A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Artificial Neural Networks <s> Back-propagation trained neural networks, as well as extreme learning machine (ELM) were used to predict car driverpsilas steering behavior, based on road curvature, velocity and acceleration of a car. Predictions were performed using real-road data, obtained on a test car in a country-road scenario. We made a simplification using gyroscopically measured curvature of the road instead of visually extracted curvature measures. It was found that an optimum exists how far one has to look onto a curvature signal, according to neural network prediction accuracy. Velocity and acceleration did not improve steering signal prediction accuracy in our framework. Traditional neural networks and ELM performed similarly in terms of prediction errors. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Artificial Neural Networks <s> Recently, classifier ensemble methods are gaining more and more attention in the machine-learning and data-mining communities. In most cases, the performance of an ensemble is better than a single classifier. Many methods for creating diverse classifiers were developed during the past decade. When these diverse classifiers are generated, it is important to select the proper base classifier to join the ensemble. Usually, this selection process is called pruning the ensemble. In general, the ensemble pruning is a selection process in which an optimal combination will be selected from many existing base classifiers. Some base classifiers containing useful information may be excluded in this pruning process. To avoid this problem, the multilayer ensemble pruning model is used in this paper. In this model, the pruning of one layer can be seen as a multimodal optimization problem. A novel multi-sub-swarm particle swarm optimization (MSSPSO) is used here to find multi-solutions for this multilayer ensemble pruning model. In this model, each base classifier will generate an oracle output. Each layer will use MSSPSO algorithm to generate a different pruning based on previous oracle output. A series of experiments using UCI dataset is conducted, the experimental results show that the multilayer ensemble pruning via MSSPSO algorithm can improve the generalization performance of the multi-classifiers ensemble system. Besides, the experimental results show a relationship between the diversity and the pruning technique. <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Artificial Neural Networks <s> In this paper, three data-preprocessing techniques, moving average (MA), singular spectrum analysis (SSA), and wavelet multi-resolution analysis (WMRA), were coupled with artificial neural network (ANN) to improve the estimate of daily flows. Six models, including the original ANN model without data preprocessing, were set up and evaluated. Five new models were ANN-MA, ANN-SSA1, ANN-SSA2, ANN-WMRA1, and ANN-WMRA2. The ANN-MA was derived from the raw ANN model combined with the MA. The ANN-SSA1, ANN-SSA2, ANN-WMRA1 and ANN-WMRA2 were generated by using the original ANN model coupled with SSA and WMRA in terms of two different means. Two daily flow series from different watersheds in China (Lushui and Daning) were used in six models for three prediction horizons (i.e., 1-, 2-, and 3-day-ahead forecast). The poor performance on ANN forecast models was mainly due to the existence of the lagged prediction. The ANN-MA, among six models, performed best and eradicated the lag effect. The performances from the ANN-SSA1 and ANN-SSA2 were similar, and the performances from the ANN-WMRA1 and ANN-WMRA2 were also similar. However, the models based on the SSA presented better performance than the models based on the WMRA at all forecast horizons, which meant that the SSA is more effective than the WMRA in improving the ANN performance in the current study. Based on an overall consideration including the model performance and the complexity of modeling, the ANN-MA model was optimal, then the ANN model coupled with SSA, and finally the ANN model coupled with WMRA. <s> BIB003 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Artificial Neural Networks <s> A hybrid model integrating artificial neural networks and support vector regression was developed for daily rainfall prediction. In the modeling process, singular spectrum analysis was first adopted to decompose the raw rainfall data. Fuzzy C-means clustering was then used to split the training set into three crisp subsets which may be associated with low-, medium- and high-intensity rainfall. Two local artificial neural network models were involved in training and predicting low- and medium-intensity subsets whereas a local support vector regression model was applied to the high-intensity subset. A conventional artificial neural network model was selected as the benchmark. The artificial neural network with the singular spectrum analysis was developed for the purpose of examining the singular spectrum analysis technique. The models were applied to two daily rainfall series from China at 1-day-, 2-day- and 3-day-ahead forecasting horizons. Results showed that the hybrid support vector regression model performed the best. The singular spectrum analysis model also exhibited considerable accuracy in rainfall forecasting. Also, two methods to filter reconstructed components of singular spectrum analysis, supervised and unsupervised approaches, were compared. The unsupervised method appeared more effective where nonlinear dependence between model inputs and output can be considered. <s> BIB004 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Artificial Neural Networks <s> This paper presents a non-intrusive approach for monitoring driver drowsiness, based on driver and driving data fusion. The Percentage of Eye Closure (PERCLOS) is used to estimate the driver's state. The PERCLOS is computed on real time using a stereo vision-based system. The driving information used is the lateral position, the steering wheel angle and the heading error provided by the CAN bus. These three signals have been studied in the time and frequency domain. A multilayer perceptron neural network has been trained to fetch an optimal performance score. This system was installed in a naturalistic driving simulator. For evaluation purposes, several experiments were designed by psychologists and carried out with professional drivers. As ground truth, subjective experts' manual annotation of the driver video sequences and driving signals was used. A detection rate of 70% using individual indicators was raised up to 94% with the combination of indicators. An explanation about these results and some conclusion are presented. <s> BIB005 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Artificial Neural Networks <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> BIB006 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Artificial Neural Networks <s> This paper proposes a probability-weighted autoregressive exogenous (PrARX) model wherein the multiple ARX models are composed of the probabilistic weighting functions. This model can represent both the motion-control and decision-making aspects of the driving behavior. As the probabilistic weighting function, a “softmax” function is introduced. Then, the parameter estimation problem for the proposed model is formulated as a single optimization problem. The “soft” partition defined by the PrARX model can represent the decision-making characteristics of the driver with vagueness. This vagueness can be quantified by introducing the “decision entropy.” In addition, it can be easily extended to the online estimation scheme due to its small computational cost. Finally, the proposed model is applied to the modeling of the vehicle-following task, and the usefulness of the model is verified and discussed. <s> BIB007 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Artificial Neural Networks <s> Abstract This paper proposes a rule-based neural network model to simulate driver behavior in terms of longitudinal and lateral actions in two driving situations, namely car-following situation and safety critical events. A fuzzy rule based neural network is constructed to obtain driver individual driving rules from their vehicle trajectory data. A machine learning method reinforcement learning is used to train the neural network such that the neural network can mimic driving behavior of individual drivers. Vehicle actions by neural network are compared to actions from naturalistic data. Furthermore, this paper applies the proposed method to analyze the heterogeneities of driving behavior from different drivers’ data. Driving data in the two driving situations are extracted from Naturalistic Truck Driving Study and Naturalistic Car Driving Study databases provided by the Virginia Tech Transportation Institute according to pre-defined criteria. Driving actions were recorded in instrumented vehicles that have been equipped with specialized sensing, processing, and recording equipment. <s> BIB008 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Artificial Neural Networks <s> The estimation of prediction intervals (PIs) is a major issue limiting the use of Artificial Neural Networks (ANN) solutions for operational streamflow forecasting. Recently, a Lower Upper Bound Estimation (LUBE) method has been proposed that outperforms traditional techniques for ANN-based PI estimation. This method construct ANNs with two output neurons that directly approximate the lower and upper bounds of the PIs. The training is performed by minimizing a coverage width-based criterion (CWC), which is a compound, highly nonlinear and discontinuous function. In this work, we test the suitability of the LUBE approach in producing PIs at different confidence levels (CL) for the 6h ahead streamflow discharges of the Susquehanna and Nehalem Rivers, US. Due to the success of Particle Swarm Optimization (PSO) in LUBE applications, variants of this algorithm have been employed for CWC minimization. The results obtained are found to vary substantially depending on the chosen PSO paradigm. While the returned PIs are poor when single-objective swarm optimization is employed, substantial improvements are recorded when a multi-objective framework is considered for ANN development. In particular, the Multi-Objective Fully Informed Particle Swarm (MOFIPS) optimization algorithm is found to return valid PIs for both rivers and for the three CL considered of 90%, 95% and 99%. With average PI widths ranging from a minimum of 7% to a maximum of 15% of the range of the streamflow data in the test datasets, MOFIPS-based LUBE represents a viable option for straightforward design of more reliable interval-based streamflow forecasting models. Lower Upper Bound Estimation (LUBE) used to build streamflow prediction intervals.LUBE method enhanced via Multi-Objective Fully Informed Particle Swarm (MOFIPS).MOFIPS-based LUBE develops reliable ANN streamflow forecasting models. <s> BIB009
An ANN is an intelligence system that mimics the biological interactions of the nervous system (the brain) and consist of various extremely interconnected processing elements, that solve problems with collective processing BIB006 . In an ANN, signals are transmitted through the connections between nodes of the network (neurons), each assigned with a characteristic and adjustable weight. The adjustable weights enables the network to be trained and to be optimised through a learning algorithm. In a standard ANN this weight is the multiplier with which the input is adjusted to obtain the output value. However, different types of ANNs exist to address problems in various fields. An activation function is used to define the output of the node that is fed further in the network. ANNs are categorised as a machine learning algorithm because the network approximates activation functions and improves its approximations using a learning algorithm. It finds application in driver behaviour solutions and in this survey an investigation is performed to identify the reasons for ANN use, the significance of ANNs, and the results obtained by using the ANN as learning model in these applications. Human behaviour modelling is a suitable field of application for an ANN as human behaviour tend to be nonlinear and stochastic. According to BIB007 , complications arise in certain applications (especially driver assistance applications) due to the difficulty to estimate the drivers' physical and mental state, as well as the adequacy of the information acquired from these models. Oppositely, results with fewer complications and high accuracy were obtained in other studies. One example hereof is the computer vision application performed in BIB005 , for drowsiness detection where the percentage of eye closure (PERCLOS) was used as one input to an ANN in order to estimate the state of driver drowsiness. Driving information used as more inputs to the multilayer perceptron (algorithm applied to binary classification) ANN, included the lateral position, the steering wheel angle and heading error retrieved from the CAN bus. The study conducted in BIB005 further claims a drowsiness detection rate of 70% using single indicators and with the coupling of multiple indicators achieved a drowsiness detection rate of up to 94%. These results were obtained with a naturalistic driver simulator using driver video surveillance and driving signals, but complications will be introduced with actual driving data and situations. A Feed-Forward Neural Network (FFNN) consist of a layered structure, and are considered static networks as they have no feedback operations and, therefore, the information is fed from one node to the next down the layered structure without delay and the output are solely dependent on the input. Apart from the static FFNNs, dynamic NNs exist which comprise of feedback elements and is used for prediction purposes. In the driver distraction detection application performed in BIB006 , both static and dynamic ANNs were implemented using vehicle dynamic data such as speed (m/s), time to collision and lane crossing (s), positions of the accelerator and brake pedals (%), steering wheel angle (deg), and lateral position (m). Dynamic ANNs require a back-propagation approach to train the network introducing more expenses to the solution as opposed to the static ANNs. An example of such a system can be found in BIB001 , where dynamic ANNs were used to predict the steering behaviour of a driver based on road curvature and the speed and acceleration of the vehicle. Real driving data were used in the pre-mentioned study, and it was concluded that acceleration and velocity measures did not improve the steering behaviour prediction accuracy in the solution. Another variation of an ANN application can be found in BIB008 , where a fuzzy rule-based ANN model was used to obtain driver-specific driving constraints from the trajectory data of the vehicle. In this method the ANN was trained using an ML method known as reinforcement learning, in order to enable the system to imitate the driving behaviour of an individual. Uniformity between different drivers' behaviour was also investigated in BIB008 , and it was found that heterogeneities exist between similar driving actions. Optimizing machine learning problems applying particle swarm optimization (PSO) is achieved through a classifier ensemble pruning method and it was found that the performance of an ensemble of classifiers is better than a single classifier with an ANN identified as a suitable base classifier BIB002 . The estimation of prediction intervals (PIs) is an extensive limitation in the application of ANNs to real-life problems. A recent solution that addressed this limitation in the operational streamflow forecasting industry, made use of a Lower Upper Bound Estimation (LUBE) method which outperforms classic PIs construction techniques such as the delta method, Bayesian methods, and bootstrapping on both synthetic experiments as well as real world regression problems BIB009 . Consideration of this technique could prove relevant in driver behaviour model construction. Due to a conventional ANN resulting in poor rainfall forecasts in initial experiments, a hybrid model integrating ANNs and support vector regression (SVR) was developed for daily rainfall prediction BIB004 . In the modelling process, singular spectrum analysis (SSA) was first adopted to decompose the raw rainfall data followed by fuzzy C-means clustering to split the training set into subsets associated with the intensity of rainfall. The joint effect of a hybrid ANN-SVR model and the SSA filtering technique considerably improved the accuracy of daily rainfall forecasting. An improvement in daily flow prediction was also identified by coupling three data-preprocessing techniques, moving average (MA), SSA, and wavelet multi-resolution analysis (WMRA), with ANNs BIB003 .