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

2026-01-08

arXiv.org

AI Summary

This work proposes a real-time driving intention recognition method based on electroencephalography (EEG) to enhance human–vehicle collaboration in advanced driver assistance systems. Raw EEG signals were collected using a driving simulator, and for the first time, an end-to-end convolutional neural network (CNN) was employed to directly process the raw data—bypassing complex preprocessing—to classify three driving intentions: left turn, right turn, and straight ahead. Experimental results demonstrate an average classification accuracy of 83.7%, with the highest performance achieved for right-turn intention detection. These findings not only validate the feasibility of CNNs in brain–vehicle communication but also highlight the pronounced neural activity associated with right-turn decisions, offering a novel paradigm for intelligent driving systems that operate without physical interaction.

Technology Category

Cognitive Modeling & Cognitive Systems: Neural Spike CodingHumans and AI: Brain-Sensing and AnalysisIntelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

Responsible Web: Machine-in-the-loop, human agency and autonomyEconomics, Online Markets and Human Computation: The sharing economySystems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• driver-intention prediction
• brain-computer interface
• EEG
• real-time communication
• steering intention

Innovation

• Brain-computer interface
• Driver intention prediction
• Deep learning
• EEG
• Convolutional neural network