A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Accident Detection <s> Traffic flow characteristics that lead to crashes on urban freeways are examined. Since these characteristics are observed prior to crash occurrence, they are referred to as "crash precursors." The objectives are (a) to explore factors contributing to changes in crash rate for individual vehicles traveling over an urban freeway and (b) to develop a probabilistic model relating significant crash precursors to changes in crash potential. The data used to examine crash precursors were extracted from 38 loop detector stations on a 10-km stretch of the Gardiner Expressway in Toronto for a 13-month period. An aggregate log-linear model was developed relating crash rates to the selected crash precursors observed upstream of the crash site. The results of this analysis suggest that the variation of speed and traffic density are statistically significant predictors of crash frequency after controlling for road geometry, weather, and time of day. With the model, crash potential can be established based on the precu... <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Accident Detection <s> The increasing activity in the Intelligent Transportation Systems (ITS) area faces a strong limitation: the slow pace at which the automotive industry is making cars "smarter". On the contrary, the smartphone industry is advancing quickly. Existing smartphones are endowed with multiple wireless interfaces and high computational power, being able to perform a wide variety of tasks. By combining smartphones with existing vehicles through an appropriate interface we are able to move closer to the smart vehicle paradigm, offering the user new functionalities and services when driving. In this paper we propose an Android-based application that monitors the vehicle through an On Board Diagnostics (OBD-II) interface, being able to detect accidents. Our proposed application estimates the G force experienced by the passengers in case of a frontal collision, which is used together with airbag triggers to detect accidents. The application reacts to positive detection by sending details about the accident through either e-mail or SMS to pre-defined destinations, immediately followed by an automatic phone call to the emergency services. Experimental results using a real vehicle show that the application is able to react to accident events in less than 3 seconds, a very low time, validating the feasibility of smartphone based solutions for improving safety on the road. <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Accident Detection <s> This paper presents an innovative idea for the classification of individual drivers. The classification is based on each driver's driving features like, ratio of indicators to turns, number of brakes, number of time horn used, average gear, average speed, maximum speed and gear. K-means and hierarchical clustering is used to separate out the slow, normal and fast driving styles based on recorded data. Experimental result shows that k-means outperformed hierarchical clustering for recorded multi-attribute data. <s> BIB003 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Accident Detection <s> Naturalistic driving studies permit the study of driving behaviour during every day driving. Such studies have a long duration and rare events such as near-crashes and even crashes do occur during the period of the study. This fact gives an opportunity to study events that are otherwise difficult to find. However, the vast amount of data recorded within these naturalistic driving studies demands a huge amount of manual work to identify hazardous situations. This paper concerns the development and validation of a new method, based on critical jerk, to identify safety critical braking events during car driving. The method was compared with one of today's most used method, which is based on the longitudinal acceleration measure. Both methods were applied on near-crash data from the 100-car naturalistic driving study previously carried out by the Virginia Tech Transportation Institute (VTTI). The data included 637 near-crashes. The results from the analyses showed that the critical jerk method performed approximately 1.6 times higher overall success rate than the method based on the longitudinal acceleration measure. In addition, a positive correlation was found between driver's safety critical braking event and crash involvement. The conclusion is that the critical jerk method is capable of detecting safety critical braking events and may also be used for assessing high risk drivers. <s> BIB004
Real-time acceleration and jerk variance evaluation for individual vehicles are often used to detect anomalous behaviour in risk assessment BIB003 . Accident detection is performed mainly based on methods that monitor traffic density as described in BIB001 , but real-time driver behaviour and vehicle movement analysis systems can also prove relevant and immediate. Certain early-accident detection applications include immediate dispatch of emergency services and road-side assistance services directly upon accident detection BIB002 . This means that emergency assistance services can be aware of accidents, and the severity thereof, before or without the incident being reported. Accidents can be detected based on impact readings based on longitudinal and transversal acceleration measurements. In BIB004 , an innovative system is presented to integrate accident detection with driver behaviour and driver style classification systems. This is done based on critical jerk evaluation, which enables safety critical braking event identification and accident occurrence recognition from real-time driving data, due to the high correlation between them. The jerk method that was performed had a success rate of 1.6 times greater than the longitudinal acceleration methods.