the page: roi-camera-event-review.)

Half is the example's assumption. The agents take the context on every event, the misreads the road clip settles, the coaching follow-up and its log, and the dispute file. Watching the events that matter, every coaching conversation and every decision about a driver's record stay with your people.

The running cost is an estimate: a few cents of model usage an event, including a look at the clip's frames, plus hosting, monitoring and upkeep, at about 1,500 events a month.

The calculator counts hours. It leaves the drivers out, and one good driver kept is worth about $16,100 by the replacement-cost study: close to half the year's saving, from one driver.

If your safety team spends its week clearing clips instead of coaching, that's the work to hand over first.

Have Qvery Build Your Camera Event Review Agents: https://calendly.com/qvery-vlad/30min

## No Flag Reaches a Driver's Record Without a Person

A person who has watched the road clip decides every flag before it touches a driver's score, record or pay, and the agents work inside that line.

**What the agents may do, and what they never do**

| The agents may | The agents never |
|---|---|
| Close an event the road clip shows is a misread, with the reason written on it | Discipline, counsel or contact a driver about a flag |
| Pull the hours, the route and the driver's history into an event | Change a score, a record or a pay line before a person decides |
| Book and track the coaching your safety manager orders | Delete, trim or edit a clip |
| Build the file when a driver disputes an event | Close a kind of event you haven't approved them to close |

Driver-facing video is personal data, and we treat it that way. Before the agents touch your camera platform, we agree in writing what they may read and do and where the data is processed. They start read-only, with the least access the job needs.

Every action they take is logged, and at first nothing leaves your company until a person has seen it. Qvery AI OÜ is an EU company and signs a data processing agreement, and you decide which kinds of events the agents close on their own.

The fleets drivers praise already work this way, by hand: an experienced reviewer watches each event, clears the misreads he's seen before, and flags the place so it stops coming back. The agents run that practice on every event, the hour it arrives.

**What Reddit says**

> We would have to have a team of 30 people if we had to review every “ding”. It’s like 40 per driver per day. Most don’t even make it to the carrier.
> ([r/Truckers · Aug 2025](https://www.reddit.com/r/Truckers/comments/1mf0810/major_aggravation_with_ai_cameras/n6fk0zg/))

> I have been hit for things that clearly never happened and the points stuck until they rolled off in 4 weeks.
> ([r/Truckers · Jul 2025](https://www.reddit.com/r/Truckers/comments/1m0i4in/so_the_ai_camera_just_hit_me_for_a_red_light/n3al46h/))

> My company reviews the events logged for validity, and if a bad read they flag the event and location to address them and not pop up again
> ([r/Truckers · Aug 2025](https://www.reddit.com/r/Truckers/comments/1mf0810/major_aggravation_with_ai_cameras/n6fh4ud/))

## Your First Two Weeks Change Nothing on a Driver's Record

We start from what your safety team does today: which events it opens first, what it checks before calling a driver, how it records a decision. From you we need:

- Read access to the camera platform and the ELD.
- Your dispatch notes.
- Your coaching policy as it stands.

For the first two weeks the agents write their verdict beside each event and **close nothing**. Your safety manager works the queue as she does now and sees, event by event, where the agents agree with her and where they don't.

Then you choose which kinds of events the agents may close on their own.

The natural first ones are the misreads a road clip settles at a glance, like work zones and signs on the wrong road. Every week after that, we show you the hours handed back to your safety team and the false flags that never reached a driver.

## Frequently asked questions



### Do AI dashcams reduce accidents?

The best controlled evidence measures risky driving, not crashes. In NIOSH's trial on 315 work vehicles, in-cab warning lights alone made no measurable difference; risky driving fell when supervisors went through each driver's own clips with them. The road-facing clip also protects drivers: when a car cuts in, it shows who caused the crash.

### Can an AI camera flag be grounds for discipline on its own?

A company can write a driver up over a flag, and it's the quickest way to lose the drivers who know the flag was wrong. A flag is the camera's guess about a few seconds of video. Our agents never discipline or contact a driver; a person decides, with the road clip in front of them.

### How do we cut false alerts from Samsara, Netradyne, Motive or Lytx?

Turning an alert down cuts the real cases along with the false ones. Recalibrate any camera that flags constantly, since drivers report one truck dinging all day while the next stays quiet. Then keep a list of the places that fool the camera, like exit-ramp signs and work zones. Our agents keep that list current.

### Do driver-facing cameras make drivers quit?

I haven't found a study that measures it. Turnover was high before AI cameras: 79.2% a year at smaller truckload carriers from 1995 to 2017, in the American Trucking Associations' survey. What drivers describe is narrower than the camera: a write-up from someone who didn't watch the road side, and false points on their score.

### Will this work with the camera system we already have?

We start from the access your camera platform gives you as a customer, and the ELD you already run. The agents read what those systems hold and write their notes back where your safety team works. If your platform has no way in for software, we say so before anything is built.

### What happens when a driver disputes an event?

The agents build the file a reviewer would: both clips, the day's hours, the route, the dispatch note and the driver's history. A person reads it and answers. If it was a misread, the reason goes on the record, the points come off, and the place joins the misread list.

### Do the agents ever delete or edit footage?

No. Every clip stays exactly as the camera recorded it, in the platform that stores it. The agents add a decision and a reason to each event, so the record shows that someone looked and what they concluded.

### Does this replace our safety manager?

No. It takes the clearing, the looking-up and the follow-up off your safety manager's desk, so the week goes to coaching and to the drivers who need it. We measure the agents in hours handed back and false flags stopped before they reach a driver.

### How soon would the agents be running on our events?

Within two weeks of the day we have access to your camera platform and your ELD. For those first weeks the agents write their verdict on each event and close nothing, so you see how they judge your events before they act. Then you choose which kinds they close on their own.

### What does it cost to run the agents each month?

In the example, about $550 a month: a few cents of model usage an event, a look at the clip's frames included, plus hosting, monitoring and upkeep, at about 1,500 events a month. More events cost more to run. The build itself is a fixed price for a defined result, agreed before any work starts.

### Can the agents coach our drivers directly?

No, and we wouldn't build them to. Drivers resent a verdict from someone who wasn't there, and software in the cab is further away than any office. The agents prepare what the conversation needs and see that it happens; the conversation stays with a person your drivers know.