AI or Machine Learning Application Trends In Vehicle Imaging

Currently, numerous companies are making use of AI in the vehicle imaging space for a wide area of applications such as assessing vehicle condition, evaluation, and insurance claims. In the future, companies such as Audi plan to make use of machine learning (ML) in their quality control process.

Current Trend: AI applications for vehicle inspection and evaluation

  • AI is used to automate the visual inspection and RV calculation for returned used cars.
  • UVeye offers automatic vehicle inspection solutions that make use of advanced technologies that include proprietary hardware combined with deep learning and computer-vision algorithms. Their products are deployed globally and enable customers to automatically scan, detect and identify anomalies, foreign objects or modifications in the undercarriage of any vehicle.
  • In India, companies in the used-car industry such as Mahindra First Choice Wheels, Quikr, Droom, and Truebil, among others are leveraging AI-driven algorithms to predict the price of a car based on parameters like its condition, color, make, history along with other geographical and seasonal factors. Also, Microsoft and ICICI Lombard have developed an AI-enabled car inspection application that uses vehicle imaging and enables customers to buy or renew policies and make damage claims.
  • RAVIN, a company based in Israel and London, offers an AI-based solution that uses vehicle imaging for analyzing a vehicle’s current condition.

Why it is considered a current trend

  • The use of AI for vehicle inspection and evaluation was identified as a trend due to the growing number of companies currently making use of AI in the vehicle imaging space for a wide area of applications such as assessing vehicle condition, evaluation, and insurance claims.

Future Trend: The use of AI for enhanced quality control

  • According to McKinsey, AI-powered hardware can be used to visually inspect and provide superior quality control on various products such as painted car bodies, machined parts, and textured metal surfaces, among others.
  • In October 2018, Audi announced its plans to apply machine learning (ML) in series production. The company is currently testing its automated component inspections for series production at its Ingolstadt press shop.
  • According to the company, the ML quality inspection solution will replace the current optical crack detection with smart cameras in the future. Furthermore, the company states that machine learning can be applied to other visual quality inspections to support paint shops or assembly shops in the future.

Why it is considered a future trend

  • The use of AI for enhanced quality control is considered a future trend because Audi hinted the future application of AI in the vehicle imaging space for quality control applications and is currently only testing the solution and plans to deploy it soon. Furthermore, McKinsey opined that there would be a rise in the AI-based solutions for quality control applications in the future.


To identify current and future trends in the application of AI or ML in the vehicle imagining and condition grading space in the U.S., we commenced with an extensive search through industry reports and market studies published by leading research firms such as Gartner, Forrester, IDC, Euromonitor International, Frost & Sullivan and Markets & Markets, among others. We found relevant information on global trends but not specific to the U.S. Next, we searched for any information on automotive manufacturers making use of AI or ML in the vehicle imaging and condition grading space in the U.S. We also searched through blogs and media pages such as Asmag, TimesofIndia, and Forbes. While we could not find information specific to the U.S., we found information on Audi, a German-based auto company and Microsoft. Hence, we have provided global trends in the application of AI or ML in the vehicle imagining and condition grading space.

Companies that Develop AI applications that can be Used in Vehicle Imaging and Condition Grading

Two companies that develop AI or machine learning applications that can be used in vehicle imaging and condition grading are Fyusion and HomeNet Automotive.


  • Fyusion uses computer vision technology and machine learning technology to create robust imaging solutions.
  • The company’s goal is to make 3D imaging more valuable than 2D imaging.
  • The company’s technology is covered by over 70 patents that allow them to create what they term as ‘fyuses.’
  • Fyuses are interactive 3D images created by moving a camera around a person, object, or scene.
  • Users can be able to interact with images in up to 360 degrees surround view.
  • For vehicle imaging, the company has the 360 degrees 3D Fyuse Imaging.
  • The product can show the vehicle inside and out and create professional-level images.
  • The product also provides important metrics on user engagement.
  • It also allows for manual tags that conduct condition analysis of the vehicle and also provides more in-depth vehicle information.
  • It offers cloud hosting, which ensures that images can be instantly uploaded.
  • The company’s partners include; Manheim, Gulliver, Harley Davidson, Cox Automotive, Home Net Automotive, and Audi.
  • The product is in its introduction phase as Fyusion began marketing and showing how their technology works to marketers in late July 2019.
  • Fyusion is located in San Francisco, United States.


Home Net Automotive helps dealers ensure that their directory is consistently updated, and distributed in a quickly and accurately and that the images are of quality. Dealers merchandise and distribute their inventory from a unified platform and share the data on their websites and other third party sites. Their products include SnapLot360 (simple to use image capture), Photo Backgrounding, Overdrive, Rapid Retail, Inventory Online, and Essentials, among others.The Snap Lot 360 provides a 3D view of the vehicle’s interior and exterior. It offers high-resolution images in seconds using image stabilization to increase efficiency.The hot spot tagging features drive more engagement as users can understand more about the vehicle.  Crown Auto Group is an example of this product’s customer.The company’s partners include; Smart Web Concepts, Auto Check, World Dealer, Auto Trader, CarFax, Chrome Data, Digital Dealer, eBay Motors, DigiGo,, FlickFusion, LotVantage, Motor Web, Adesa, and Auto Jockey.HomeNet launched the SnapLot 360 technology in January 2019, and the company launched a pilot program to test drive the new technology.The product is still in its introduction phase. HomeNet has its headquarters in Exton, Pennsylvania in the United States.

Glenn is the Lead Operations Research Analyst at The Digital Momentum with experience in research, statistical data analysis and interview techniques. A holder of degree in Economics. A true specialist in quantitative and qualitative research.


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