近年来，脑机接口的迅速发展增加了该项技术的实际应用，如何在现实场景 中应用脑机接口技术成为研究的热点话题。道路交通运输业一直是人类经济发展 的重要支柱之一，主动脑机接口技术和被动脑机接口技术在不同的道路交通运输 领域逐渐发挥越来越重要的作用。将脑机接口技术实际运用到交通驾驶领域，能够增加脑机接口技术的实用性， 并能解决一些交通驾驶领域的难题。本文从两个方面展开研究:一方面是被动式 脑机接口技术，通过检测脑电信号的波动，来识别疲劳状态，疲劳驾驶一直是一 种危险的交通驾驶行为，现阶段已有从多个方面针对疲劳驾驶检测的研究，本文 以耳后脑电为研究手段，通过采集一定人群的疲劳驾驶脑电数据，分析特征并且 建立疲劳分类模型，并搭建一套疲劳驾驶在线检测系统。另一方面为了缓解人在 道路交通驾驶中产生的不利因素，设计出一套结合自动驾驶技术精细化控制和脑 机接口指令的驾驶系统，实现人发出任务型指令，而机器自动执行指令，降低人 为误操作和不良驾驶习惯的影响。同时，为了不干扰驾驶者的视线和不阻挡驾驶 者活动，将 SSVEP 刺激呈现在 AR 眼睛上，增加系统的实用性。本论文工作立足于从主动式和被动式脑机接口技术两个方面展开了研究，开 发出一套耳后脑电疲劳驾驶检测系统，分析疲劳驾驶在耳后脑电的特征;也提出 了基于 AR 现实的 SSVEP-BCI 驾驶控制系统，为将脑机接口技术应用于道路交通 运输领域提供了有益探索。

In recent years, the rapid development of the Brain-Computer Interface has increased the practical application of this technology. Applying brain-computer interface technology in natural scenes has become a hot topic of research. The road transportation industry has always been one of the essential pillars of human economic development. Active brain-computer interface (A-BCI) and passive brain-computer interface (P-BCI) is playing more and more critical roles in different road transportation fields.The practical application of brain-computer interface technology in traffic driving can increase the practicability of brain-computer interface technology and solve some complex problems in the field of road transportation. This paper carries out research from two aspects: One is passive brain-computer interface technology, detection of brain electrical signals to identify the state of fatigue. Fatigued driving has always been a dangerous traffic-driving behavior. At the present stage, from multiple aspects in view of the fatigue test of the existing research, this article, by means of behind-ear EEG signals as the research through a population of fatigue driving EEG data, The characteristics were analyzed, ed and the fatigue classification model was established, and a set of fatigue driving online detection system was built. On the other hand, to alleviate the adverse factors caused by human driving in road traffic, a driving system combining fine control of automated driving technology and brain-computer interface instruction is designed to realize human issuing task-based instructions and machine executing instructions automatically, reducing the influence of human misoperation and bad driving habits. At the same time, in order not to disturb the driver's line of sight and not block the driver's activities, SSVEP stimulation is presented on the AR devices, increasing the practicality of the system.Based on active and passive brain-computer interface technology research, this thesis developed a set of behind-the-ear EEG fatigue driving detection systems to analyze the characteristics of behind-the-ear EEG fatigue driving. The SSVEP-BCI driving control system based on AR reality is also proposed, which provides beneficial exploration for the application of brain-computer interface technology in road transportation.