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The automotive industry continues to be a hotbed of innovation, with activity driven by factors such as passenger safety and enhanced in-car experiences, and growing importance of technologies such as artificial intelligence, the Internet of Things, sensors, and machine learning. In the last three years alone, there have been over 1.2 million patents filed and granted in the automotive industry, according to GlobalDatas report on Artificial intelligence in Automotive: AI-assisted CAD.

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 stabilising 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.

290+ innovations will shape the automotive industry

According to GlobalDatas Technology Foresights, which plots the S-curve for the automotive industry using innovation intensity models built on over 619,000 patents, there are 290+ innovation areas that will shape the future of the industry.

Within the emerging innovation stage, manufacturability analysis, autonomous parking, and lidar for vehicle anti-collision are disruptive technologies that are in the early stages of application and should be tracked closely. Speed profile estimation, smart light dimmers, and driver drowsiness detection are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas are road slope estimation and adaptive cruise control, which are now well established in the industry.

Innovation S-curve for artificial intelligence in the automotive industry

AI-assisted CAD is a key innovation area in artificial intelligence

AI is frequently integrated with CAD for automated decision-making (ADM), which involves the use of AI algorithms to collect, process, model, and use data in support of automated decisions. Finally, decision-making feedback is reviewed and evaluated in order to improve the decision-making process.

GlobalDatas 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 80+ companies, spanning technology vendors, established automotive companies, and up-and-coming start-ups engaged in the development and application of AI-assisted CAD.

Key players in AI-assisted CAD a disruptive innovation in the automotive industry

Application diversity measures the number of different applications identified for each relevant patent and broadly splits companies into either niche or diversified innovators.

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

Source: GlobalData Patent Analytics

Taiwan Semiconductor Manufacturing is one of the leading innovators in AI-assisted CAD. The company has developed ground-breaking microelectronic technologies, with 3D integrated circuit packaging. A huge amount was invested in this chip development project by Taiwan Semiconductor Manufacturing and the Japanese governments to enhance the scope of the CAD industry. Other companies innovating in this technology domain are Halliburton, General Motors, Bosch, Toyota Motors, Hitachi, etc.

To further understand how artificial intelligence is disrupting the automotive industry, access GlobalDatas latest thematic research report on Artificial Intelligence (AI) in Automotive.

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

GlobalDatas 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 worlds largest industries.

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