These button presses created the ground-truth labels we need to train a supervised learning model. She released the button when the driver indicated the car had returned to handling normally. In addition, we logged the driver’s perception of oversteering: When the driver indicated the car was oversteering, my colleague, riding in the car as a passenger, pressed a button on her laptop. ![]() With the help of a professional driver, we conducted live driving tests in a BMW M4 at the BMW proving grounds in Miramas, France (Figure 2).ĭuring the tests, we captured signals commonly used in oversteer detection algorithms: the vehicle’s forward acceleration, lateral acceleration, steering angle, and yaw rate. We began by gathering real-world data from a vehicle before, during, and after oversteering. Despite having little previous experience with machine learning, in just three weeks we completed a working ECU prototype capable of detecting oversteering with over 98% accuracy. Working in MATLAB ®, we developed a supervised machine learning model as a proof of concept. A car with underinflated tires on an icy road might need vastly different threshold values than the same car operating with properly inflated tires on a dry surface.Īt BMW, we are exploring a machine learning approach to detecting oversteering. ![]() In practice, however, this approach has proven difficult to implement because of the interplay of the many factors involved. For example, when measurements from onboard sensors exceed established threshold values for parameters in the model, the system determines that the car is oversteering. In theory, such systems can identify an oversteering condition by using mathematical models based on first principles. Modern stability control systems are designed to automatically take corrective action when oversteer is detected.
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