• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.10a
• /
• pp.98-101
• /
• 2009

Recently, there is use of the vehicle network for vehicle diagnostic method and Increased use of the vehicle protocol such as (CAN(Controller Area Network), MOST, LIN, FlexRay), Distributed control and data about the vehicle are being sought methods for real-time observation and monitoring and trend tends to have gone into this. In this case of automotive diagnostic module in today, there is Primarily to use DLC(Data Link Connector)Connector called self-check terminal. Generally, vehicle Diagnoses to use DLC Connector such as OBD2(On Board Diagnostics) Through Diagnostic Module(scanner). But there limit diagnostic as engine and powertrain part, and not consider user's perspective In this paper, By designing Vehicle diagnostic monitoring system using Ethernet Port, transmit and Receives CAN protocol vehicle data, and implement Easily monitoring system that provide and Diagnoses to provide vehicle's state and information to use PC.

• IEIE Transactions on Smart Processing and Computing
• /
• v.3 no.5
• /
• pp.313-318
• /
• 2014

Recently, Ethernet-based Diagnostic Over Internet Protocol (DoIP) was applied to automotive systems, and in-vehicle gateways have been introduced to integrate Ethernet with traditional in-vehicle networks, such as the local interconnect network (LIN), controller area network (CAN) and FlexRay. The introduction of in-vehicle gateways and of Ethernet based diagnostic protocols not only decreases the complexity of the networks, but also reduces the update time for ECU software reprogramming while enabling the use of a range of services, including remote diagnostics. In this paper, a diagnostic gateway was implement for an automotive system, and the performance measurements are presented. In addition, a range of applications provided by the diagnostic gateway are proposed.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.12
• /
• pp.1959-1968
• /
• 2016

The recent automobiles, a number of ECU inside the vehicle has been used. Also, each ECU is connected to different types of networks in accordance with the characteristics. Therefore, efficient data exchange between discrete network has emerged as a very important element. The gateway is responsible for the ability to exchange data between discrete network. In this study, we propose the new gateway algorithm to provide the structure of the mapping table to improve the efficiency of data exchange between discrete network. Also it provides a structure of a new gateway algorithm with a function of adjusting the priority of the data to be transmitted to another network arbitrarily. Moreover, the proposed gateway structure may simultaneously convert the transmission data input from a particular network to multiple networks. Another advantage is easy to change the entire data structure only if we change the table structure in the gateway.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.12
• /
• pp.5842-5861
• /
• 2019

A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this paper proposes "An Optimal Driving Support Strategy(ODSS) based on an Genetic Algorithm for Autonomous Vehicles" which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle(consumable conditions, RPM levels etc.). The proposed ODSS consists of 4 modules. The first module is a Data Communication Module (DCM) which converts CAN, FlexRay, and HSCAN messages of vehicles into WAVE messages and sends the converted messages to the Cloud and receives the analyzed result from the Cloud using V2X. The second module is a Data Management Module (DMM) that classifies the converted WAVE messages and stores the classified messages in a road state table, a sensor message table, and a vehicle state table. The third module is a Data Analysis Module (DAM) which learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. The fourth module is a Data Visualization Module (DVM) which displays the optimal driving strategy and the current driving conditions on a vehicle monitor. This paper compared the DCM with existing vehicle gateways and the DAM with the MLP and RF neural network models to validate the ODSS. In the experiment, the DCM improved a loss rate approximately by 5%, compared with existing vehicle gateways. In addition, because the DAM improved computation time by 40% and 20% separately, compared with the MLP and RF, it determined RPM, speed, steering angle and lane changes faster than them.