The automotive industry continues to be a hotbed of patent innovation. Innovation in motion-based segmentation in the automotive industry is driven by the growing demand for autonomous vehicles, advancements in LiDAR sensor technology, government regulations, and the increasing availability of high-quality LiDAR data. Motion-based segmentation technology is used for real-time object detection, 3D scene reconstruction, and high-definition mapping, enabling safer navigation and more accurate mapping for various autonomous driving applications. The automotive industry is utilizing advanced technologies in motion-based segmentation, including deep learning, optical flow, stereo vision, and radar. These technologies can identify and track moving objects in LiDAR point cloud data, even in challenging environments, with varying accuracy and computational costs. 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: motion-based segmentation. 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
Motion-based segmentation is a key innovation area in artificial intelligence
Motion-based segmentation refers to the process of identifying and separating objects from their background by analyzing the motion in the scene. By detecting the motion of objects, this technique can distinguish between the moving and non-moving parts of an environment to recognize objects and their movement.
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 motion-based segmentation.
Key players in motion-based segmentation – 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 motion-based segmentation
Source: GlobalData Patent Analytics
Intel is one of the leading patent filers in motion-based segmentation for the automotive industry. This technology aids in object detection, obstacle avoidance, lane keeping assistance, adaptive cruise control, and automated parking. Alphabet and Baidu are some of the other key patent filers in this area. Applications of motion-based segmentation include autonomous vehicles, advanced driver assistance systems, and traffic management, making roads safer and more efficient.
In terms of application diversity, Kioxia leads the pack, while BlackBerry and Zhejiang Geely stood in the second and third positions, respectively. By means of geographic reach, Intel held the top position, followed by Alphabet and NVIDIA.
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.
