The automotive industry continues to be a hotbed of innovation, with activity driven by electrification, connected vehicles, and autonomous vehicles, and growing importance of technologies such as artificial intelligence (AI), machine learning (ML), cyber security, and cloud computing. In the last three years alone, there have been over 1.2 million patents filed and granted in the automotive industry, according to GlobalData’s report on Cloud in Automotive: AI-assisted fault monitoring.
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 GlobalData’s 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, autonomous parking, manufacturability analysis, and LiDAR for vehicle anti-collision are disruptive technologies that are in the early stages of application and should be tracked closely. Intelligent automated assistant, real-time fault diagnosis and condition monitoring system, 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 adoptive cruise control, which are now well established in the industry.
Innovation S-curve for cloud in the automotive industry
AI-assisted fault monitoring is a key innovation area in cloud
The AI detection system employs artificial neural networks (ANNs) as the core fault detector to rapidly predict a malfunction in the vehicle. The detection system gives early warning to the vehicle user in case of an unexpected issue with the vehicle during driving.
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 60+ companies, spanning technology vendors, established automotive companies, and up-and-coming start-ups engaged in the development and application of AI-assisted fault monitoring.
Key players in AI-assisted fault monitoring – 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’.
Patent volumes related to AI-assisted fault monitoring
Source: GlobalData Patent Analytics
Information technology giants, IBM and Microsoft, lead in number of patents filed in AI-assisted fault monitoring systems. These companies are working on several AI solutions to be employed in the auto industry. Some of the areas included are automotive engineering simulation workloads, fault injection detectors, and light-weight fault localisation systems. Other major companies filing patents in the area are People.ai, Oracle , Dell Technologies, and Aurora Labs.
To further understand how cloud is disrupting the automotive industry, access GlobalData’s latest thematic research report on Automotive.
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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.
