A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Eco Driving <s> Abstract This study is aimed at finding independent measures to describe the dimensions of urban driving patterns and to investigate which properties have main effect on emissions and fuel-use. 62 driving pattern parameters were calculated for each of 19 230 driving patterns collected in real traffic. These included traditional driving pattern parameters of speed and acceleration and new parameters of engine speed and gear-changing behaviour. By using factorial analysis the initial 62 parameters were reduced to 16 independent driving pattern factors. Fuel-use and emission factors were estimated for a subset of 5217 cases using two different mechanistic instantaneous emission models. Regression analysis on the relation between driving pattern factors and fuel-use and emission factors showed that nine of the driving pattern factors had considerable environmental effects. Four of these are associated with different aspects of power demand and acceleration, three describe aspects of gear-changing behaviour and two factors describe the effect of certain speed intervals. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Eco Driving <s> As vehicle manufacturers continue to increase their emphasis on safety with advanced driver-assistance systems (ADASs), we propose a device that is not only already in abundance but portable enough as well to be one of the most effective multipurpose devices that are able to analyze and advise on safety conditions. Mobile smartphones today are equipped with numerous sensors that can help to aid in safety enhancements for drivers on the road. In this paper, we use the three-axis accelerometer of an Android-based smartphone to record and analyze various driver behaviors and external road conditions that could potentially be hazardous to the health of the driver, the neighboring public, and the automobile. Effective use of these data can educate a potentially dangerous driver on how to safely and efficiently operate a vehicle. With real-time analysis and auditory alerts of these factors, we can increase a driver's overall awareness to maximize safety. <s> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Eco Driving <s> Emerging electric-drive vehicles, such as hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs), hold the potential for substantial reduction of fuel consumption and greenhouse gas emissions. User driving behavior, which varies from person to person, can significantly affect (P)HEV operation and the corresponding energy and environmental impacts. Although some studies exist that investigate vehicle performance under different driving behaviors, either directed by vehicle manufacturers or via on-board diagnostic (OBD) devices, they are typically vehicle-specific and require extra device/effort. Moreover, there is no or very limited feedback to an individual driver regarding how his/her personalized driving behavior affects (P)HEV performance. ::: ::: This paper presents a personalized driving behavior monitoring and analysis system for emerging hybrid vehicles. Our design is fully automated and non-intrusive. We propose phone-based multi-modality sensing that captures precise driver---vehicle information through de-noise, calibration, synchronization, and disorientation compensation. We also provide quantitative driver-specific (P)HEV analysis through operation mode classification, energy use and fuel use modeling. The proposed system has been deployed and evaluated with real-world user studies. System evaluation demonstrates highly-accurate (0.88-0.996 correlation and low error) driving behavior sensing, mode classification, energy use and fuel use modeling. <s> BIB003 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Eco Driving <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> BIB004 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Eco Driving <s> The European Commission has recently promoted research programs aimed at finding solutions to the ever more compelling problem of air pollution from road vehicles and has also indicated among the possible impacts of cooperative Intelligent transportation a better sustainability, by cutting down pollutant emissions and reducing consumptions. In fact many practical solutions will develop that allow drivers and managements to optimize resources and to contain costs and the emissions of pollutants by applying communication systems between vehicles (V2V) and between vehicles and infrastructure (V2I). Along this mainstream this paper present a cooperative system which offer drivers the ability to manage their consumption and driving style, suggesting corrections to the behavior usually adopted. The new contribution of this paper is the cooperative approach between drivers to achieve a common goal of a better common energy consumption strategy. Since the fuel consumption has to be evaluated with regards to the specific vehicle type the system is based also on crowdsourcing of the specific vehicle consumption performances. The paper describes a system that gathers data on fuel consumption from the cooperating drivers that by can build together the data set necessary to the system itself and accept this new paradigm: crowd sourced cooperation for a better world. <s> BIB005 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Eco Driving <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> BIB006
The concept of "eco driving" (economic driving) represents a driving culture where smarter and safer driving results in more fuel-efficient driving which contributes to environmental protection and pollution prevention. According to BIB004 , the difference in fuel consumption (and thus gas emissions) between a normal driving style and an aggressive driving style is estimated to be above 40%, in favour of the normal driving style. Therefore, the promotion of eco driving will greatly contribute towards green projects and cost-effective driving due to improved fuel efficiency. The EcoSmart and TutorDrive collaborated project in BIB005 directly address the application of improved driving economy. The properties of urban driving that have the greatest effect on emissions and fuel consumption are described BIB001 . Driving pattern parameters such as speed, acceleration and gear-changing behaviour were investigated, and the influence of these parameters analysed through regression analysis. The analysis was dependent on the environmental impact such as the route choice and was included in the study. It was found that speeding in itself does not influence emission considerably, and the attempt to lower speed limits for traffic safety reasons are not key problems from an environmental point of view. Instead, it was motivated that the main focus should fall on adapting environments, driving style and vehicles in a way that discourages heavy acceleration, power demand, and high engine revolutions. An eco-friendly and safe driving style can therefore be maintained with the avoidance of sudden accelerations, rapid braking, and cornering actions. Real-time fuel consumption optimization is performed in many driver behaviour applications, and in BIB006 the driver is made aware of fuel burdensome actions by assessing driving behaviour with a smartphone-based system. Another solution based on Fuzzy logic, that makes use of a driver scoring approach, addresses fuel consumption using a smartphone BIB002 . The emergence of electric drive vehicles, like hybrid electric vehicles (HEVs), promises a considerable decrease in fuel consumption and greenhouse gas emissions BIB003 . However, this benefit offered by HEV technology will be greatly influenced by driving behaviour that deviates substantially between drivers. A personalised driving behaviour monitoring system for hybrid vehicles was introduced that addresses vehicle performance enhancement, to fill the vacant market for HEV fuel optimization BIB003 .