Automotive Data Traffic Filtering and Classification with Finding Errors

Abstract

Due to the rapid development of automotive technology and the rapid increase in number of advanced driver assistance systems (ADAS) in today’s vehicles, the amount of automotive data traffic is significantly rising day by day. Finding errors and disrupted messages in vehicle communication network is crucial part of vehicle safety system, as well as the safety of the whole its environment. The focus of this paper is analyzing the automotive data traffic transmitted via the CAN bus in the vehicle. The proposed software solution parses the.asc log which contains all the traffic data on the bus. The software solution filters and classifies messages according to their names and routing direction, and according to the routing rules in the network that are described in the routing scenario. Furthermore, proposed solution finds errors by looking for irregularities in data block and checking the response time of the message. As a result of the software solution, a report is given in form of.xlsx file. Based on this report, it is possible to analyze the communication and take steps to improve future communication. The proposed solution is created in Python script language and has features of modularity and upgradability since it is created in modules that can be modified independently of other modules. Verification was carried out within the software solution so that different cases were created by entering anomalies into the.asc file. The results of the verification are positive and meet the requirements set prior to making the solution itself. The proposed solution solves the problem of displaying dry data so that all data read from the bus is filtered, classified and displayed in graphical form for easier analysis of the data itself.