The automotive industry continues to be a hotbed of patent innovation. The automotive industry is leveraging the increasing availability of data to develop end-to-end learning models for tasks such as driving and object detection. The affordability of computing has made it more feasible to use these models in real-time applications like autonomous driving. The growing demand for autonomous vehicles also makes end-to-end learning models a promising approach for developing autonomous vehicle software. The automotive industry is utilizing deep learning, generative adversarial networks (GANs), reinforcement learning, and simulation-based training for end-to-end learning models, enhancing tasks such as image recognition, natural language processing, and machine translation, while minimizing damage to vehicles or people. In the last three years alone, there have been over 1.7 million patents filed and granted in the automotive industry, according to GlobalData’s report on Artificial intelligence in automotive: end-to-end learning models. Buy the report here.

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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

Company Total patents (2021 - 2023) Premium intelligence on the world's largest companies
Huawei Investment & Holding 11 Unlock Company Profile
FedEx 22 Unlock Company Profile
Qualcomm 7 Unlock Company Profile
Wing Aviation 7 Unlock Company Profile
Toyota Motor 12 Unlock Company Profile
Baidu 40 Unlock Company Profile
Alphabet 38 Unlock Company Profile
Robert Bosch Stiftung 5 Unlock Company Profile
Apple 9 Unlock Company Profile
Honda Motor 6 Unlock Company Profile
General Motors 38 Unlock Company Profile
Sony Group 12 Unlock Company Profile 6 Unlock Company Profile
NVIDIA 13 Unlock Company Profile
International Business Machines 7 Unlock Company Profile
NEC 11 Unlock Company Profile
Intel 57 Unlock Company Profile
Ford Motor 6 Unlock Company Profile
FiveAI 31 Unlock Company Profile
Samsung Group 5 Unlock Company Profile
Cognata 9 Unlock Company Profile
Beijing TuSimple Future Technology 4 Unlock Company Profile
SafeRide Technologies 6 Unlock Company Profile
Perceptive Automata 14 Unlock Company Profile
PlusAI 7 Unlock Company Profile
Stradvision 9 Unlock Company Profile
PlusAI 18 Unlock Company Profile
Luminar Technologies 15 Unlock Company Profile
Brain Trust Innovations I 5 Unlock Company Profile
Phantom AI 6 Unlock Company Profile
Here 9 Unlock Company Profile
Uatc 8 Unlock Company Profile
Curie AI 5 Unlock Company Profile
Toyota Motor 6 Unlock Company Profile

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.