A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Fleet Management <s> Fleet management system is a rapid growing industry. This system helps institutions to manage vehicle fleet efficiently and effectively through smart allocation of resources. In this project, an intelligent fleet management system which incorporates the power of concurrent Global Positioning System (GPS) and Global System for Mobile Communications (GSM) real-time positioning, front-end intelligent and web-based management software is proposed. In contrast to systems which depend solely on GPS positioning, the proposed system provides higher positioning accuracy and is capable to track the target at areas where GPS signals are weak or unavailable. The terminal is powered by Front-End Intelligent Technology (FEI), a comprehensive embedded technology that is equipped with necessary artificial intelligence to mimic human intelligence in decision-making for quicker response, better accuracy and less dependence on a backend server. With less dependency on the backend, large scale fleet management system can be implemented more effectively. The proposed system is successfully implemented and evaluated on twenty vehicles including buses and cars in Universiti Teknologi Malaysia (UTM). Results from the test-bed shown that user can monitor and track the real-time physical location and conditions of their vehicles via Internet or Short Message Service (SMS). The web-based fleet management software also helped the user to manage fleets more effectively. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Fleet Management <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> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Fleet Management <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> BIB003 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Fleet Management <s> Ƞ Telematics form an important technology enabler for intelligent transportation systems. By deploying on-board diagnostic devices, the signatures of vehicle vibration along with its location and time are recorded. Detailed analyses of the collected signatures offer deep insights into the state of the objects under study. Towards that objective, we carried out experiments by deploying telematics device in one of the office bus that ferries employees to office and back. Data is being collected from 3-axis accelerometer, GPS, speed and the time for all the journeys. In this paper, we present initial results of the above exercise by applying statistical methods to derive information through systematic analysis of the data collected over four months. It is demonstrated that the higher order derivative of the measured Z axis acceleration samples display the properties Weibull distribution when the time axis is replaced by the amplitude of such processed acceleration data. Such an observation offers us a method to predict future behaviour where deviations from prediction are classified as context-based aberrations or progressive degradation of the system. In addition we capture the relationship between speed of the vehicle and median of the jerk energy samples using regression analysis. Such results offer an opportunity to develop a robust method to model road-vehicle interaction thereby enabling us to predict such like driving behaviour and condition based maintenance etc. <s> BIB004 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Fleet Management <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> BIB005
The area of fleet management is rapidly growing, and includes a range of applications such as vehicle maintenance, vehicle telematics (tracking and diagnostics), speed monitoring, driver monitoring, fuel management and health and safety inspections BIB002 . Through effective fleet management, companies can minimise the risk vehicles and drivers are subject to, improve efficiency of their service and reduce overhead costs. Some of these advances are now also driving government legislation in various countries. MIROAD (Mobile-Sensor-Platform for Intelligent Recognition Of Aggressive Driving), is an inexpensive, accessible and intelligent system designed and implemented in BIB002 , that satisfies certain fleet management requirements without mounting expensive camera and surveillance monitoring devices in a vehicle, but rather uses driver behaviour and driving style analysis techniques and a mobile sensor platform in order to detect aggressive driving. Other fleet management systems that utilise two-axis accelerometer and GPS readings, (for instance the application proposed by BIB003 , and the practically tested intelligent fleet management system in BIB001 ), are normally implemented on a web server with a web interface, where organizations can monitor and optimise their fleet in an easily accessible and effective manner. These fleet management systems focus on the importance of feedback and history tracking. The behaviour and integrity of a driver is essential in fleet management and fleet optimisation, as the driver takes responsibility for the fleet vehicle, which is a costly company asset, as well as the vehicle's load. Therefore, driver performance scoring practices can prove valuable in assessing the risk of an individual driver. Such applications include SenseFleet BIB005 , which identifies risky manoeuvres performed by the driver using a Fuzzy Inference System (FIS). Evaluation experiments on SenseFleet have resulted in accurate identification of risky driving manoeuvres based on acceleration, braking and cornering actions, and also provided representative scores for a large number of test cases. Another functionality of SenseFleet provides road network and traffic information feedback, which aids in the tasks of fleet management and route optimization. Currently telematics is a leading technology in fleet management, where data acquisition is performed by "black box" tracking devices or on-board vehicle diagnostic devices. In BIB004 , the prediction of future behaviour using real-time and historical data from telematics devices, enables the modelling of road-vehicle interaction which provides condition based maintenance information and other fleet management information specific to the driver. Scoring techniques are applied, and in this instance a hazard rate (HR) score is generated for each trip. The trip information with the accompanying HRs are used to model complex road-vehicle interaction where degradation of the suspension-damper system could be identified, and an algorithm could estimate hazard rates for different road types to enable better and safer route planning.