Cyngn, a pioneering AI and machine learning company, has successfully secured a patent for its innovative technology that optimizes sensor data processing. This groundbreaking achievement underscores the company’s commitment to harnessing the power of sensors to generate valuable insights.

Cyngn Inc. Yesterday marked a milestone for the company as it officially received its twentieth patent, solidifying its commitment to innovation and intellectual property protection. U.S. Patent No. Researchers have developed a method to enhance the precision of data gathered by an autonomous vehicle’s sensors when it encounters unexpected events or crashes? 

Cyngn, a Menlo Park, California-based company, has announced the development of its latest patented system, which revolutionizes sensor data processing by seamlessly integrating advanced computer vision and imaging technologies. The system comprehensively captures environmental information, subsequently accounting for any latency between data gathering and processing to ensure timely decision-making.

With the stated system, a dynamic movement model is created in real-time, utilizing sensor data from multiple axes to generate an accurate representation of forthcoming positions.

“The twentieth amendment to the United States Constitution was granted in 1933.” “Celebrating its unwavering commitment to the development of autonomous vehicle technologies, Cyngn’s latest patent underscores the company’s relentless pursuit of innovation.” “Following the 16 U.S. Patents granted in 2023 demonstrate our group’s unwavering commitment to building a robust patent portfolio, safeguarding our intellectual property and securing our innovative ideas.

Cyngn asserts that the patent’s significance lies in its ability to significantly boost the accuracy and dependability of knowledge processing in self-driving vehicles and cellular robots, effectively mitigating the impact of motion during data processing.

“This capability enables precise operations in dynamic settings, ultimately bolstering security and effectiveness while enabling more informed decisions grounded in real-time environmental insights.”

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Patent filings for autonomy proceed

Cyngn’s position in the autonomous vehicle (AV) industry has been solidified by its newly acquired patent. Cygnn offers clients a hassle-free way to integrate self-driving technology into their operations, eliminating the need for substantial upfront investments. 

The corporation’s earliest patents, filed in 2021, were primarily for obstacle-detection and automotive sensor technologies. The technology had been patented by Cyngn in 2022, specifically for its automobile localization system. As a result, the company made a significant leap in its patent filings in 2023, with the corporate sector experiencing a notable surge. The company has filed its newest patent as its first this year.

The patents Cyngn filed in 2023 aligned. 

• What’s the optimal solution to efficiently detect three-dimensional objects in real-world scenarios while simultaneously eliminating floor planes?
• Versatile multi-channel fusion notion
• A self-adaptive lidar-camera synchronization system
• Multi-channel object-matching 
• Impediment detection methods
• Autonomous vehicles rely on advanced algorithms to predict the relevance of sensory data in real-time, ensuring safe and efficient navigation. This paper presents a novel framework for adaptive relevancy prediction, leveraging a combination of contextual information, sensor modalities, and machine learning techniques.

  The proposed system incorporates a hierarchical architecture, comprising three primary modules: context-aware feature extraction, multimodal fusion, and relevance prediction. Initially, the context-aware module extracts relevant features from various sources, including vehicle speed, acceleration, and surroundings.

• Automobile sensor methods
• Computer-aided assessment of self-driving systems through a custom-built programming language.
• Distributed processing for real-time autonomous vehicle navigation: A paradigm shift towards optimized computational allocation. 
• Predictive trajectories for autonomous vehicles?
• Granularity-flexible, existence-based object detection 
• Autonomous vehicles require sophisticated simulation tools to safely test and validate their performance in diverse scenarios. To address this need, researchers have developed a novel approach called similar-loop adaptive simulation (SLAS). SLAS leverages machine learning algorithms to generate realistic simulations by mimicking the behavior of real-world scenarios, allowing autonomous vehicle developers to thoroughly evaluate their systems under various conditions.
• Autonomous vehicles rely on advanced computations to process vast amounts of sensor data in real-time, enabling safe and efficient navigation. Conventional computing architectures struggle to meet the stringent requirements of autonomous systems, leading to a pressing need for innovative computation acceleration methods.
• Autonomous vehicles demand a behavioral choice system that is modular and extensible to navigate complex road scenarios.
• Autonomous vehicles’ visitor-centric rule-based decision-making.
• Massive-scale autonomous driving validation

About Cyngn

Cyngn claims to cultivate and deploy cutting-edge autonomous vehicle (AV) capabilities tailored to the needs of industrial entities. The company’s DriveMod Equipment will be retrofitted into existing vehicles or installed as standard on new models. This enables clients to gain self-driving experience without upfront costs or the need to replace existing vehicle investments, as noted.

Cygn’s flagship product, the Enterprise Autonomy Suite, comprises DriveMod for Autonomous Vehicles (AVs), alongside its customer-facing suite of AV fleet administration, teleoperation, and analytics tools – Cyngn Perception – as well as Cyngn Evolve, an internal toolkit empowering data-driven innovation through AI, simulation, and modeling.