The problem with this approach is that to achieve the “necessary accuracy” automotive engineers have historically built high-fidelity models from first principles, which for complex systems can take a long time to build and simulate. In the controls field, this is called the “plant model ” in wireless vehicle communications, it is called “channel model.” In the reinforcement learning field, it’s called the “environment model.” Whatever you call it, the idea is the same: create a simulation-based model that gives you the necessary accuracy to recreate the physical system your algorithms interact with. When designing algorithms that interact with physical systems, such as an algorithm to control a hydraulic valve, simulation-based modeling of the system is key to enabling rapid design iteration for your algorithms. Challenge 2: Approximating Complex Systems with AI The combination of Simulink and MATLAB enables engineers to simulate data in the same environment that they build their AI models, meaning they can automate more of the process and not have to worry about switching toolchains. Using industry tools such as Simulink and Simscape, automotive engineers can generate simulated data that mirrors real-world scenarios. With a model’s performance dependent on the quality of the data it is being trained with, automotive engineers can improve outcomes with an iterative process of simulating data, updating an AI model, observing what conditions it cannot predict well, and collecting more simulated data for those conditions. Simulation gives access to internal states that might not be measured in an experimental setup, which can be very useful when debugging why an AI model doesn’t perform well in certain situations-including testing the viability of models predicting nonlinear values like NOx (nitric oxide and nitrogen dioxide) emissions.The automotive engineer has full control over the environment and can simulate scenarios that are difficult or too dangerous to create in the real world-such as emergency braking on icy roads or near-miss collision navigation with autonomous vehicles.Computational simulation is in general much less costly than physical on-vehicle experiments.The use of simulation to augment existing training data has multiple benefits: Rather than spending all a project’s time tweaking the AI model’s architecture and parameters, it has been shown that time spent improving the training data can often yield larger improvements in accuracy. In recent years, data-centric AI has brought the AI community’s focus to the importance of training data. Simulation can help automotive engineers overcome these challenges. ‘Bad’ data can leave an automotive engineer spending hours trying to determine why the model is not working, without the promise of insightful results. Projects are more likely to fail without robust data to help train a model, making data preparation a crucial step in the AI workflow. Engineers also must be mindful of the fact that while most AI models are static (they run using fixed parameter values), they are constantly exposed to new data and that data might not necessarily be captured in the training set. The process of collecting real-world data and creating good, clean, and cataloged data is difficult and time-consuming, particularly in the automotive field. Challenge 1: Data for Training and Validating AI Models The third is the use of AI models in embedded systems for applications such as controls, signal processing and embedded vision, where simulation has become a key part of the design process.Īs automotive engineers are finding new ways to develop more effective AI models, this piece will explore how simulation and AI combine to solve challenges of time, model reliability and data quality. The second is the use of AI models as approximations for complex high-fidelity simulations that are computationally expensive, also referred to as reduced-order modeling. The first has to do with addressing the challenge of insufficient data, as simulation models can be used to synthesize data that might be difficult or expensive to collect. At a high level, there are three key points of interaction between AI and simulation in the automotive industry.
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