Analyzing driving behavior for fuel efficiency using ECU data based on artificial intelligence

In this study, a comprehensive analysis of driving behavior with an emphasis on fuel consumption and driver categorization is presented. Data from 80 drivers were collected using a custom-designed datalogger connected to the vehicle’s On-Board Diagnostics (OBD) port. Critical features related to driving patterns were extracted through a correlation matrix and concepts in the field of powertrains. Key variables such as acceleration and deceleration were identified and derived. Regression models were applied to predict fuel consumption based on this driving feature. Through this analysis, the most influential factors affecting fuel efficiency were highlighted. Additionally, unsupervised machine learning techniques were employed to cluster drivers into distinct groups based on their driving styles. A comparative study of various algorithms was conducted to evaluate the efficacy of different clustering methods. Valuable insights for automotive manufacturers, policymakers, and drivers are offered by the results, emphasizing the role of driving behavior in fuel efficiency and the potential for tailored driver assistance systems.