eMCOS Description
A robust application platform designed for autonomous driving systems utilizes diverse collective data to perceive the outside environment and make informed driving decisions independently. This system is supported by a scalable distributed computing infrastructure that leverages many-core processors alongside a variety of processor types to deliver the enhanced computational power essential for sophisticated information processing. Furthermore, the platform is designed to be adaptable, enabling software applications to operate across a range of hardware resources while maintaining real-time capabilities to guarantee both safety and reliability in autonomous operations. This versatility ensures that the system can evolve alongside advancements in technology and changing operational requirements.
eMCOS Alternatives
TRACTIAN
Tractian is the Industrial Copilot for maintenance and reliability, combining hardware and software solutions to monitor asset performance, manage industrial operations, and implement predictive maintenance strategies. Its AI-driven platform empowers businesses to prevent unplanned equipment downtime and boost production output. The company is headquartered in Atlanta, GA, and extends its presence globally with offices in Mexico City and Sao Paulo. Learn more at tractian.com.
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RaimaDB
RaimaDB, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times.
RaimaDB is a high-performance, cross-platform embedded database optimized for mission-critical applications in industries such as IoT and edge computing. Its lightweight design makes it ideal for resource-constrained environments, supporting both in-memory and persistent storage options. RaimaDB offers flexible data modeling, including traditional relational models and direct relationships through network model sets. With ACID-compliant transactions and advanced indexing methods like B+Tree, Hash Table, R-Tree, and AVL-Tree, it ensures data reliability and efficiency. Built for real-time processing, it incorporates multi-version concurrency control (MVCC) and snapshot isolation, making it a robust solution for applications demanding speed and reliability.
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DriveMod
DriveMod represents Cyngn's comprehensive solution for autonomous driving, seamlessly integrating with commonly available sensing and computing equipment to empower industrial vehicles with the ability to understand their environment, make informed decisions, and execute actions. This innovative system is designed to fit effortlessly into your current operations, allowing for straightforward programming of vehicle routes, loops, and missions. Essentially, anything a human driver can accomplish, DriveMod is capable of achieving as well. You can safely equip any commercially available vehicle with autonomous features through a simple retrofit process. The adaptability of DriveMod guarantees that diverse fleets operate efficiently, regardless of the vehicle's make or model. By leveraging advanced AI software alongside top-tier sensors and computing technology, DriveMod delivers performance that surpasses that of human operators. It can identify thousands of objects and evaluate numerous potential paths, efficiently determining the best route in mere fractions of a second, thereby revolutionizing the way vehicles navigate their surroundings. This remarkable capability positions DriveMod as a leading solution in the realm of autonomous vehicle technology.
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Apollo Autonomous Vehicle Platform
A combination of sensors, including LiDAR, cameras, and radar, gather data from the vehicle's surroundings. By employing sensor fusion technology, perception algorithms are capable of identifying, locating, measuring the speed, and determining the orientation of various objects on the road in real time. This advanced autonomous perception system is supported by Baidu's extensive big data infrastructure and deep learning capabilities, along with a rich repository of labeled real-world driving data. The robust deep-learning platform, complemented by GPU clusters, enhances processing power. Additionally, the simulation environment enables virtual driving across millions of kilometers each day, leveraging diverse real-world traffic and autonomous driving data. Through this simulation service, partners can access an extensive array of autonomous driving scenarios, allowing for rapid testing, validation, and optimization of models in a manner that prioritizes both safety and efficiency, ultimately fostering advancements in autonomous vehicle technology.
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Company Details
Company:
eSOL
Website:
www.esol.com/embedded/emcos_autoware.html
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