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 study in @cite_0 implemented an Android-based application that monitored the vehicle via an OBD II interface by measuring the air-bag trigger and G-force experienced by the passenger during a collision, to detect accidents.
@cite_0
2074967085 1965068863 2070420170 1498765256
The increasing activity in the Intelligent Transportation Systems (ITS) area faces a strong limitation: the slow pace at which the automotive industry is making cars "smarter". On the contrary, the smartphone industry is advancing quickly. Existing smartphones are endowed with multiple wireless interfaces and high computational power, being able to perform a wide variety of tasks. By combining smartphones with existing vehicles through an appropriate interface we are able to move closer to the smart vehicle paradigm, offering the user new functionalities and services when driving. In this paper we propose an Android-based application that monitors the vehicle through an On Board Diagnostics (OBD-II) interface, being able to detect accidents. Our proposed application estimates the G force experienced by the passengers in case of a frontal collision, which is used together with airbag triggers to detect accidents. The application reacts to positive detection by sending details about the accident through either e-mail or SMS to pre-defined destinations, immediately followed by an automatic phone call to the emergency services. Experimental results using a real vehicle show that the application is able to react to accident events in less than 3 seconds, a very low time, validating the feasibility of smartphone based solutions for improving safety on the road. 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. 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. This study implemented a mobile diagnosing system that provides user-centered interfaces for more precisely estimating and diagnosing engine conditions through communications with the self-developed ECU only for industrial CRDI engine use. For the implemented system, a new protocol was designed and applied based on OBD-II standard to receive engine data values of the developed ECU. The designed protocol consists of a message structure to request data transmission from a smartphone to ECU and a response message structure for ECU to send data to a smartphone. It transmits 31 pieces of engine condition information simultaneously and sends the trouble diagnostic code. Because the diagnostic system enables real-time communication through modules, the engine condition information can be checked at any time. Thus, because when troubles take place on the engine, users can check them right away, quick response and resolution are possible, and stable system management can be expected.