A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Support Vector Machines <s> With the proliferation of smart portable devices, more people have started using them within the vehicular environment while driving. Although these smart devices provide a variety of useful information, using them while driving significantly affects the driver's attention towards the road. This can in turn cause driver distraction and lead to increased risk of crashes. On the positive side, these devices are equipped with powerful sensors which can be effectively utilized towards driver behavior analysis and safety. This study evaluates the effectiveness of portable sensor information in driver assistance systems. Available signals from the CAN-bus are compared with those extracted from an off-the-shelf portable device for recognizing patterns in driving sub-tasks and maneuvers. Through our analysis, a qualitative feature set is identified with which portable devices could be employed to prune the search space in recognizing driving maneuvers and possible instances of driver distraction. An absolute improvement of 15% is achieved with portable sensor information compared to CAN-bus signals, which motivates further study of portable devices to build driver behavior models for driver assistance systems. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Support Vector Machines <s> There is accumulating evidence that driver distraction is a leading cause of vehicle crashes and incidents. In particular, increased use of so-called in-vehicle information systems (IVIS) and partially autonomous driving assistance systems (PADAS) have raised important and growing safety concerns. Thus, detecting the driver's state is of paramount importance, to adapt IVIS and PADAS accordingly, therefore avoiding or mitigating their possible negative effects. The purpose of this paper is to show a method for the nonintrusive and real-time detection of visual distraction, using vehicle dynamics data and without using the eye-tracker data as inputs to classifiers. Specifically, we present and compare different models that are based on well-known machine learning (ML) methods. Data for training the models were collected using a static driving simulator, with real human subjects performing a specific secondary task [i.e., a surrogate visual research task (SURT)] while driving. Different training methods, model characteristics, and feature selection criteria have been compared. Based on our results, using a support vector machine (SVM) has outperformed all the other ML methods, providing the highest classification rate for most of the subjects. Potential applications of this paper include the design of an adaptive IVIS and of a “smarter” PADAS. <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Support Vector Machines <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> BIB003 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Support Vector Machines <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> BIB004
In supervised learning applications the labels to which inputs should be matched are known and therefore the machine is aware of the classification that it needs to reproduce. A system that assigns labels to unseen instances is known as a classifier. SVMs are rather insensitive to the curse of dimensionality and are efficient enough to handle large scale problems in both sample and variable space, with the fundamental functionality to map data to a higher dimensional space via a kernel function BIB002 . In BIB003 , a SVM method is used to recognise driving events and the results of applying a Gaussian radial basis function (RBF) kernel as opposed to K-Mean clustering are evaluated. The optimal recognition rate achieved was 60%, and it was observed that acceleration event data alone did not meet expectations. Better results were obtained using supervised learning as opposed to unsupervised learning, with the conclusion that shortened datasets outperformed the full dataset probably because of the RBF kernel. The study conducted in BIB001 also implemented a RBF kernel using the UTDrive instrumented vehicle as the sensor platform. The feature vector applied to perform the manoeuvre recognition task consisted of 28 signals, of which, 5 signals are obtained from the CAN-bus and 23 signals are obtained from a portable device (accelerometer signals, vehicle speed signals and bearing signals retrieved from the GPS sensor). Driver manoeuvre detection is performed using SVMs in BIB001 , and in order to perform multi-class classification with a SVM, different approaches exist and in this instance a SVM was used with a Gaussian RBF kernel. In BIB002 , the authors proposed a nonintrusive method for real-time detection of visual distraction, using vehicle dynamics data and without using the eye-tracker data as inputs to classifiers. Their results proved that using SVMs outperformed the other machine learning techniques, providing the highest classification rate for most of the test cases. These results are especially important as it is applied to partially autonomous driving assistance systems (PADAs) in order to guarantee and ensure safety. SVM techniques have been applied in driving sleepiness detection algorithms with high robustness and practicability, some of these applications are listed in BIB004 .