Below the Surface: How AI Revolutionizes Damage Detection for Carsharing Fleets
Managing a carsharing fleet comes with many challenges, but one of the most critical is damage detection and reporting. Traditional methods rely heavily on manual inspections, driver self-reporting, and sometimes even lead to disputed claims—causing inefficiencies and reimbursement shortfalls, with the operator ultimately bearing the entire cost.
At carvaloo®, we use Artificial Intelligence to automate the entire damage process, eliminating guesswork and ensuring accurate, reliable results. Just like with an iceberg, only a fraction of the process is visible above the surface: the finished damage report. But beneath it, a sophisticated AI-driven system works seamlessly to detect, verify, and document every damage event. Let’s take a deeper dive into how it works.
Step 1: Detecting Damage with Motion AI
The first step in identifying vehicle damage begins with carvaloo’s Motion AI. Using vehicle motion data from the telematics unit, our technology detects even minor incidents by recognizing specific impact and movement patterns.
Our AI is trained on more than 300 million kilometers of trip data and tens of thousands of real damage cases, allowing it to distinguish between harmless vibrations (like a pothole hit) and actual damage events. This process often requires no additional hardware—many carsharing telematics devices are already compatible, making integration seamless and cost-effective.
Step 2: Engaging the Driver with Smart Communication
Once the Motion AI flags a damage event, the next step is to verify the damage. The smart feedback agent automatically contacts the responsible driver and the subsequent driver before they begin their trip.
The driver receives a prompt to upload photos of the damaged area. This step ensures transparency while reducing disputes and enabling faster claim resolution.
Step 3: Verifying the Damage with Image AI
This is where Image AI comes into play. After the driver submits images, the AI specifies the type and size of the damage and cross-references the pictures with previous photos of the vehicle, identifying whether the damage is recent or pre-existing. Image AI recognizes when a user has photographed and uploaded an existing damage and prevents someone from being charged for the wrong damage.
But Image AI doesn’t just rely on driver reports. It also performs regular, automated vehicle scans, ensuring that even unreported damages are detected. This proactive approach prevents fleet operators from missing critical information that could impact claims and repairs.
Step 4: Generating a Ready-to-Claim Damage Report
Once all data points—motion detection, driver input, and image verification—are collected and analyzed, carvaloo’s system consolidates everything into a comprehensive damage report. This report includes:
- Time and location of the damage event
- Driving situation during the damage
- AI-assessed type and size of the damage
- Driver confirmation and statements
- Before-and-after images for verification
Fleet operators receive a complete, claims-ready document, eliminating the need for manual investigation. This not only accelerates the claims process but also significantly reduces operational overhead.
The Future of Damage Detection in Carsharing
By automating damage detection and reporting, carvaloo® empowers carsharing operators to minimize downtime, prevent revenue loss, and improve fleet efficiency. The result? Faster claims resolutions, fewer losses due to repairs, and a seamless experience for both operators and users.Thanks to the clear assignment to the responsible person, the operator can recover repair costs instead of having to bear them.
With AI taking care of what happens below the surface, you can focus on keeping your fleet running smoothly. Are you ready to revolutionize your damage management process? Get in touch with us today!