### Road Friction Estimation using AI and Digital Twin Simulation

How do you evaluate your guessing ability when you do not have the correct answer to compare to? The problem is very difficult to answer, so instead of answering it, can we change the problem? Looking at the complex problem of friction estimation for vehicles from a programming and data science approach, can we replace the existing problems with others that might be easier to solve, and if so, how well does that work?

A process using simulation and a neural network has been developed to investigate the possible benefits of using a completely digital approach to the friction estimation problem. Using simulation of vehicle dynamics, the process has removed errors that are very difficult to eliminate in the real world, but has also created problems that didn’t exist before, for example a mismatch between the real car and the simulated car.

So why do we need to improve friction estimation? Several problems exist with the development and testing process, read more about them in the earlier post!

In order to solve – or at least circumvent – those problems, a digital vehicle model was created in a simulation environment created using Unity 3D. The digital vehicle is based on vehicle data from NEVS, and can be driven around and tested for any friction coefficient in any situation from ordinary driving to extreme manoeuvres, and the friction is always known.

When it comes to making an estimate, the friction estimation methods that are popular today are extremely complex and are based on complicated modelling of tyres with an immense amount of dynamic variables.

The purpose of the methods is to find a connection between the input variables and the friction coefficient using a complex physically derived connection. To circumvent the complexity, the “Universal approximation theorem” was instead used to find a purely mathematical connection between the inputs and the friction coefficient, by asking a different question.

A multilayer perceptron type of neural network with two hidden layers was used as it can approximate functions proven by the theorem, and can relatively quick give an insight into the performance of the process for evaluation. The neural network model was used to classify the sensor readings into 1 out of 4 categories of friction, from ice to dry asphalt.

In an optimal scenario, where the neural network has been trained on driving scenarios that are similar to the scenarios the neural network is evaluated on and little model mismatch, the network reached an estimation accuracy of 94.3%. However, when the network was evaluated on driving scenarios that were dissimilar to the training scenarios, and using a lot of model mismatch, it estimated 37.4% correctly.

The conclusion from the results being that the digitalisation process can be highly beneficial if used in a way where the difference between physical and digital vehicle is minimised and the model is trained on scenarios it will be exposed to. If the process is used in an adverse way only a set of additional errors has been introduced.

This blog post is based on a thesis that Jonas Karlsson has done at Combine in collaboration with NEVS in the fall of 2019. The full report will be published shortly and will be publicly available to interested parties.