Announced details
The Company will hold an online seminar jointly with NVIDIA and MathWorks Japan. Capital and personnel relationships are Not disclosed.
NVIDIA Japan G.K. and MathWorks Japan. Other information, including their locations, representatives, capital and year of establishment, is Not disclosed.
High-definition 3D map data is surveyed and developed in 26 countries worldwide, including Japan, North America, Europe, South Korea and the Middle East
Participation fee: Free
The company does not address Contract Value / Impact on Business Performance.
| Item | Details |
|---|---|
| Names of Products / Services Covered | HD Map (high-definition 3D map data), Raw Scene Pack, MathWorks RoadRunner, NVIDIA Cosmos Transfer |
| Counterparty | NVIDIA Japan G.K., MathWorks Japan |
| Counterparty overview | NVIDIA Japan G.K. and MathWorks Japan. Other information, including their locations, representatives, capital and year of establishment, is Not disclosed. |
| Relationship with the Company | The Company will hold an online seminar jointly with NVIDIA and MathWorks Japan. Capital and personnel relationships are Not disclosed. |
| Offering price | Participation fee: Free |
| Launch date | Online seminar scheduled for 15 July 2026 (Wednesday), from 14:00 to 15:40 (JST) |
| Applicable area | Automated Operation, Advanced driver-assistance systems (ADAS), Physical AI, AI training, evaluation and validation, 3D simulation, Autonomous-driving algorithm development and validation |
| Location scale | High-definition 3D map data is surveyed and developed in 26 countries worldwide, including Japan, North America, Europe, South Korea and the Middle East |
Items this document does not state (10)
These are items the company does not state in this document. We do not leave them blank, nor fill them in from other sources or by inference.
The reasons the company gives
The company's own explanation, not something inferred from the numbers.The development of autonomous driving and ADAS is becoming more advanced and complex, increasing the importance of data used for AI training, evaluation and validation. In some use cases involving 3D simulation, the domain gap, or discrepancy from real-world images, presents a challenge.