Accelerate Test Times with AI
Table of Contents
- Introduction
- Background of Monolith AI
- The Use Case: Track testing with Machine Learning
- Partnership with Kistler
- The Power of Machine Learning in Testing
- Benefits of Plausibility Modeling
- The Monolith Platform and Dashboard
- Predicting Test Results with Machine Learning
- Predicting Trends and Highlighting Uncertainties
- Cost and Time Savings
- Conclusion
Article
Introduction
👋 Welcome! I'm excited to tell you about Monolith AI and how they have leveraged machine learning in track testing for autonomous driving. In this article, we'll dive into the partnership between Monolith AI and Kistler, the power of machine learning in testing, the benefits of plausibility modeling, and the incredible results achieved through the Monolith platform.
Background of Monolith AI
Monolith AI is a company that focuses on machine learning and its applications in various industries. Their main objective is to bring the benefits of autonomous driving technologies to the research and development (R&D) process. By leveraging machine learning, Monolith AI aims to accelerate decision-making and improve efficiency in R&D.
The Use Case: Track Testing with Machine Learning
One specific use case that Monolith AI has been working on is track testing. Track testing is a crucial part of the development process for autonomous vehicles. It involves conducting various tests in different scenarios, such as extreme weather conditions or high-speed situations. However, track testing can be time-consuming and expensive.
Partnership with Kistler
Monolith AI partnered with Kistler, a German-Swiss company specializing in test equipment, to tackle the challenge of track testing. Kistler provided Monolith AI with a wealth of test data from different tracks and scenarios. Together with a team of automotive engineers from the UK and test engineers from Kistler, they set out to develop a tool that could reduce the time and cost of track testing.
The Power of Machine Learning in Testing
Machine learning proved to be a Game-changer in this project. By training machine learning models on the test data, Monolith AI was able to recognize complex scenarios and predict the forces on the tires based on the driver's input and sensor data. This approach not only reduced the amount of time spent on testing but also provided accurate and reliable results.
Benefits of Plausibility Modeling
One of the key benefits of using machine learning in track testing is the ability to build plausibility models. These models have a rough understanding of the physics involved and can predict the rest of the test data based on a small proportion of the data. Plausibility modeling allows engineers to quickly identify any mistakes or inconsistencies in the test data, saving time and effort in the debugging process.
The Monolith Platform and Dashboard
Monolith AI developed a platform called Monolith Live, which provides a fully no-code environment for building machine learning solutions. The platform uses a notebook workflow, allowing engineers to build and compile their solutions into applications that can be easily shared and used by others. The dashboard in Monolith Live allows engineers to analyze and Visualize the test data, making it easier to understand and interpret the results.
Predicting Test Results with Machine Learning
Using the machine learning models developed on the Monolith platform, engineers could predict test results based on a small set of generic driving tests. This approach enabled them to extend the virtual test time and make informed decisions without the need for extensive physical testing. The predictions were accurate enough to capture the trends and behaviors of the vehicle in different scenarios.
Predicting Trends and Highlighting Uncertainties
The machine learning models not only predicted test results but also highlighted uncertainties. When encountering a new test environment or Scenario, the models would indicate areas where they had not seen similar data before, indicating a higher level of uncertainty. This information was valuable in identifying potential limitations of the models and areas that required further physical testing.
Cost and Time Savings
By leveraging machine learning in track testing, Monolith AI and Kistler were able to achieve remarkable cost and time savings. After analyzing the first 20% of the tests, they were able to predict the results of the remaining tests with an accuracy of 72%. This meant that 8 out of 11 test days were no longer necessary, resulting in significant cost reductions and accelerated development timelines.
Conclusion
In conclusion, the collaboration between Monolith AI and Kistler in track testing with machine learning has demonstrated the immense potential of this technology in the automotive industry. By using machine learning models to predict test results and identify uncertainties, engineers can significantly reduce the time and cost of track testing. The Monolith platform offers a user-friendly environment to build and deploy machine learning solutions, making it accessible to a wide range of users.
So why not take a look at Monolith AI and see how their machine learning platform can revolutionize the way you conduct track testing and accelerate your development process?
Highlights
- Monolith AI leverages machine learning to accelerate decision-making in R&D.
- The partnership between Monolith AI and Kistler aims to reduce the time and cost of track testing for autonomous vehicles.
- Machine learning models can predict test results and identify uncertainties, saving valuable time and resources.
- The Monolith platform offers a user-friendly environment for building and deploying machine learning solutions.
- By using machine learning, engineers can achieve significant cost and time savings in track testing.
- Plausibility modeling helps identify mistakes and inconsistencies in the test data, improving accuracy and reliability.
- Machine learning models provide confidence intervals and can help debug failures in testing.
- Monolith AI's track testing case study demonstrates the power of machine learning in predicting trends and highlighting uncertainties.
- Monolith AI's platform, Monolith Live, enables engineers to analyze and visualize test data easily through a no-code environment.
- By leveraging machine learning, engineers can extend virtual test time and make informed decisions without extensive physical testing.
FAQ
Q: What is Monolith AI?
A: Monolith AI is a company specializing in machine learning and its applications in various industries, with a focus on accelerating decision-making in R&D.
Q: How does machine learning help in track testing?
A: Machine learning models can predict test results based on a small set of driving tests, reducing the need for extensive physical testing and accelerating the development process.
Q: What are the benefits of plausibility modeling?
A: Plausibility modeling allows engineers to quickly identify mistakes and inconsistencies in the test data, saving time and effort in the debugging process.
Q: How does Monolith Live help in track testing?
A: Monolith Live is a platform developed by Monolith AI that provides a no-code environment for building machine learning solutions. It offers a dashboard for analyzing and visualizing test data, making it easier to understand and interpret the results.
Q: What are the cost and time savings achieved through machine learning in track testing?
A: By leveraging machine learning, engineers can predict test results and identify uncertainties, resulting in significant cost reductions and accelerated development timelines. In the case study, 72% of tests were accurately predicted, leading to 8 out of 11 test days being unnecessary.
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