TIRE-ROAD FRICTION ESTIMATION USING MACHINE LEARNING – FOR VEHICLES OF TOMORROW
If you have driven a car in snowy and icy conditions you have probably performed a task called tire-road friction estimation without even thinking about it. Simply put, you have adjusted the corner speed or braking distance to compensate for the changes in friction you have observed with your eyes (or your feet when walking to the car). But in the future, when the car is autonomous, this knowledge about available friction needs to be observed by the car. The safety systems of today’s vehicles perform basic estimations of environmental conditions like tire-road friction and the systems can act on these estimations. But as long as the systems can rely on the driver whom can override the systems, these estimations can be seen as recommendations and the car always has a last resort in the driver. In an (arguable) near future, we expect autonomous vehicles to populate the roads and the recommendations suddenly become decisions. How shall we design algorithms to estimate the tire-road friction and how do we deal with the final fallback?
Today’s modern vehicles feature a large set of advanced driver-assistance systems (ADAS), such as electronic stability control, lane departure warning systems, anti-lock brakes, and several others. These systems are dependent on multiple inputs to model the current state of the vehicle as well as the environment, and one can argue that the vehicle’s interaction with the road is the most important input.
The tire-road friction is essential to the stability of the vehicle and have been found to be the most important factor in avoiding crashes. About a quarter of all crashes occur due to weather-related issues, and accidents are twice as likely to happen on wet asphalt compared to dry asphalt. There is however no way to accurately measure the available friction, so some type of estimation algorithm needs to be developed.
All of the systems undergo extensive testing and are required to be evaluated for a large number of test scenarios. However, this introduce two major issues.
First, real-world data can only be used to analyse what has already happened, thus there is no certainty about what would happen in an untested reality and there is an increased risk for unforeseen conditions and edge cases. It simply takes too much time to test enough driving cases.
Second, testing for a large set of scenarios is impractical as the actual value needs to be known in order to evaluate the system, which the value often is for the testing sites. For an environment where the actual friction is an estimate, i.e. a public road, the testing is prone to errors and limits the available testing sites with valid verification since otherwise the estimations are compared to other estimations. If the approach is to train a machine learning algorithm there is no reference value of a correct answer.
To overcome these issues, simulation has been proposed as a solution. By using a digitally controlled environment, the true value of the tire-road friction is known. Furthermore, simulation allows for controllability, reproducibility, and standardization as measurement errors and uncertainties can be both eliminated and introduced at will.
Simulation of high friction driving
Combine is currently doing a master thesis together with a major Swedish automotive company where we are investigating the possibility to digitalize the testing process. By using the generated simulation data, we will train a machine learning algorithm to estimate the tire-road friction. The master thesis is planned to be finalized by the end of the year, so stay tuned about the results.