A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Normal / Safe Driving Style <s> Driving speed and vehicle movement are important factors in road safety. Abnormal driver generally drives above speed limit, changes speed suddenly or changes vehicle lateral position incessantly. One approach to tackling this problem is to develop automated instrument for detecting abnormal drivers. Global Position System (GPS) provides special features which can be utilized for this application as it could supplies location, time, and speed simultaneously. In-vehicle transponders send real time GPS data via radio frequency to a Monitoring Station for processing, and from detection algorithm, abnormal situation will be detected automatically. The normal speed parameter and lateral position changing obtained from our previous study can be used as threshold value in detection algorithm. If the driver behavior is evidently different from normal driving, then abnormal driving is recognized. Experiments and simulated data were used to verify the effectiveness of the detection algorithm. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Normal / Safe Driving Style <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> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Normal / Safe Driving Style <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> BIB003
In driver style applications the main goal is to detect behaviour out of the ordinary BIB001 , and for learning and classification techniques, a reference driving style should be defined from which deviation can be identified. In this study online driver behaviour classification into pre-set driving styles is investigated. Aggressive, inattentive and drunk driving styles are styles of driving that deviate from the norm and expected behaviour of a driver. It is therefore necessary to define a driving style that can represent a driver's normal behaviour in order to identify behaviour that diverges from the norm. For instance, aggressive driving is classified when the driver's behaviour proves to be more aggressive than the normal state of driving recorded for the specific driver based on his behaviour during the training of the classifier. This approach is not a new initiative and are followed in many driving style analysis studies and referred to as either "normal driving", "safe driving", "typical driving" or "non-aggressive" driving. An euclidean norm was evaluated on accelerometer data in BIB002 in order to use the deviations from the average of this norm to classify the driving behaviour in different classes, in this case Below Normal (BN), Normal (N), Aggressive (A), Very Aggressive (VA). In BIB001 , speed and the change in lateral position were used as parameters in the driving style detection algorithm and deviations from normal driving were used to identify abnormal driving events. In order to enable for differentiation with high selectivity, a discrete scale with different levels was used to distinguish different driving styles using the deviations from the norm as classifier parameter in BIB003 . The normal driving style may vary greatly between drivers depending on various variables, such as the AI algorithms implemented, the vehicle used and the behaviour of the driver during the training period of the classifier. The study is not focussed on identifying a style of driving which should correlate between drivers, but rather the deviation of driver behaviour based on the recorded driving history for an individual. A "safer driver" can easily be confirmed with accrued driving experience and increased recorded driver history as sufficient driving behaviour history data will improve machine learning classification. For road safety enhancement purposes it can be advantageous to determine a safe or ideal driving style to which a driver can adapt his behaviour, once characteristics of other risky driving styles such as aggressive driving, inattentive driving or drunk driving have been identified.