Driver-Intention Prediction with Deep Learning: Real-Time Brain-to-Vehicle Communication

TL;DR

This paper introduces a deep learning approach using CNNs to predict driver steering intentions from EEG signals in real-time, enabling more intuitive brain-to-vehicle communication systems.

Contribution

It presents a novel CNN-based method for classifying driver intentions from EEG data with high accuracy in a simulated environment.

Findings

Achieved 83.7% accuracy in intention classification

CNN effectively processes raw EEG data with minimal pre-processing

Higher accuracy observed for right-turn intentions

Abstract

Brain-computer interfaces (BCIs) allow direct communication between the brain and electronics without the need for speech or physical movement. Such interfaces can be particularly beneficial in applications requiring rapid response times, such as driving, where a vehicle's advanced driving assistance systems could benefit from immediate understanding of a driver's intentions. This study presents a novel method for predicting a driver's intention to steer using electroencephalography (EEG) signals through deep learning. A driving simulator created a controlled environment in which participants imagined controlling a vehicle during various driving scenarios, including left and right turns, as well as straight driving. A convolutional neural network (CNN) classified the detected EEG data with minimal pre-processing. Our model achieved an accuracy of 83.7% in distinguishing between the three steering intentions and demonstrated the ability of CNNs to process raw EEG data effectively. The classification accuracy was highest for right-turn segments, which suggests a potential spatial bias in brain activity. This study lays the foundation for more intuitive brain-to-vehicle communication systems.