A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Driver Performance Assessment <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> Driver Performance Assessment <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> BIB002 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Driver Performance Assessment <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> BIB003
For most people driving is a habitual and easy activity as it tends to be second nature to perform driving manoeuvres due to daily repetition. However, driving is a complex decision-making process because of the dynamic nature of various components. Components such as the driver, the vehicle and environment, as well as the relationship between these entities should be a key focus in driver performance assessment BIB001 . Driving capabilities of a person depend on the age of the driver, driving experience, their physical, emotional and psychological/mental state, as well as environmental influences such as the weather or the time of day. Four driving style classes were categorised and applied in a driving performance inference system, with the signature of multi-dimensional acceleration at the core of the solution, with the aim to allocate all possible driving events to one of these classes BIB002 . The proposed solution, performs classification in real-time, and proves to be cost-effective. EcoSmart is a mobile application that addresses fuel consumption improvement based on driving style as part of a remedial energy consumption and pollution strategy BIB003 . The EcoSmart application uses the principles of driver behaviour assessment from the kinematic data (acceleration and speed), in order to cooperate the driver to correct and adopt behaviour to proceed towards the ideal driving behaviour for optimal fuel consumption and energy emissions.