A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Early-Warning Applications <s> Motor vehicles greatly influence human life but are also a major cause of death and road congestion, which is an obstacle to future economic development. We believe that by learning driving patterns, useful navigation support can be provided for drivers. In this paper, we present a simple and reliable method for the recognition of driving events using hidden Markov models (HMMs), popular stochastic tools for studying time series data. A data acquisition system was used to collect longitudinal and lateral acceleration and speed data from a real vehicle in a normal driving environment. Data were filtered, normalized, segmented, and quantified to obtain the symbolic representation necessary for use with discrete HMMs. Observation sequences for training and evaluation were manually selected and classified as events of a particular type. An appropriate model size was selected, and the model was trained for each type of driving events. Observation sequences from the training set were evaluated by multiple models, and the highest probability decides what kind of driving event this sequence represents. The recognition results showed that HMMs could recognize driving events very accurately and reliably. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Early-Warning Applications <s> This paper presents investigations into the role of computer-vision technology in developing safer automobiles. We consider vision systems, which cannot only look out of the vehicle to detect and track roads and avoid hitting obstacles or pedestrians but simultaneously look inside the vehicle to monitor the attentiveness of the driver and even predict her intentions. In this paper, a systems-oriented framework for developing computer-vision technology for safer automobiles is presented. We will consider three main components of the system: environment, vehicle, and driver. We will discuss various issues and ideas for developing models for these main components as well as activities associated with the complex task of safe driving. This paper includes a discussion of novel sensory systems and algorithms for capturing not only the dynamic surround information of the vehicle but also the state, intent, and activity patterns of drivers. <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Early-Warning Applications <s> Assessment of driving behavior and estimation of risk therein is an important use case for telematics and vehicular networking technology domain, which has gained interest in the fleet management and consumer verticals, but above all with the car insurance firms. These firms have started incorporating the results of such analysis to provide their customers with "pay as you drive" insurance plans, which decide the initial premium based on the driver's past history of crashes and violations. Then the parties enter into an evaluation period of about two months (say), at the end of which premium is recalculated based on analysis of accelerometer, GPS and on-board diagnostic (OBD II) data. However, the consumer has no knowledge of the progress of his or her evaluation due to lack of access to device data from insurance provider. One major criterion of such evaluation is based on the detection and aggregation of high-risk maneuvers by the driver, which typically constitutes of hard bump, sharp cornering, hard stop and speed limit violations. In this paper, we propose a novel method named MobiDriveScore for routine ventures using which the consumer can assess his/her own driving pattern. Thus the consumer can consciously reduce the risk associated with his/her driving by using MobiDriveScore. For MobiDriveScore, detection of maneuvers is done using accelerometer and GPS sensors only, which are integral part of modern day smartphones. Using MobiDriveScore, the user can choose an alternate route or choose to travel a little early to improve upon the premium rates and stay safe. It will also help aggressive drivers to become more passive. Experiments have been conducted and the initial results of MobiDriveScore are found to be encouraging. <s> BIB003 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Early-Warning Applications <s> Road traffic accidents (RTAs) pose a public health and development challenge and greatly affect the human capital development of every nation. Main causes for a significant percentage of RTAs are recklessness, fatigue or stress, inexperienced driving and driving under the influence of alcohol. Reputed automobile companies and authorities have established standards and/or proactive/reactive solutions over the years to overcome this problem but the rates continue to grow each year. Software solutions that focus on preventing RTAs are difficult to be found in economy class automobiles that are used by the majority of consumers. The limited number of successful applications are either bound to a certain brand/model and/or focus on a specific task. SmartV is smart phone based vigilance monitoring system that focuses on the majority of factors contributing to RTAs. These include DUI (driving under the influence of alcohol), health issues and vehicle defects. The mobile application communicates with a heart rate monitor and a Bluetooth OBDII Adapter. These will continuously monitor the heart rate of the driver and the status of the vehicle respectively. During the course of the drive, the system monitors the behavior of the vehicle for existence of dangerous driving patterns that are defined in Visual Detection of DWI Motorists[8] a study conducted by U.S.A. N.H.T.S.A. The final product is a versatile, non-intrusive, flexible and most importantly an affordable mobile application that captures the vehicle's behavior, the health level of the driver and the status of the vehicle. Real environment operation of SmartV has yielded significant results proving itself to be a successful and an affordable Vigilance Monitoring System for the future. <s> BIB004 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Early-Warning Applications <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> BIB005 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Early-Warning Applications <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> BIB006 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Early-Warning Applications <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> BIB007 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Early-Warning Applications <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> BIB008
In the instance where probable risk while driving is determined, one way to reduce the risk is to warn the driver prior to an incident of the risk, which can either be distraction, aggression, drunk driving or external risk originating from another vehicle or object. Such solutions and applications already exist, and systems aimed at early detection and alert of dangerous vehicle manoeuvres typically related to drunk driving, proved highly efficient using a smartphone as the sensory, processing and feedback platform BIB005 . Reliability assessment with real-time feedback was successfully implemented in BIB001 , which predicts future driving events by assessing the driver's driving style pattern history. Another approach is the grading of a driver on a "safety scale" based on driving style and driver behaviour analysis, and providing real-time information and feedback to assist in order to improve consciousness and promote safe driving BIB006 . The method of scoring a driver's performance or risk is elaborated on further in other applications in particular insurance practices BIB007 BIB003 BIB008 . A computer-vision system was designed in BIB002 that not only observes activities and risk outside the vehicle, i.e., instance detecting and tracking roads, and avoid hitting obstacles or pedestrians, but simultaneously look inside the vehicle to monitor the awareness of the driver and predict the driver's intentions (predicted driving actions to follow) and state of mind. This system considers all three components that influence driving namely the environment, vehicle and driver, and in order to promote safe driving, alerts the driver of risk originating from any one of these components. More computer-vision alert systems exist such as DriveSafe BIB007 , a driver safety application for iPhones that identifies inattentive driving styles and provides relevant feedback to drivers, and SmartV BIB004 , an intelligent vigilance monitoring smartphone application to prevent road traffic accidents with audible smartphone alerts.