A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Fuzzy Logic <s> This paper presents a general approach for recognition of driving maneuvers in advanced driver assistance systems (ADAS). Such systems often rely on the identification of driving maneuvers (overtaking, left turn at intersections, etc.) to improve the prediction of potential collisions or to trigger appropriate support for the driver. The proposed maneuver recognition approach combines a fuzzy rule base to model basic maneuver elements and probabilistic finite-state machines to capture all possible sequences of basic elements that constitute a driving maneuver. The proposed method is specifically tailored to ADAS requirements because of its low computational complexity, its flexibility and its straight-forward design based on easily comprehensible logical rules. In addition, we propose a suitable training method to optimize the fuzzy rule base. Our approach is evaluated on the recognition of turn maneuvers. Experiments on real data from a test vehicle demonstrate the feasibility of the proposed method. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Fuzzy Logic <s> Fatigue driving is an important cause of traffic accidents, the key to avoid fatigue driving is to detect the driver’s fatigue condition in real time. Driving behaviors such as accelerate???brake???shift and steer, continuous change when drivers become more and more fatigue. By detecting and analyzing the changing signals of them, it can show whether the driver is fatigue or not. This fatigue driving detection system is composed of driving simulator, data acquisition system, PC serial communication System and fatigue identification model. <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Fuzzy Logic <s> Detection and classification of aggressive driving can be based on the use of physiological signals or biometric information like electrocardiogram, electro dermal activity, and respiration. This research proposes a driving performance inference system based on the signature of acceleration in the two dimensions and speed. Driving style can be categorized to: below normal, normal, aggressive, and very aggressive. One of the targets of this paper is to recognize the driving events that fall into each of these categories. Many of driving styles are a major cause of traffic crashes. Many of these styles can be detected with reasonable accuracy using driving performance. The main idea is to utilize the 2-axis accelerometer that is embedded in most of the GPS tracker devices that are used in vehicle tracking and fleet management to recognize the driving styles. This method can be utilized for vehicle active safety purpose. Fuzzy logic inference system is used in classification of the extracted feature to the predefined driving styles. the power of the Euclidean norm of longitudinal and lateral is used as input to the fuzzy inference system in addition to the speed. Classification of the driving style is real time and almost cost nothing. <s> BIB003 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Fuzzy Logic <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> BIB004 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Fuzzy Logic <s> Profiling driving behavior has become a relevant aspect in fleet management, automotive insurance and eco-driving. Detecting inefficient or aggressive drivers can help reducing fleet degradation, insurance policy cost and fuel consumption. In this paper, we present a Fuzzy-Logic based driver scoring mechanism that uses smartphone sensing data, including accelerometers and GPS. In order to evaluate the proposed mechanism, we have collected traces from a testbed consisting in 20 vehicles equipped with an Android sensing application we have developed to this end. The results show that the proposed sensing variables using smartphones can be merged to provide each driver with a single score. <s> BIB005 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Fuzzy Logic <s> The proliferation of smartphones and mobile devices embedding different types of sensors sets up a prodigious and distributed sensing platform. In particular, in the last years there has been an increasing necessity to monitor drivers to identify bad driving habits in order to optimize fuel consumption, to reduce CO2 emissions or, indeed, to design new reliable and fair pricing schemes for the insurance market. In this paper, we analyze the driver sensing capacity of smartphones. We propose a mobile tool that makes use of the most common sensors embedded in current smartphones and implement a Fuzzy Inference System that scores the overall driving behavior by combining different fuzzy sensing data. <s> BIB006 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Fuzzy Logic <s> Nowadays more and more driver assistance systems are implemented in cars. By adapting the system to the driving style of the driver, the acceptance of the driver to such a system could be enhanced. In this paper a system for online driving style recognition is designed. It is implemented in Matlab/Simulink and uses fuzzy logic for identifying the current driving style. It is fully parameterisable via a central parameter file and could therefore be adapted to nearly every car. The recognition was tested by using a vehicle dynamics simulation with 68% correct classifications over time. <s> BIB007 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Fuzzy Logic <s> In recent year many researcher and industries are working on VANET and trying to implement the concepts in real world. Many VANET systems are proposed and tested on simulation but very few inventors implemented it. Vehicular ad hoc networks (VANETs) are being developed to provide on-demand wireless communication infrastructure among vehicles and authorities. Such an infrastructure is expected to deliver multiple road safety and driving assistance applications. Vehicles will be equipped with sensors and communication devices that will allow them to cooperate with each other. Vehicles can exchange different type of information as per requirements on demand for specified application. With the purpose of supporting and improving data collection and distribution, in this paper a smart phone-based platform is designed that exploits low-cost dedicated hardware to interact with sensors on board and in the vehicle surroundings. <s> BIB008 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Fuzzy Logic <s> Today's smartphones and mobile devices typically embed advanced motion sensors. Due to their increasing market penetration, there is a potential for the development of distributed sensing platforms. In particular, over the last few years there has been an increasing interest in monitoring vehicles and driving data, aiming to identify risky driving maneuvers and to improve driver efficiency. Such a driver profiling system can be useful in fleet management, insurance premium adjustment, fuel consumption optimization or CO2 emission reduction. In this paper, we analyze how smartphone sensors can be used to identify driving maneuvers and propose SenseFleet, a driver profile platform that is able to detect risky driving events independently from the mobile device and vehicle. A fuzzy system is used to compute a score for the different drivers using real-time context information like route topology or weather conditions. To validate our platform, we present an evaluation study considering multiple drivers along a predefined path. The results show that our platform is able to accurately detect risky driving events and provide a representative score for each individual driver. <s> BIB009
Another area of great interest in driver behaviour prediction and recognition is the usage of the fuzzy logic (FL) principle. Fuzzy logic represents logic as a degree of truth or in some cases many valued logic is imposed instead of binary logic known as true or false. Fuzzy sets were introduced in 1965 as a class of objects with a continuum of grades of membership  . These sets are characterised by a membership function which allocates a grade of membership to each object ranging between zero and one, this is to address precision in logic when addressing human reasoning BIB001 . A fuzzy interface was used in BIB002 to detect driver fatigue, and in BIB004 an adaptive neurofuzzy inference system (ANFIS) was used to detect driver distraction. The reason for the implementation of an FL system (FLS) in BIB004 , is due to the ability of a FLS to simultaneously interpret numerical data and linguistic knowledge and its ability to map nonlinear input data to a scalar feature vector output. The membership functions applied in FLS are normally curves with a triangular shape or a Gaussian shape. Defuzzification is the estimation of the output fuzzy sets of the FLS into a single number. The nonlinearity and structured knowledge representation are the primary advantages of ANFIS over classical linear approaches in adaptive filtering and inference systems. An area in driver behaviour and driver style analysis where FL finds valuable application is in driver performance or risk scoring applications. An example of such a system is presented in BIB009 , where a FLS is used to compute a score for different drivers. It was found that this implementation accurately detected risky driving events and a representative score for each individual driver through a FLS. The FLS design of the fuzzy-logic based scoring mechanism applied in BIB005 , can be found in Figure 8 . Smartphone sensory data was used in this study that included accelerometer and GPS data, the sensing data was filtered after which event detection was performed. The input data was fuzzyfied thereafter and fuzzy rules were applied to infer conclusions. The defuzzification produced a score (between 0 and 100) and was used to provide each driver with personal feedback. In BIB006 , the output variable represented three categories (Normal, Moderate and Aggressive) whereas in a similar study by the same authors conducted afterwards, BIB005 , the output variable was extended to four categories (Calm, Average, Moderate and Aggressive). This was done to address increased complexity in the profiling algorithm as an increased number of output categories led to a better representational score. The output fuzzy set of both these examples can be seen in Figure 9 . Fuzzy inference techniques were further applied in driving style recognition studies, namely the study conducted in BIB007 which yielded a 68% classification confidence using vehicle dynamic data, the study performed in BIB003 where 2-axis accelerometer and speed readings were integrated, and BIB008 which made use of the three-axis accelerometer data to determine not only driving style but vehicle body posture as well. 