International Journal of Science, Engineering and TechnologyAuthors: Dhanika T. Bhangale, Assistant Professor Namrata D. Ghuse
Abstract: The use of many sensors in vehicles allows us to study how road users interact. This is important for various applications in vehicle scenarios. In this context, we introduce a new training method called Sequential Training. This method divides the Neural Network (NN) layers of the Deep Neural Network (DNN) model into two sets. One set is tailored for the user, while the other is designed to work together, focusing on the road environment. We apply deep learning in situations where vehicle users, each with unique driving behaviors and styles, interact with their surroundings. We need to create specific models for each indi- vidual vehicle user in every environment. This process requires collecting relevant data to train the machine learning models. Such data collection can be expensive and, in many cases, may even be impossible. This approach aims to integrate dynamic road condition sensors, such as weather and real-time traffic data, to improve the adaptability of scenario-specific layers.
