The analysis and discussion of the onboard diagnostic data will help understand the driver's behavioral characteristics and develop a sustainable transportation system. The research content of this ...paper is to mine the driving behavior data through the vehicle preload equipment, analyze the factors affecting safe driving and establish a prediction model. This study collected data from 50 Taiwanese drivers while operating in heavy traffic. Understand the driver's behavioral characteristics through data analysis, such as calculating the times of the driver's emergency braking is based on the driving speed. From this result, the index of dangerous driving is defined. In addition, the dangerous driving index and other variables were analyzed, and it was found that there was a significant relationship between dangerous driving and vehicle mileage and maximum speed. According to the results, the drive speed increases the driving risk, the mileage reduces the risk probability, and the prediction accuracy is 78.9%. Complete data needs to be collected to evaluate complete driving behavior characteristics for developing sustainable transportation goals.
This research is created as a system for a car rental service. The system has monitoring features allowing the car rental owner to monitor the engine condition. The monitoring is performed by using a ...web interface that could read data from OBD-II that sent by raspberry pi 2. The owner can also monitor the position of the car by using coordinates sent from a smartphone by utilizing the GPS feature. In addition, it also has a reporting feature that allows the owner to track the data history from OBD II about the engine rpm, speed, engine load & temperature. Moreover, the owners can identify the route that has been passed by their car. Likewise, this system provides a program that analyzes the car driver's behavior based on the determined rules. The analysis was conducted based on all data from OBD-II in the database server and all data of driving rules violation performed by the driver. The result is an assessment of the driver as a driving error rate. This study generated 173 data of which 9 were driving rules violations with a 5.20% driving error rate. The report can be obtained by selecting the time interval. It also downloadable and can be sent by e-mail.
Road vehicles operations are continuously monitored through physical parameters (temperature, air flow, rotation rate); such measurements are retrieved by electronic sensors and communicated, over ...the internal vehicle communications protocol, towards the Main Control Unit for further processing. In this paper we present our selection of parameters for monitoring key vehicle operations and briefly describe the sensors employed for the retrieval of these parameter values. The values are retrieved through the OBD-II diagnostics protocol and they are related with the vehicle operation and with the fuel consumption. As proof of concept, focused experimentation has taken place, through a 5 km trip with low and heavy traffic. Values retrieved from the OBD-II scanner are presented and discussed. In terms of evaluation, the raw values as well as the calculated measurements related to fuel consumption are compared with manufacturer standards and the user driving behaviour has been identified as the key factor influencing the fuel consumption for a given model.
Since 1996, the California Air Resources Board has established the OBD-II as a communication protocol that systems use to control gas emissions. Such a standard was created in 1988 and allows modern ...vehicles to obtain technical engine data such as fuel injection, speed, and air mass flow. In this article, an experimental test bench is presented using the Raspberry-Pi 3 programming board and a serial connection to the OBD-II port. Also, the standard ELM327 is employed as a connection adapter to analyze data sets and to calculate the ideal time for hydrogen injection. This process is achieved by an in-situ generator as a supplementary fuel source for internal combustion engines. It permits the reduction of greenhouse gas emissions and improves fuel efficiency, as it has already been reported in multiple previous types of research. The methodology describes the process of acquiring data through the OBD-II port, the type of hydrogen generator, and the calibration implemented according to expert system rules as well as the installation process of the system in the experimental vehicle. The developed algorithm is implemented using the programming language Python to determine the ideal time for activating the hydrogen generator. The results include simulation behavior of the fuzzy logic algorithm in Matlab of vehicle engine for its comparison with the validated results and offer a proposal to optimize hydrogen injection for future researches. Then, the efficiency of electrolyzer in ICE is improved by extending the lifetime of the entire device and the electrolyte.
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•Experiments injecting H2 into ICE using Raspberry-Pi 3B and the OBD-II port.•An in-situ H2 generator (12 V/40 A, 1 L/min) as a supplemental fuel source for ICE.•Improvement HHO injection versus traditional ON/OFF control injection schemes.•Fuzzy Logic algorithm control implementation on portable hardware.
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, ...the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
In the last decade, the growth of the automotive market with the aid of technologies has been notable for the economic, automotive and technological sectors. Alongside this growing recognition, the ...so called Internet of Intelligent Vehicles (IoIV) emerges as an evolution of the Internet of Things (IoT) applied to the automotive sector. Closely related to IoIV, emerges the concept of Industrial Internet of Things (IIoT), which is the current revolution seen in industrial automation. IIoT, in its turn, relates to the concept of Industry 4.0, that is used to represent the current Industrial Revolution. This revolution, however, involves different areas: from manufacturing to healthcare. The Industry 4.0 can create value during the entire product lifecycle, promoting customer feedback, that is, having information about the product history throughout it is life. In this way, the automatic communication between vehicle and factory was facilitated, allowing the accomplishment of different analysis regarding vehicles, such as the identification of a behavioral pattern through historical driver usage, fuel consumption, maintenance indicators, so on. Thus, allowing the prevention of critical issues and undesired behaviors, since the automakers lose contact with the vehicle after the purchase. Therefore, this paper aims to propose a customer feedback platform for vehicle manufacturing in Industry 4.0 context, capable of collecting and analyzing, through an OBD-II (On-Board Diagnostics) scanner, the sensors available by vehicles, with the purpose of assisting in the management, prevention, and mitigation of different vehicular problems. An intercontinental evaluation conducted between Brazil and Italy locations shown the feasibility of platform and the potential to use in order to improve the vehicle manufacturing process.
Vehicles are the major source of air pollution in modern cities, emitting excessive levels of CO
and other noxious gases. Exploiting the OBD-II interface available on most vehicles, the continuous ...emission of such pollutants can be indirectly measured over time, although accuracy has been an important design issue when performing this task due the nature of the retrieved data. In this scenario, soft-sensor approaches can be adopted to process engine combustion data such as fuel injection and mass air flow, processing them to estimate pollution and transmitting the results for further analyses. Therefore, this article proposes a soft-sensor solution based on an embedded system designed to retrieve data from vehicles through their OBD-II interface, processing different inputs to provide estimated values of CO
emissions over time. According to the type of data provided by the vehicle, two different algorithms are defined, and each follows a comprehensive mathematical formulation. Moreover, an unsupervised TinyML approach is also derived to remove outliers data when processing the computed data stream, improving the accuracy of the soft sensor as a whole while not requiring any interaction with cloud-based servers to operate. Initial results for an embedded implementation on the Freematics ONE+ board have shown the proposal's feasibility with an acquisition frequency equal to 1Hz and emission granularity measure of gCO
/km.
Autonomous vehicles (AVs) have the potential to provide new paradigms to enhance the safety, mobility, and environmental sustainability of surface transportation. However, as vehicles become more ...computerized and internally interconnected by electronic control systems, their vulnerability to cyber-attacks is a fast-growing concern and a national priority. Evidence from the Internet virus suggests that AVs will have critical challenges posed by epidemic-style malware like Stuxnet. This self-propagating malware is a fast and powerful way of disrupting the AV system and transportation infrastructure. This study presents a mathematical model for Stuxnet-style malware's temporal and spatial spread. Taking cues from the field of epidemiology and ecology, the malware will be described as an infectious epidemic to capture the dynamics of temporal and spatial propagation behavior. This study is the first attempt to analyze the spread of Stuxnet-style malware on AVs. The future uses of such a model for the temporal-geographic spread of AVs-based infectious malware are discussed.
•Implementation of the vehicle's engine start/stop system.•In-depth analysis of the effect of the system on fuel consumption, noise production and the quantity of polluted gases.•The use of a ...reliable and inexpensive intelligent control system.
Prior to the emergence of alternative energy sources, research on the start-stop system, which shuts down the engine at idle, was rapidly conducted by car manufacturers, in parallel with the growing need for energy-efficient technologies and the tightening of environmental regulations for vehicles. In addition, in order to increase sales as well as convenience and fuel efficiency, car manufacturers are attempting to commercialize start-stop technology by merging it with the generic smart key system. This paper shows a new practical study to evaluate a start/stop system designed specifically for the job. We have built and implemented a complete electronic start-stop mechanism for an aftermarket smart key system that uses an ECU starter and OBD-II interface. In addition to meeting all specified response time thresholds for vehicle status requests, the implemented Start Stop system standardizes the interface with vehicles to reduce the time required to install the Start Stop system on various vehicles. The results of this study highlight the importance of the resting system in terms of reducing fuel consumption, limiting greenhouse gas emissions, and keeping the vehicle engine quiet.
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