Accurately detecting and identifying drivers' braking intention is the basis of man-machine driving. In this paper, we proposed an electroencephalographic (EEG)-based braking intention measurement strategy. We used the Car Learning to Act (Carla) platform to build the simulated driving environment. 11 subjects participated in our study, and each subject drove a simulated vehicle to complete emergency braking and normal braking tasks. We compared the EEG topographic maps in different braking situations and used three different classifiers to predict the subjects' braking intention through EEG signals. The experimental results showed that the average response time of subjects in emergency braking was 762 ms; emergency braking and no braking can be well distinguished, while normal braking and no braking were not easy to be classified; for the two different types of braking, emergency braking and normal braking had obvious differences in EEG topographic maps, and the classification results also showed that the two were highly distinguishable. This study provides a user-centered driver-assistance system and a good framework to combine with advanced shared control algorithms, which has the potential to be applied to achieve a more friendly interaction between the driver and vehicle in real driving environment.
翻译:在本文中,我们提出了一个基于电脑摄影(EEG)的制动意图测量战略。我们使用汽车学习到动作(Carla)平台来建立模拟驾驶环境。我们的研究有11个主题参与其中,每个主题驱动模拟车辆完成紧急制动和正常制动任务。我们在不同制动情况下比较了EEEG地形图,并使用三个不同的分类器通过EEEG信号来预测对象的制动意图。实验结果表明,紧急制动中主体的平均反应时间为762米;应急制动和不制动可以很好区分,而正常制动和不制动则不容易分类;两种不同类型的制动、紧急制动和正常制动在EEG地形图中有着明显的差异,分类结果还表明这两种类型都非常可辨别。本研究提供了一个以用户为中心的驾驶员协助系统和良好的框架,可以与先进的共同控制算法相结合,这些算法有可能在驱动器和驱动器之间实现更友好的互动。