Abstract
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.
Highlights
There have been a few studies on human–machine interaction applied to autonomous vehicles [1,2]
This study confirmed the possibility of a module for vehicle driving speed control based on multimodal bio-signals
To analyze the driver’s emotions, we proposed a vehicle speed control and driving assistance system module using a 1D convolutional neural network (1D CNN) model without input data of 1.1 s and without separate feature extraction to analyze the driver’s emotions
Summary
There have been a few studies on human–machine interaction applied to autonomous vehicles [1,2]. Few studies on human–machine interaction for vehicle control systems using the driver’s situation and emotion have been presented. Jeon et al [3] researched the effect of drivers’ emotional change on vehicle driving and control ability, and Izquierdo-Reyes et al [4] designed vehicle control systems in a new aspect through research that analyzed driving scenarios and emotions for autonomous driving and driver assistance systems. Grimm et al [5] presented studies on the interaction between a driver and a vehicle. The complementary and necessity of changing vehicle driving and control ability according to circumstances and emotions were confirmed in the previous study. Detailed research is required for the advancement and integration of the human–machine interaction and the supplemented vehicle control module. An accurate method and an analysis applicable to the existing vehicle system are necessary
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