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

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