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AI simulation systems can accelerate autonomous development

Industrial vehicles are becoming increasingly intelligent. This trend, fueled by a need for greener, safer and more efficient job sites, will continue to grow in the next decades.

Currently, vehicles and robots seen in industrial areas are usually carrying out simple, independent tasks. But their complexity is increasing rapidly. To keep up with the pace of innovation, there is a greater need for higher-quality data. Following the traditional method of simply augmenting existing data sets can only take you so far. Scalability is hindered, which has a direct effect on innovation. For example, dealing with corner scenarios such as a tunnel collapse, simulating millions of scenarios with a few clicks, or testing a new lidar model on a non-existent prototype might become very challenging or impossible to implement. In short, data processing can be laborious, impractical and costly – sometimes all of them at the same time.

This is where simulation and synthetic data combined with machine learning can assist AI development. Generating digital twins with correct physical properties and an automated simulation pipeline process can help solve all these problems at a better cost-to-efficiency ratio than current methods. Unfortunately, simulations created with industry-specific needs in mind are currently non-existent or simply incapable of supporting intensive AI/deep learning model development and software testing.

That is why AILiveSim has developed a scalable simulation platform and services that accelerate overall AI and autonomous development. The platform is specifically designed for training, testing and validation systems, either based on traditional algorithms or machine learning adapted to users’ current and future needs. The combination of powerful procedural content and an algorithmic exploration feature reduces manual work and costs while getting companies to market faster with confidence in the AI they deliver.

Booth: IVT5

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