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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, autonomous on-demand logistics, end-to-end learning models, and adaptive driver alerting are disruptive technologies that are in the early stages of application and should be tracked closely. Vehicular vision, adaptive cruise control, and predictive acceleration control are some of the accelerating innovation areas, where adoption has been steadily increasing.
Innovation S-curve for artificial intelligence in the automotive industry
End-to-end learning models is a key innovation area in artificial intelligence
End-to-end learning models refer to machine learning models that take raw input data and directly output an action or decision without the need for manual feature engineering or intermediate steps. These models learn from the raw data to make decisions or actions.
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 30+ companies, spanning technology vendors, established automotive companies, and up-and-coming start-ups engaged in the development and application of end-to-end learning models.
Key players in end-to-end learning models – 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 end-to-end learning models
Source: GlobalData Patent Analytics
Intel, the top patent filer in automotive end-to-end learning models, has filed two patents for a deep learning system for autonomous vehicle prediction and control. The system uses sensor data to predict vehicle trajectory in real time and improve vehicle performance and safety. These patents could revolutionize autonomous vehicle development, making them safer, more efficient, and more affordable. Baidu and Alphabet some of the other leading patent filers in end-to-end learning models.
In terms of application diversity, FiveAI leads the pack. Luminar Technologies and Ford Motor stood in the second and third positions respectively. By means of geographic reach, FiveAI held the top position followed by FedEx and Intel.
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.
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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.