Solutions by industry and area

In-vehicle

We provide optimal GPU configurations and technical support tailored to the unique AI application needs of the automotive industry.

Challenges of AI implementation in the automotive industry

In developing in-vehicle AI, including autonomous driving and ADAS, companies face a wide range of challenges, such as processing massive amounts of sensor data from cameras and LiDAR, achieving high-precision, real-time inference performance, building simulation environments, and developing assets. Given these challenges, the ability to build an AI platform that encompasses development, verification, and mass production is a crucial factor determining competitiveness.

  • Establishing a learning platform that integrates real-world and virtual data.

  • Utilization of sensor data processing and simulation environments

  • Realizing end-to-end AI that understands diverse worlds

  • Building a secure AI infrastructure

Advanced in-vehicle AI enabled by NVIDIA technology

By combining NVIDIA's GPUs, AI software, and simulation technologies, we will build a foundation optimized for in-vehicle AI development.
It accelerates the entire process from data generation to training and validation, simultaneously improving accuracy and streamlining development.

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Expansion of the learning data infrastructure

By leveraging NVIDIA Omniverse™ and NVIDIA Cosmos™, we build a learning environment that combines real-world data with synthetic data generated in virtual space. This allows for the efficient generation and expansion of rare cases and diverse driving scenarios in virtual space, realizing a scalable learning data foundation that is not solely dependent on the real world.

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3D spatial reconstruction

NVIDIA Omniverse NuRec reconstructs real-world driving data from cameras, LiDAR, and other sources as a high-fidelity three-dimensional space. By recreating a near-realistic environment as a digital twin, it converts the data into a format that can be directly used for training and validating AI models, thereby supporting improved development accuracy.

Image of speeding up the development loop

Speeding up the development loop

With Omniverse at its core, we build a development loop that enables end-to-end execution of data generation, simulation, learning, and evaluation. By continuously improving AI models while moving between the real world and virtual space, we simultaneously achieve faster and more efficient development cycles.

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Enhanced End-to-End Model

Leveraging NVIDIA Alpamayo, we enable end-to-end AI learning based on a global understanding. By utilizing rich data generated in a virtual space, we support improvements in model accuracy and generalization performance, enabling adaptation to diverse driving environments and unexpected scenarios.

Specific in-vehicle application scenarios

AI is increasingly being used in various business areas within the automotive industry. Below, we will introduce some actual use cases.

  • Driving scenario and rare case generation

  • Synthesis and expansion of sensor data

  • Advanced learning of end-to-end AI models

  • Automatic generation of simulation environments

  • Advanced in-car experience and HMI

  • AI chatbots that utilize manufacturing process knowledge.

Examples of generative AI solutions in the automotive industry

Here are some examples of NVIDIA solutions for AI implementation.
Through the challenges, solutions, and results, you will gain a concrete understanding of how AI can be used in the automotive industry.

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