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Data Insights Artificial intelligence in automotive: AI in damage estimation

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However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilizing and reaching maturity.

Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.

300+ innovations will shape the automotive industry

According to GlobalData’s Technology Foresights, which plots the S-curve for the automotive industry using innovation intensity models built on over one million patents, there are 300+ innovation areas that will shape the future of the industry.

Within the emerging innovation stage, self-driving neural networks, autonomous lane changing and AV simulation and testing are disruptive technologies that are in the early stages of application and should be tracked closely. Predictive acceleration control, collaborative autonomous driving, and AV perception and control systems are some of the accelerating innovation areas, where adoption has been steadily increasing.

Innovation S-curve for artificial intelligence in the automotive industry

AI in damage estimation is a key innovation area in artificial intelligence

AI in damage estimation refers to the use of artificial intelligence techniques, such as image processing and machine learning algorithms, to automatically estimate the repair cost and identify the extent of damage to a vehicle or property. This involves analyzing images of the damaged vehicle or property, detecting external damage, inferring internal damage, and calculating an estimated repair cost based on a parts database and labor costs.

GlobalData’s analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 2765+ companies, spanning technology vendors, established automotive companies, and up-and-coming start-ups engaged in the development and application of AI in damage estimation.

Key players in AI in damage estimation – a disruptive innovation in the automotive industry

‘Application diversity’ measures the number of applications identified for each patent. It broadly splits companies into either ‘niche’ or ‘diversified’ innovators.  

‘Geographic reach’ refers to the number of countries each patent is registered in. It reflects the breadth of geographic application intended, ranging from ‘global’ to ‘local’. 

Patent volumes related to AI in damage estimation

State Farm Mutual Automobile Insurance is one of the key patent filers in AI in damage estimation. The company’s Computer Vision and Image Analysis helps in AI-driven computer vision systems. It can analyze images and videos of damaged vehicles to assess the extent and type of damage. Convolutional neural networks (CNNs) are often used to identify and categorize dents, scratches, and other damages. Samsung Group, Robert Bosch Stiftung, and NVIDIA are some of the other key patent filers in this area.

In terms of application diversity, ADOBE leads the pack. Meta Platforms and Fujifilm stood in the second and third positions respectively. By means of geographic reach, Cox Enterprises held the top position followed by Stryker and The Saudi Public Investment Fund.

To further understand the key themes and technologies disrupting the automotive industry, access GlobalData’s latest thematic research report on Artificial Intelligence (AI) in Automotive.

GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.

GlobalData’s Patent Analytics tracks patent filings and grants from official offices around the world. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.