Car Drift Recognition Using Machine Learning
Tobias Freudling, BMW Group
In this presentation, you’ll see a demonstration of the automatic classification of vehicle data, which results from different driving maneuvers and aims to predict the oversteering of a vehicle. A comparison between a classical implementation and the machine learning approach is established. To date, certain thresholds have been defined in the classical sense, which should signal an overshoot when this threshold is exceeded. In some cases, there are also speed-dependent sleepers determined by many years of experience. Thus, the question of how well and how quickly measurements can be characterized using machine learning methods also comes into question.
A model based on a classification algorithm in Statistics and Machine Learning Toolbox™ was trained with a focus on the concrete driving situation of the overdrive. For the data sets used for training, the prediction accuracy of the model is over 95%. In the next step, this model is then applied to new records. The first evaluations show promising results.
Recorded: 17 Apr 2018
You can also select a web site from the following list
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.