Project Damage Control – AI Reshaping of the Automotive and Fleet Management Sectors

29. August 2025

Artificial Intelligence is reshaping the automotive and fleet management sectors by introducing automation, precision, and scalability into processes that were traditionally manual and error-prone. In fleet management, AI enables businesses to monitor vehicle conditions, automate inspections, and streamline reporting, thereby reducing human error and saving valuable time.

Within the Damage Control project, this vision is realized through the combined expertise of the consortium members. TubeIQ contributes deep industry process knowledge, ensuring that the AI workflows reflect real-world rental and fleet management practices. DunavNET brings advanced technical expertise in high-performance computing, computer vision, and AI model development, enabling the deployment of robust multimodal solutions. Meanwhile, AAA-1 RENT provides direct access to operational data and insights from daily fleet operations, ensuring the solution is practical, validated, and impactful for the industry.

Together, these complementary strengths enable the consortium not only to apply AI for vehicle damage assessment but also to demonstrate how technology can modernize fleet management by improving efficiency, transparency, and customer satisfaction.

AI-Powered Vehicle Damage Assessment

Transforming a few photos of a damaged vehicle into a clear, structured report is no longer a distant goal, it’s becoming reality thanks to advances in artificial intelligence. With more vehicles on the road and insurance claims becoming more detailed, companies need faster, more reliable ways to assess damage - AI is making this possible.

In the Damage Control project, partners explore how multimodal large language models (LLMs), deep learning, and natural language processing (NLP) can work together to deliver a smarter, faster approach to damage assessment. Instead of simply detecting visible issues, the system understands context, turning raw images into insights. In the Damage Control project, high-performance computing (HPC) resources with a hybrid AI strategy and commercial multimodal models are tested in parallel, covering the entire workflow from detection to generating a structured JSON output with human-readable descriptions. This approach balances flexibility, transparency, and scalability, enabling seamless integration into real-world workflows.

Turning Images into Insights

The solution takes vehicle images as input and automatically identifies and describes the type, location, and severity of damage, producing structured outputs that are ready for insurance processing. The models were trained on thousands of annotated images, and HPC clusters accelerated large-scale experimentation and faster model convergence. Vision LLMs bridge the gap between visual data and natural language: the system doesn’t just flag damaged areas, it generates detailed, context-aware descriptions, produces standardized reports, and supports multilingual claim submissions, making it practical for global operations.

Results That Matter

The Damage Control project has already delivered high accuracy in detecting and describing vehicle damage, automatically generating structured, insurance-ready data, and significantly reducing the time required to process claims. These outcomes improve operational efficiency while maintaining clarity and usability for insurers, fleet managers, and end customers.

Looking Ahead

By turning raw images into structured insights, the system enhances decision-making, streamlines insurance workflows, and improves customer experience. As AI continues to evolve, projects like Damage Control are paving the way for a smarter, faster, and more connected automotive ecosystem.

Conclusion: Overview of the Damage Control Objectives

The Damage Control sub-project was designed with a clear objective: to modernize and streamline the way vehicle damage is reported and documented in the rental and fleet management industry. By leveraging AI technologies such as computer vision, natural language processing, and document intelligence, the project delivers a descriptive, standardized, and transparent assessment of damages that significantly reduces manual workload and human error.

For the industry, this means faster and more reliable service offerings. The integration of AI allows rental companies and fleet operators to provide near real-time assessments and accelerate claims processing, which we project could increase customer satisfaction by 20–30%. This creates not only operational efficiency but also a competitive advantage, enabling early adopters to differentiate themselves by offering innovative, technology-driven services.

For the consortium, the combined expertise of TubeIQ, DunavNET, and AAA-1 RENT creates synergy fosters innovation, enabling the consortium to deliver tangible benefits not just to the partners, but also to SMEs across Europe by setting new benchmarks in efficiency and standardization.

Ultimately, Damage Control is not only an enabler of business growth for the partners but also a step forward for the broader automotive ecosystem. It represents how AI can be responsibly applied in real-world contexts to enhance transparency, streamline operations, and create sustainable value for both companies and their customers.

Learn more about this FFplus sub-project here.

Authors: Daliborka Nedić (DunavNET) and Aleksandar Ristić (TubeIQ)