The automotive industry continues to be a hotbed of patent innovation. Activity is driven by safety, cost, performance, and convenience, and growing importance of technologies such as 3D object detection, sensor fusion. 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: object detection DNNs. 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

Object detection DNNs is a key innovation area in artificial intelligence

Object detection deep neural networks (DNNs) are a type of machine learning algorithm used in computer vision to detect objects within an image or video. These DNNs use a combination of convolutional neural networks (CNNs), which are used for feature extraction, and region proposal networks (RPNs), which propose candidate regions likely to contain an object, to accurately identify objects in complex visual scenes.

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 20+ companies, spanning technology vendors, established automotive companies, and up-and-coming start-ups engaged in the development and application of object detection DNNs.

Key players in object detection DNNs – 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 object detection DNNs

Company Total patents (2010 - 2022) Premium intelligence on the world's largest companies
Huawei Investment & Holding 42 Unlock Company Profile
Qualcomm 48 Unlock Company Profile
Toyota Motor 18 Unlock Company Profile
Baidu 235 Unlock Company Profile
Alphabet 226 Unlock Company Profile
Hyundai Motor 23 Unlock Company Profile
Aptiv 31 Unlock Company Profile
Honda Motor 33 Unlock Company Profile
Panasonic 98 Unlock Company Profile
Porsche Automobil 39 Unlock Company Profile
General Motors 82 Unlock Company Profile 74 Unlock Company Profile
NVIDIA 186 Unlock Company Profile
LG 39 Unlock Company Profile
Walmart 147 Unlock Company Profile
Kia 18 Unlock Company Profile
NEC 19 Unlock Company Profile
Intel 326 Unlock Company Profile
Ford Motor 92 Unlock Company Profile
Micron Technology 25 Unlock Company Profile
Uber Technologies 33 Unlock Company Profile
SZ DJI Technology 22 Unlock Company Profile
Brain 22 Unlock Company Profile
Proxy Technologies 40 Unlock Company Profile
Samsung Group 47 Unlock Company Profile
Beijing TuSimple Future Technology 70 Unlock Company Profile
Nuro 246 Unlock Company Profile
Stradvision 309 Unlock Company Profile
Toyota Motor 19 Unlock Company Profile

Source: GlobalData Patent Analytics

Intel is one of the leading patent filers in object detection DNNs for the automotive industry. One of the latest patents Intel has filed is for a system and method for training autonomous vehicles (AVs) to navigate complex environments. The system uses a deep learning algorithm to predict other vehicles' and pedestrians' behavior, and a reinforcement learning algorithm to train the AV to navigate safely and efficiently. The system is designed to be deployed in a simulation, where the AV learns from the simulation to navigate the real world safely. This patent could potentially improve the safety and performance of AVs in real-world conditions. Stradvision and Nuro are some of the other key patent filers in object detection DNNs.

In terms of application diversity, Nuro leads the pack, while Walmart and Proxy Technolgies stood in the second and third positions, respectively. By means of geographic reach, Nuroheld the top position, followed by Walmart and Proxy Technologies.

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.