CNN LSTM DRIVING STYLE CLASSIFICATION MODL BASED ON DRIVER OPERATION TIME SERIES DATA

Project Code :TCMAPY1203

Objective

The objective of this study is to develop a robust and accurate classification model for driving styles using a CNN-LSTM architecture. By leveraging the strengths of CNNs in capturing spatial features and LSTMs in capturing temporal patterns, the model aims to accurately classify diverse driving behaviors. The study aims to enhance automotive safety and efficiency by providing insights into driver behavior and enabling the development of intelligent driver assistance systems.

Abstract

This study proposes a CNN-LSTM model for classifying driving styles using time series data from driver operations, leveraging the strengths of both CNNs and LSTMs for effective feature extraction and temporal pattern recognition. Experimental results demonstrate the model's superior classification accuracy and robustness compared to existing methods. Visualization techniques enhance interpretability, offering insights into features influencing driving style classification.

Keywords: CNN-LSTM, auto motive safety, driver behavior analysis, and driving style categorization.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.