Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles

Currently, the existing vehicle-centric semi-autonomous driving modules do not consider driver’s situation and emotions. In an autonomous environment, when changing to manual driving, human–machine interface advanced driver assistance systems (ADAS) are essential assist vehicle driving. This study proposes a that considers emotions enhance ADAS. A 1D convolutional neural network model based on multimodal bio-signals is used applied control vehicles. The possibility of confirmed by classifying four scenarios controlling speed vehicle. experiment, using simulator hardware-in-the-loop simulation equipment, we confirm response system 351.75 ms recognizes eight through bio-signal data.