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Active Sound Design (ASD) allows the Personalized Engine Sound System to be implemented for different types of vehicles and in different geographical regions. While this process is possible, it requires a lot of on-road tuning and therefore is very time consuming. This study presents an efficient way of tuning ASD sounds based on binaural synthesis in a lab environment instead of on-road tuning. The on-road vehicle operating sounds are reproduced by a desktop NVH simulator while the binaural ASD sounds are synthesized by convolving measured Binaural Vehicle Impulse Responses with the output of ASD multi-channel amplifier in real time. A set of binaural recordings on road are compared with the reproduced sound in the lab environment. The comparison results showed the validity of the proposed method for ASD. The main advantage of this approach is the possibility of back-to-back comparison across different ASD tunings.

This study was conducted to develop and validate a multidimensional measure of shift quality as perceived by drivers during kick-down shift events for automatic transmission vehicles. As part of the first study, a survey was conducted among common drivers to identify primary factors used to describe subjective gear-shifting qualities. A factor analysis on the survey data revealed four semantic subdimensions. These subdimensions include responsiveness, smoothness, unperceivable, and strength. Based on the four descriptive terms, a measure with semantic scales on each subdimension was developed and used in an experiment as the second study. Twelve participants drove and evaluated five vehicles with different gear shifting patterns. Participants were asked to make kick-down events with two different driving intentions (mild vs. sporty) across three different speeds on actual roadway (local streets and highway).

This ‘Motion-Sensor Moustick’ is a sort of new concept control device as if a combination of PC mouse and joystick. It has three simple buttons and a haptic wheel designed for a faster and easier use to learn the vehicle infotainment functions. In addition it has a motion sensor to call a menu via hand approach to change media channels or to display status with just a driver's hand motion within a certain distance. Also this development includes a new concept GUI(graphical user interface) which is compatible with the ‘Moustick’ device. This development could be very helpful to use a car infotainment system.

A Head-Up Display (HUD) is electrical device that provides virtual images in front of driver. Virtual images are consists of various driving information. Because HUD uses optical system there exist image distortions with respect to image height and driver’s eye position. Image warping is image correction method that makes a geometrical change on image to minimize image distortions. In this paper to minimize image distortions, we use optical data driven warping matrix for each image height. But even though we applied data driven warping matrix, image distortions occur due to assemble and manufacturing tolerances when HUD is built. In this paper, we also suggest pre-warping method to minimize image distortions considering tolerances. We simulated 3 compensation functions to get rid of image distortions from the tolerances. By using proposed pre-warping method we could reduce maximum x, y distance by 31.5%, 39% and average distance by 32.2%, 27.9% of distortions.

The powertrain durability test mode often defines the method by reflecting figures such as frequency of use or severity, but in complex systems, durability is difficult to verify in real life conditions under simple conditions. Therefore, in this session, a new analysis method modeled for each driving unit is presented, rather than analyzing time series data in time to extract representative driving pattern from the perspective of the powertrain load reflecting driving situation and driver’s will by applying machine learning technique, and to develop realistic durability test evaluation mode.

An active sound design (ASD) technique enables the implementation of a specific sound in addition to the real engine/e-motor sound in a vehicle. However, it is difficult to satisfy the various needs of customers because it can provide only a few sounds designed by the manufacturer. This paper presents the method of providing the appropriate driving sound and soundscape in an electric vehicle according to the driver’s emotion and driving environment in real-time. For this purpose, it is studied how to construct a driving sound library from the various sound sources and how to recognize a driver's total emotion from the multi-modal data such as facial expression, heart rate, and electrodermal activity using the CNN and support vector machine algorithms. Then it is discussed how to generate the driving sound of electric vehicle according to the driver’s emotion.