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Solutions

Rich Experience in Mass Production of Before-market Products
Take the lead to realize mass production in OEMs of passenger vehicles in China, which has been applied to several before-market and after-market projects for mass production. Conduct joint research and development cooperation with several autonomous driving programs of first-line auto OEMs.
Widely Recognized Timeliness, Accuracy and Robustness
FPGA is used to realize flexible deep learning network, which has low power consumption, high stability and reliability, high object detection rate, low false detection rate, and has realized accurate detection of various targets.
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Support Autonomous Driving at Different Levels
With front-view and multi-camera surround-view visual perception based on deep learning and advanced VSLAM technology, we provide L0-L2 advanced driving assistance systems, L3/L4 Auto Parking Assist (APA) / Auto Valet Parking (AVP) and high-level Autonomous driving.
Provide Efficient and Convenient Localized Service
Rooted in the Chinese market, we are committed to introducing internationally advanced artificial intelligence technologies to China for industrial applications, and is always listening to the needs of Chinese automobile customers, and responding to market changes quickly so as to help our customers launch more competitive product solutions timely for win-win cooperation.

Support Autonomous Driving at Different Levels

Support L0-L2 advanced driving assistance system (ADAS) based on monocular vision deep learning, which can provide LDW, FCW, PCW, SLI and other functions.Support L3/L4 Auto Parking Assist (APA) / Auto Valet Parking (AVP) based on surround-view vision and deep learning, which can identify the surrounding environment of the car in real-time and output information such as detected vehicles, pedestrians, obstacles, parking spaces, traffic lanes, etc.Support L3-L4 high-level Autonomous driving scheme based on deep learning and visual SLAM. The world’s top visual perception, fusion, positioning, path planning and control algorithms are implemented on the embedded low-power-consumption chip in line with specifications and standards for automobiles to achieve Autonomous driving under different scenes and conditions.
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