A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Radial Basis Function <s> Currently there are many research focused on using smartphone as a data collection device. Many have shown its sensors ability to replace a lab test bed. These inertial sensors can be used to segment and classify driving events fairly accurately. In this research we explore the possibility of using the vehicle's inertial sensors from the CAN bus to build a profile of the driver to ultimately provide proper feedback to reduce the number of dangerous car maneuver. Braking and turning events are better at characterizing an individual compared to acceleration events. Histogramming the time-series values of the sensor data does not help performance. Furthermore, combining turning and braking events helps better differentiate between two similar drivers when using supervised learning techniques compared to separate events alone, albeit with anemic performance. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Radial Basis Function <s> Driving sleepiness is still a major causes of traffic accidents. Individual drivers, under various conditions, act and respond in different manners. This article presents the attempt of a straight-line driving simulator study that examined the effects of driver behavior style differences and individual differences on driver sleepiness detection which is based on driving performance measures. A total of 15 drivers who were classified into two categories through subjective assessment based on a Driver Behavior Questionnaire participated in driving simulator experiments. A total of 18 detection models, including 15 SE models for each subject, an A model for the aggressive drivers, an NA model for the non-aggressive drivers, and a G model for all experiment participants, were developed using support vector machine method based on driving performance characteristic parameters. The results show that the G model is not suitable for all drivers due to its lower mean accuracy of 69.88% (standard deviation = 7.70%) and higher standard deviation. The SE models for each subject show the best detection accuracy performance of 84.26% (standard deviation = 5.38%); however, it is impossible to set up a special detection model for every individual driver. The SD models on different style categories show an accuracy value of 77.54% (standard deviation = 5.78%). The results demonstrate that driver style differences as well as individual differences have great effects on driver sleepiness detection (F = 19.148, p \ 0.000). <s> BIB002
When training a SVM, the application of a kernel and specifically a RBF. The RBF is used in order to map samples of low dimensionality into higher dimensional space. The RBF used in BIB002 is given by In this function, i x and j x are two data points, C and γ are the two unknown parameters for the RBF kernel. In BIB001 , the power intervals for the unknown parameters was found as C = [2 −15 ; 2  ] and γ = [2 −15 ; 2 15 ].