A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Hijacking Detection <s> Driving style can characteristically be divided into two categories: “typical” (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentially-aggressive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. To increase awareness and promote driver safety, we are proposing a novel system that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS, video) to detect, recognize and record these actions without external processing. Our system differs from past driving pattern recognition research by fusing related inter-axial data from multiple sensors into a single classifier. It also utilizes Euler representation of device attitude (also based on fused data) to aid in classification. All processing is done completely on the smartphone. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Hijacking Detection <s> Traffic safety and energy efficiency of vehicles are strictly related to driver’s behavior. The scientific literature has investigated on some specific dynamic parameters that, among the others, can be used as a measure of unsafe or aggressive driving style such as longitudinal and lateral acceleration of vehicle. Moreover, the use of modern mobile devices (smartphones and tablets), and their internal sensors (GPS receivers, three-axes accelerometers), allows road users to receive real time information and feedback that can be useful to increase awareness of drivers and promote safety. This paper focuses on the development of a prototype mobile application that can evaluate the grade of safety that drivers are keeping on the road by measuring of accelerations (longitudinal and lateral) and warning for users when it can be convenient to correct their driving style. The aggressiveness is evaluated by plotting vehicle’s acceleration on a g-g diagram specially studied and designed, where horizontal and lateral acceleration is displayed inside areas of “Good Driving Style”. Several experimental tests were carried out with different drivers and cars in order to estimate the system accuracy and the usability of the application. This work is part of the wider research project M2M, Mobile to Mobility: Information and communication technology systems for road traffic safety (PON National Operational Program for Research and Competitiveness 2007-2013) which is based on the use of mobile sensor computing systems for giving real-time information in order to reduce risks and to make the transportation system more safe and comfortable. <s> BIB002
Anti-hijacking precautionary methods are currently approached in a manner of real-time panic alerts, that is normally implemented as a hardware switch that notifies a control room or activate an immobiliser on the vehicle. This type of action cannot be classified as driver behaviour, as it is a sporadic trigger in case of emergency. Although driver style analysis is used for driver safety in BIB001 , anti-hijacking is also discussed in this work, with mentions that a software application might not be able to prevent a driver from being hijacked. A valuable application using driver behaviour and driving style analysis techniques would be able to detect hijack occurrence shortly after the hijacking took place, by recognising different driving patterns and notifying authorities of suspicious activity and the possibility of a hijacking having just occurred. This application can assess the driver's behaviour and based on differences identified between the different driving styles the application might be able to infer that another driver is driving the vehicle. An innovative method for identification of individual drivers is proposed and implemented in BIB002 , using features such as frequency of indicator use when turning, braking frequency, favourable gear, gear shift frequency and average speed. The need for such a system differs between countries, but at the moment such an high-jacking detection application, and research toward its success, is in demand in developing countries where hijacking and vehicle theft are notably higher compared to first world countries. Currently the literature does not provide substantial theft detection related studies applying driver behaviour information and features.