Environment Classification Using Machine Learning Methods for Eco-Driving Strategies in Intelligent Vehicles

Applied Sciences

Jose del C. Julio-Rodríguez¹  Carlos A. Rojas-Ruiz²  M. Rogelio Bustamante-Bello²  Alfredo Santana-Díaz²  Ricardo A. Ramirez-Mendoza² 

[1] Faculty of Mechanical Engineering, RWTH Aachen University, 52074 Aachen, Germany;School of Engineering and Sciences, Tecnologico de Monterrey, Toluca 50110, Mexico;

关键词: electric vehicles;    driving environment classification;    machine learning;    electromobility;    energy consumption;   

DOI  :  10.3390/app12115578

来源: DOAJ

【 摘 要 】

This work presents the development of a classification method that can contribute to precise and increased awareness of the situational context of vehicles, for it to be used in autonomous driving applications. This work aims to obtain a method for machine-learning-based driving environment classification that does not involve computer vision but instead makes use of dynamics variables from Inertial-Measurement-Unit (IMU) sensors and instantaneous energy consumption measurements. This article includes details about the data acquisition, the electric vehicle used for the experiments, and the pre-processing methods employed. This explores the viability of a method for classifying a vehicle’s driving environment. The results of such a system can potentially be used to provide precise information for path planning, energy optimization, or safety purposes. Information about the driving context could be also used to decide if the conditions are safe for autonomous driving or if human intervention is recommended or required. In this work, the feature selection process and statistical data pre-processing methods are evaluated. The pre-processed data are used to compare 13 different classification algorithms and then the best three are selected for further testing and data dimensionality reduction. Two approaches for feature selection based on feature importance and final classification scores are tested, achieving a classification mean accuracy of 93 percent with a real testing dataset that included three driving scenarios and eight different drivers. The obtained results and high classification accuracy represent a first approach for the further development of such classification systems and the potential for direct implementation into autonomous driving technology.

【 授权许可】

Unknown