用脑电波预测司机转向意图，实现脑控汽车的实时通信。
Driver-Intention Prediction with Deep Learning: Real-Time Brain-to-Vehicle Communication
- 通过卷积神经网络直接处理原始脑电信号，无需复杂预处理。
- 转向意图识别准确率达83.7%，右转识别效果最佳。
- 为未来自动驾驶中脑机直连交互提供技术基础，适合人机交互研究者。
脑机接口（BCI）可在无需言语或肢体动作的情况下实现大脑与电子设备的直接通信。在需要快速响应的驾驶场景中，车辆的高级驾驶辅助系统若能即时理解驾驶员意图，将显著提升安全性。本研究提出一种基于深度学习的脑电图（EEG）信号驱动的转向意图预测方法。研究在驾驶模拟器中构建受控环境，参与者在不同驾驶场景中想象操控车辆，包括左转、右转和直行。采用卷积神经网络（CNN）对采集的脑电信号进行分类，仅需极少预处理。模型在区分三种转向意图时达到83.7%的准确率，并表现出对右转段更高的识别性能，提示可能存在空间性脑活动偏倚。该研究为更直观的脑-车通信系统奠定了基础。
原文摘要 · Abstract (English)
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.
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