This paper describes the work that has been done in the design and development of a wireless on-board diagnostic system (OBD II) fleet management system. The system aims to measure speed, distance, and fuel consumption of vehicles for tracking and analysis purposes. An OBD II reader is designed to measure speed and mass air flow, from which the distance and fuel consumption are also computed. This data is then transmitted via WiFi to a remote server. The system also implements global positioning system tracking to determine the location of the vehicle. A database management system is implemented at the remote server for the storage and management of transmitted data and a graphical user interface is developed for analysing the transmitted data. Various qualification tests are conducted to verify the functionality of the system. The results demonstrate that the system is capable of reading the various parameters, and can successfully process, transmit, and display the readings.
The impact of driving behaviour on fuel consumption was monitored in @cite_16 by measuring various parameters such as mass air flow using a Bluetooth OBD II reader. An Android application was used to view the parameters measured for analysis. The measured data was then sent to a web-based remote server. This system exploits the advantage of vehicle on-board systems by using accessible parameters to perform fuel consumption calculations.
@cite_16
2070420170 1965068863 1565069923 2171801645
We evaluate a mobile application that assesses driving behavior based on in-vehicle acceleration measurements and gives corresponding feedback to drivers. In the insurance business, such applications have recently gained traction as a viable alternative to the monitoring of drivers via "black boxes" installed in vehicles, which lacks interaction opportunities and is perceived as privacy intrusive by policyholders. However, pose uncertainty and other noise-inducing factors make smartphones potentially less reliable as sensor platforms. We therefore compare critical driving events generated by a smartphone with reference measurements from a vehicle-fixed IMU in a controlled field study. The study was designed to capture driver variability under real-world conditions, while minimizing the influence of external factors. We find that the mobile measurements tend to overestimate critical driving events, possibly due to deviation from the calibrated initial device pose. While weather and daytime do not appear to influence event counts, road type is a significant factor that is not considered in most current state-of-the-art implementations. AbstractOrdinarily, a driver knows the current driving state of a vehicle through the On Board Diagnosis-II (OBD-II) data. Lately convenience devices related to real-time vehicle control and driving information data have been using OBD-II from the vehicle network.However, when these devices receive vehicle data rom the OBD-II network, each device receives its own information separately using its own vehicle network. If the driver changes the product and OBD-II connector to use OBD-II, the driver also changes the OBD-II connector. As a result, the driver spends useless money for the product and vehicle to use OBD-II.In this paper, we implemented integrating an OBD-II connector that uses Bluetooth, Wi-Fi, and WCDMA modules to overcome the above disadvantage. Despite the recent technological improvements in vehicles and engines, and the introduction of better fuels, road transportation is still responsible for air pollution in urban areas due to the increasing number of circulating vehicles, and their relative travelled distances. We develop a methodology to calculate, in real-time, the consumption and environmental impact of spark ignition and diesel vehicles from a set of variables such as Engine Fuel Rate, Speed, Mass Air Flow, Absolute Load, and Manifold Absolute Pressure, all of them obtained from the vehicle's Electronic Control Unit (ECU). Our platform is able to assist drivers in correcting their bad driving habits, while offering helpful recommendations to improve fuel economy. In this paper we will demonstrate through data mining, to what extent does the driving style really affect (negatively or positively) the fuel consumption, as well as the increase or reduction of greenhouse gas emissions generated by vehicles. This paper presents methods for collecting and analyzing physiological data during real-world driving tasks to determine a driver's relative stress level. Electrocardiogram, electromyogram, skin conductance, and respiration were recorded continuously while drivers followed a set route through open roads in the greater Boston area. Data from 24 drives of at least 50-min duration were collected for analysis. The data were analyzed in two ways. Analysis I used features from 5-min intervals of data during the rest, highway, and city driving conditions to distinguish three levels of driver stress with an accuracy of over 97 across multiple drivers and driving days. Analysis II compared continuous features, calculated at 1-s intervals throughout the entire drive, with a metric of observable stressors created by independent coders from videotapes. The results show that for most drivers studied, skin conductivity and heart rate metrics are most closely correlated with driver stress level. These findings indicate that physiological signals can provide a metric of driver stress in future cars capable of physiological monitoring. Such a metric could be used to help manage noncritical in-vehicle information systems and could also provide a continuous measure of how different road and traffic conditions affect drivers.