Cao Yu, Zhang Bo, Hou Xiaohui, Gan Minggang, Wu Wei
School of Automation, Beijing Institute of Technology, Beijing 100081, China.
National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2025 Jan 10;25(2):397. doi: 10.3390/s25020397.
Existing autonomous driving systems face challenges in accurately capturing drivers' cognitive states, often resulting in decisions misaligned with drivers' intentions. To address this limitation, this study introduces a pioneering human-centric spatial cognition detecting system based on drivers' electroencephalogram (EEG) signals. Unlike conventional EEG-based systems that focus on intention recognition or hazard perception, the proposed system can further extract drivers' spatial cognition across two dimensions: relative distance and relative orientation. It consists of two components: EEG signal preprocessing and spatial cognition decoding, enabling the autonomous driving system to make more contextually aligned decisions regarding the targets drivers focus on. To enhance the detection accuracy of drivers' spatial cognition, we designed a novel EEG signal decoding method called a Dual-Time-Feature Network (DTFNet). This approach integrates coarse-grained and fine-grained temporal features of EEG signals across different scales and incorporates a Squeeze-and-Excitation module to evaluate the importance of electrodes. The DTFNet outperforms existing methods, achieving 65.67% and 50.65% accuracy in three-class tasks and 84.46% and 70.50% in binary tasks. Furthermore, we investigated the temporal dynamics of drivers' spatial cognition and observed that drivers' perception of relative distance occurs slightly later than their perception of relative orientation, providing valuable insights into the temporal aspects of cognitive processing.
现有的自动驾驶系统在准确捕捉驾驶员的认知状态方面面临挑战，这往往导致决策与驾驶员的意图不一致。为解决这一局限性，本研究引入了一种基于驾驶员脑电图（EEG）信号的开创性的以人类为中心的空间认知检测系统。与专注于意图识别或危险感知的传统基于EEG的系统不同，所提出的系统能够在两个维度上进一步提取驾驶员的空间认知：相对距离和相对方向。它由两个部分组成：EEG信号预处理和空间认知解码，使自动驾驶系统能够针对驾驶员关注的目标做出更符合情境的决策。为提高驾驶员空间认知的检测准确性，我们设计了一种名为双时特征网络（DTFNet）的新型EEG信号解码方法。这种方法整合了不同尺度上EEG信号的粗粒度和细粒度时间特征，并结合了挤压与激励模块来评估电极的重要性。DTFNet优于现有方法，在三类任务中准确率达到65.67%和50.65%，在二类任务中准确率达到84.46%和70.50%。此外，我们研究了驾驶员空间认知的时间动态，发现驾驶员对相对距离的感知比对相对方向的感知稍晚出现，这为认知处理的时间方面提供了有价值的见解。
