Evaluation of a Vehicle Acceleration Behavior through Decision Tree Learning

Evaluation of a Vehicle Acceleration Behavior through Decision Tree Learning

Bucak, İhsan Ömür

The faster that a motor vehicle can accelerate to a high velocity is crucial to its performance and handling. The acceleration of the vehicle is important to know because it tells us how the car handles during merging and evasive maneuvering. Decision trees are powerful and popular tools for classification and prediction. The attractiveness of decision trees is due to the fact that, in contrast to neural networks, decision trees represent rules. Rules can readily be expressed so that humans can understand them after a brief explanation. Therefore, the objective of this paper is to develop a systematic method using decision trees of machine learning to evaluate acceleration behavior of motor vehicles based on the forces acting on the vehicle, i.e. vehicle dynamics.

Conference or Workshop Item
PeerReviewed

2009-06

518