Automotive Testing Expo 2018
Zuragon and Sonnet.ai showed the self-driving vehicle Sonnet Autodrive during Testing Expo in South Korea. It is based on the ViCANdo test environment.
As mentioned the vehicle is based on the ViCANdo technology for sensor fusion and controls and on the Sonnet.ai´s intellectual property rights for autonomous solutions. Those solutions were developed during several years and cover Radar, Lidar, as well as Computer vision and controls.
The target of collaboration between the parties is to become a notable player in the autonomous vehicle field, with a more complete portfolio of tools, services, and off the shelf algorithms and controls.
ViCANdo is a rapid-development environment for bus-based systems. It offers support for a variety of busses and technologies including Classical CAN, CAN FD, J1939, NMEA 2000, LIN, and Isobus.
Joachim Fritzson, President and CEO of Zuragon said in a comment: “We have been collaborating with Sonnet.ai for a while behind the scene and we are deeply impressed of the level of their knowledge. We are proud and happy to have them as partner and part of the Zuragon family and we have high expectations on the outcome of this collaboration.”
Dr. Joonwoo Son, Technical Director of Sonnet.ai said: “Finding a fast feet partner like Zuragon is really great for us. We expect to contribute to their portfolio and they to contribute to ours. We have just seen the beginning of this technology in South Kora and Asia. There is a lot more to come.”
Zuragon founded in 2011 is focusing on tools for development and test of Active Safety Systems (ADAS) for vehicles. With offices in Sweden, Finland, UK, Romania, and USA and Qualified resellers in China, Japan and South Korea, the company offers a worldwide support to customers and development partners. Sonnet.ai is a tech startup bringing artificial intelligence to autonomous vehicles and precision medicine. Currently, they are developing autonomous vehicles using deep learning and conventional software technologies. They are also applying AI and machine learning to predict the drug response of cancer patients based on personal genomic profiles.
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