A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Inattentive Driving Style <s> A key to the development of effective crash countermeasures is an understanding of pre-crash causal and contributing factors. This research effort was initiated to provide an unprecedented level of detail concerning driver performance, behavior, environment, driving context and other factors that were associated with critical incidents, near crashes and crashes for 100 drivers across a period of one year. A primary goal was to provide vital exposure and pre-crash data necessary for understanding causes of crashes, supporting the development and refinement of crash avoidance countermeasures, and estimating the potential of these countermeasures to reduce crashes and their consequences. The 100-Car Naturalistic Driving Study database contains many extreme cases of driving behavior and performance, including severe fatigue, impairment, judgment error, risk taking, willingness to engage in secondary tasks, aggressive driving, and traffic violations. The data set includes approximately 2,000,000 vehicle miles, almost 43,000 hours of data, 241 primary and secondary drivers, 12 to 13 months of data collection for each vehicle, and data from a highly capable instrumentation system including five channels of video and vehicle kinematics. From the data, an “event” database was created, similar in classification structure to an epidemiological crash database, but with video and electronic driver and vehicle performance data. The events are crashes, near crashes and other “incidents.” Data was classified by pre-event maneuver, precipitating factor, event type, contributing factors, and the avoidance maneuver exhibited. Parameters such as vehicle speed, vehicle headway, time-to-collision, and driver reaction time are also recorded. This paper presents the 100-Car Naturalistic Driving Study method, including instrumentation and vehicle characteristics, and a sample of study results. Presented analyses address the driver characteristics, the role of inattention and distraction in rear-end and lane change events. In addition, the methodological attributes of naturalistic data collection and the implications for a larger-scale naturalistic data collection effort are provided. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Inattentive Driving Style <s> This paper gives a review of the literature on driver inattention monitoring system for the purpose of active safe driving. In this paper driving inattention is classified into two categories: fatigue and distraction, while fatigue and distraction can also contain many types and levels. Individual difference on inattention phenomenon makes it more complicated to correctly detect and recognize driving inattention. Driver attention monitoring has been intensively researched in recent years and many approaches have been proposed, which include biological signal (EEG, ECG, EOG and sEMG) processing method, subjective report method, and behavior analysis method. This survey reviews a number of promising approaches and provides an overview of recent developments in this domain. The emphasis of this paper is to discuss the various methodologies to monitor driving inattention. We conclude with some thoughts about future directions. <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Inattentive Driving Style <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> BIB003 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Inattentive Driving Style <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> Inattentive Driving Style <s> This paper presents DriveSafe, a new driver safety app for iPhones that detects inattentive driving behaviors and gives corresponding feedback to drivers, scoring their driving and alerting them in case their behaviors are unsafe. It uses computer vision and pattern recognition techniques on the iPhone to assess whether the driver is drowsy or distracted using the rear-camera, the microphone, the inertial sensors and the GPS. We present the general architecture of DriveSafe and evaluate its performance using data from 12 drivers in two different studies. The first one evaluates the detection of some inattentive driving behaviors obtaining an overall precision of 82% at 92% of recall. The second one compares the scores between DriveSafe vs the commercial AXA Drive app obtaining a better valuation to its operation. DriveSafe is the first app for smartphones based on inbuilt sensors able to detect inattentive behaviors evaluating the quality of the driving at the same time. It represents a new disruptive technology because, on the one hand, it provides similar ADAS features that found in luxury cars, and on the other hand, it presents a viable alternative for the “blackboxes” installed in vehicles by the insurance companies. <s> BIB005
Driver behaviour which indicates regular inattention to driving actions and necessary observations to complete the driving task is classified as "inattentive driving style" in this study. Most probably, inattention while driving can be observed as an instantaneous deviation from normal behaviour with the immediate following of sudden driver actions to correct behaviour resulting from inattention in the form of a reaction. An inattentive driving style differs from an aggressive driving style due to its instantaneous and sporadic nature, as opposed to an aggressive driving style which is can be observed as a pattern of misbehaviour over a period of time. Inattention is a major contributing factor to the road safety problem introduced earlier in this report. A study named the "100-Car Naturalistic Study" BIB001 was designed by the National Highway Safety Administration (NHTSA) in collaboration with the Virginia Tech Transportation Institute (VTTI) to provide insight on the influence and contribution of driver behaviour immediately preceding an accident. This study was performed on 100 vehicles fitted out with surveillance and other sensor devices for a duration of a year, driven collectively for nearly 2 million miles, and accumulated 42,000 h of data from the 241 drivers. It revealed that 78% of the 82 accidents recorded, and 65% of the 761 near accidents were the direct effect of driver inattention BIB003 . In 2010 in the USA alone, 3092 people were killed and 416,000 injured during accidents directly attributed to driver inattention BIB005 . It is estimated that 13.3% of accidents can be attributed to the involvement of driver distraction and 9.7% of crashes categorised as "looked but did not see" BIB004 . Driver inattention is an ambiguous description, as inattention is influenced by many factors and measurable events of inattention should be identified. Therefore a breakdown of "driver inattention" is performed to enable conclusions to be drawn of the state of inattention. A recent review of the current state of the art in driver inattention monitoring classified two main categories in monitoring a driving style of inattention, namely, driver fatigue and driver distraction BIB002 . The categorisation of inattention is made based on the difference between distraction and the phenomenon of fatigue. Fatigue exhibits physical symptoms such as reduced performance and a subjective feeling of drowsiness, whereas distraction may be due to a number of unpredicted reasons that occur at random. One possible application of the distinction between states of inattention can include the classification of a driver as a distraction prone driver or fatigue prone driver.