Driver Preference

Driver preference research focuses on understanding and incorporating individual driving behaviors into route planning and vehicle systems. Current research utilizes machine learning, particularly graph neural networks, reinforcement learning, and recurrent neural networks (RNNs like LSTM and GRU), to analyze historical driving data, predict future behavior, and personalize routes or vehicle control parameters. This work aims to improve efficiency, safety, and user satisfaction in transportation, impacting both the design of autonomous vehicles and the development of more effective driver assistance systems. The ultimate goal is to create more personalized and optimized driving experiences tailored to individual needs and preferences.