Driver Intention

Driver intention prediction aims to anticipate a driver's actions, such as turning or braking, using various data sources like EEG signals, in-cabin and external cameras, and even fleet-wide parking data. Current research focuses on developing robust machine learning models, including transformers, LSTMs, and SVMs, to process these diverse data types and accurately predict intentions, often incorporating techniques like self-supervision and attention mechanisms to improve performance and interpretability. This research is crucial for enhancing road safety through advanced driver-assistance systems and autonomous vehicle development, as well as improving the efficiency of transportation systems and assistive robotics.