Driver support and infotainment systems can be adapted to the specific needs of individual drivers by assessing driver skill and state. In this paper, we present a machine learning approach to classifying the skill at maneuvering by drivers using both longitudinal and lateral controls in a vehicle. Conceptually, a model of drivers is constructed on the basis of sensor data related to the driving environment, the drivers' behaviors, and the vehicles' responses to the environment and behavior together. Once the model is built, the driving skills of an unknown driver can be classified automatically from the driving data. In this paper, we demonstrate the feasibility of using the proposed method to assess driving skill from the results of a driving simulator. We experiment with curve driving scenes, using both full curve and segmented curve scenarios. Six curves with different radii and angular changes were set up for the experiment. In the full curve driving scene, principal component analysis and a support vector machine-based method accurately classified drivers in 95.7 % of cases when using driving data about high-and low/average-skilled driver groups. In the cases with segmented curves, classification accuracy was 89 %.
This paper proposes a multi-touch steering wheel for in-car tertiary applications. Existing interfaces for in-car applications such as buttons and touch displays have several operating problems. For example, drivers have to consciously move their hands to the interfaces as the interfaces are fixed on specific positions. Therefore, we developed a steering wheel where touch positions can correspond to different operating positions. This system can recognize hand gestures at any position on the steering wheel by utilizing 120 infrared (IR) sensors embedded in it. The sensors are lined up in an array surrounding the whole wheel. An Support Vector Machine (SVM) algorithm is used to learn and recognize the different gestures through the data obtained from the sensors. The gestures recognized are flick, click, tap, stroke and twist. Additionally, we implemented a navigation application and an audio application that utilizes the torus shape of the steering wheel. We conducted an experiment to observe the possibility of our proposed system to recognize flick gestures at three positions. Results show that an average of 92% of flick could be recognized.
Analysis of driving skill/driver state can be used in building driver support and infotainment systems that can be adapted to individual needs of a driver. In this paper we present a machine learning approach to analyzing driving maneuver skills of a driver that covers both longitudinal and lateral controls. The concept is to learn a driver model from sensor data that are related to driving environment, driving behavior and vehicle response. Once the model is built, driving skills of an unknown run can be classified automatically. In this paper, we demonstrate the feasibility of the proposed method for driving skill analysis based on a driving simulator experiment in a curve driving scene for both the full curve and curve segmented cases.
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