This project is used for advanced driver assistance systems (ADAS)automatic driving. At present, our project will use a more sensitive CMOS single photon detector array to reduce the number of photons required for detection to less than ten, in order to break through the cost barriers of applications of LiDARestablish low-cost, high-performance LiDAR modules.

In this work, we demonstrate a dual multiplication layers In0.52Al0.48As based avalanche photodiode, which is desired for ToF lidarFMCW lidar application due to its high performances in both Geiger modelinear mode operations. By combining the specially designed mesa shape with dual M-layer structure, we can achieve high single photon detection efficiencyneat temporal characteristic of 65ps . On the other hand, we can also achieve high gain-bandwidth product ,high saturation currenthigh photo-generated RF power under 0.9 Vbr at the same time.

Integrating deep learning, 3D information analysis, hyperspectral analysis, computer vision analysiscombined the multi-source images to develop fruit quality monitoring techniques, including: planting area monitoring, position monitoring, yield monitoring, harvesting time prediction, fruit maturity measurement, fruit quality testing, to achieve the goal of enhancing industrial value.

In this study, the embedded LiDARAI recognition of smart headlight for autonomous driving is demonstrated. At driving process, the vehicle can provide artificial intelligence to identify the type, distancespeed of the object to be measured based on the fusion data of the LiDAR point cloudoptical image of the environment,then feedback to the headlight control system. Finally, this smart headlight would perform the suitable lighting mode during the daynightfit the automotive lamp safety regulationsstandards (ECE R112SAE J3069).

In autonomous driving, LiDAR can clearly distinguish the objectsget the object information such as speeddistance at medium-range (~100 m) for autonomous driving scheme entering Level 3. Therefore, in this study, we combine the reflective optical phased array (OPA) chip1550-nm VCSELs with a photodetector, a ToF chip,a microcontroller to produce an eye-safe, all-solid-state,low cost (less than $100 USD) LiDAR module, which is the world＇s leading LiDAR technologymore cost-effective than the current products for autonomous vehicle market.

NCREE has originally developed 5D digital space—on the basis of 3D city models and connections of different kinds of sensors around the world—is an online to offline virtual space with a combination of rising 5G technology advantages. Collecting and reorganizing various 3D cartographic data with Building Information Modeling (BIM), satellite imagery, UAV 3D modeling with high-resolution cameras, LiDar point cloud data, etc., can increase the diversity of the building and landscape to make the city more realistic. 5D+5G smart city platform speeds up all aspects of value-add applications of smart cities, including Intelligent Transportation System (ITS), Smart Energy (SE), and Ambient Intelligent (AmI). We expect that the 5D smart city becomes the digital twin of a real city.

Artificial intelligence 3D sensing image processing system based on array sensing Lidar aims to construct 3D image with high-quality immersion for AR/VR. The developed 3D scene recording system is based on the color camerahigh-accuracy chaotic LiDAR. The chaotic Lidar with APD arrayTOF sensors supports millimeter-accuracyinterference-avoiding capability within 100 meter in both indooroutdoor environments. High-performance embedded CNN processor supports high-throughput, high energy-efficient,low DRAM bandwidth computations for various image AI applications.

High-accuracy 3D sensingAI image processing system for constructing high-quality immersion 3D image for AR/VR. Chaotic Lidar with APD arrayTOF sensors supports millimeter-accuracyinterference-avoiding capability. High-performance CNN processor supports high-performancelow DRAM bandwidth computations for various image AI applications.