VueX

AI development for spatial recognition using LiDAR requires dispersed tools and professional coding for data collection, precise labeling, model learning, and on-site equipment application. This process is complex and costly.

As a result, developing LiDAR AI requires significant investment of specialized personnel and time, making it difficult for small businesses to access. VueX solves this problem by combining these fragmented workflows into one.

Provides LiDAR data refinement/labeling, GPU learning, and model deployment as a single automated pipeline on a cloud-based platform. It reduces LiDAR point cloud data labeling time by up to 95% and enables rapid iterative verification with 'one-click' learning and deployment.

The coding-free GUI environment lowers the barrier to entry, making it accessible even to non-AI experts. With support for multiple LiDAR formats, real-time simulation/security functions, it simplifies the previously extensive LiDAR pre-processing and enables non-experts to develop LiDAR AI.

B2B companies and research institutions in LiDAR application fields such as autonomous vehicles, logistics robots, drones, and smart cities. Major customer base includes finished vehicle manufacturers, ADAS developers, and smart factory solution companies. (B2B-focused, with opportunities for policy/public smart city projects (B2G).)

Applicable to almost all industries using LiDAR, including drones, robots, and logistics automation, in addition to automobiles. English support and overseas partnerships are being pursued to respond to the global market.

Not significantly influenced by specific countries or regulations, but must comply with related regulations such as autonomous driving safety standards. Coupled with the declining price trend of LiDAR equipment, there is great scalability to small and medium-sized enterprises.

A CES award winner in the vehicle technology category, the "LiDAR AI development simplification" concept was highly evaluated. The level of technical completeness is relatively high, and there are some delivery results to global customers.

Market expectations are high, linked to the growth of the autonomous driving market. However, the need for cloud infrastructure and the reliability of AI models (data bias issues) are pointed out as challenges. Currently,"market verification is in progress"stage, but overall positive.