An Improved Controller Area Network Data-Reduction Algorithm for In-Vehicle Networks

2017 ◽  
Vol E100.A (2) ◽  
pp. 346-352 ◽  
Author(s):  
Yujing WU ◽  
Jin-Gyun CHUNG
Keyword(s):  
Data Reduction ◽  
Controller Area Network ◽  
Network Data ◽  
Area Network ◽  
Reduction Algorithm ◽  
Vehicle Networks

scholarly journals Development of Automatic Verification Environment for In-Vehicle Controller Area Network

2019 ◽  
Vol 7 (5) ◽  
pp. 05-10
Author(s):  
Tain-Lieng Kao ◽  
San-Yuan Wang ◽  
Ming-Hua Wu
Keyword(s):  
High Performance ◽  
Controller Area Network ◽  
Automatic Verification ◽  
Area Network ◽  
Automotive Systems ◽  
Control Units ◽  
Management Function ◽  
Vehicle Networks ◽  
Verification Process ◽  
Autopilot Systems

Due to the development of modern techniques, in the recent years, electronic vehicles and autopilot systems have beensignificant emerged in automobile and IT industrial. This leads the electronics automotive systems and auto-control systems consistedof a lot of high performance Electronic Control Units(ECUs) connected by controller area network (CAN). For realizing morecomplicated design in ECUs, this work integrates real-time OS and network management function. The results improve the CANbusnodes' designing level to as a gateway to interconnect CANbus nodes. As the number of CANbus nodes increase, the verification processis more and more complicated and takes much time. For speeding up the verification process, this work uses CANoe package toprogram the testing script for automotive verification environment. Then the engineer can connect the testing device by CAN to theenvironment for automatic verification. The engineer can define the network messages of the CANbus nodes and tune the design asthe validating progress. The testing results present as XML format and can be transferred to HTML pages for readability. Hence, thiswork realizes an automatic verification environment for CANbus in-vehicle networks.

scholarly journals CAN-D: A Modular Four-Step Pipeline for Comprehensively Decoding Controller Area Network Data

2021 ◽  
pp. 1-1
Author(s):  
Miki Elizabeth Verma ◽  
Robert Anthony Bridges ◽  
Jordan Jeffrey Sosnowski ◽  
Samuel C Hollifield ◽  
Michael David Iannacone
Keyword(s):  
Controller Area Network ◽  
Network Data ◽  
Area Network

scholarly journals CA-CRE: Classification Algorithm-Based Controller Area Network Payload Format Reverse-Engineering Method

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2442
Author(s):  
Cheongmin Ji ◽  
Taehyoung Ko ◽  
Manpyo Hong
Keyword(s):  
Reverse Engineering ◽  
Controller Area Network ◽  
Third Party ◽  
Area Network ◽  
Electronic Control ◽  
Plain Text ◽  
Security Research ◽  
Control Units ◽  
Vehicle Networks ◽  
Gain Access

In vehicles, dozens of electronic control units are connected to one or more controller area network (CAN) buses to exchange information and send commands related to the physical system of the vehicles. Furthermore, modern vehicles are connected to the Internet via telematics control units (TCUs). This leads to an attack vector in which attackers can control vehicles remotely once they gain access to in-vehicle networks (IVNs) and can discover the formats of important messages. Although the format information is kept secret by car manufacturers, CAN is vulnerable, since payloads are transmitted in plain text. In contrast, the secrecy of message formats inhibits IVN security research by third-party researchers. It also hinders effective security tests for in-vehicle networks as performed by evaluation authorities. To mitigate this problem, a method of reverse-engineering CAN payload formats is proposed. The method utilizes classification algorithms to predict signal boundaries from CAN payloads. Several features were uniquely chosen and devised to quantify the type-specific characteristics of signals. The method is evaluated on real-world and synthetic CAN traces, and the results show that our method can predict at least 10% more signal boundaries than the existing methods.

Analysis of Vehicles' Daily Fuel Consumption Frontiers with Long-Term Controller Area Network Data

2015 ◽  
Vol 2503 (1) ◽  
pp. 100-109 ◽  
Author(s):  
Dawei Li ◽  
Tomio Miwa ◽  
Takayuki Morikawa
Keyword(s):  
Fuel Consumption ◽  
Stochastic Frontier ◽  
Random Coefficients ◽  
Controller Area Network ◽  
Network Data ◽  
Area Network ◽  
Linear Regression Models ◽  
Explanatory Variables ◽  
Modeling Methodology

The vehicle fuel consumption frontier (VFCF) is the unobserved maximum amount of fuel that an individual private car user is willing to consume for driving. This study incorporated interindividual and intraindividual variations into the modeling of VFCF. Long-term controller area network data collected from private cars during 10 months in Toyota City, Japan, were used. A stochastic frontier model with random parameters was applied as the modeling methodology to deal with the panel data. The data fit of the estimation results demonstrated that models with random coefficients were preferable and had better model fits than the ordinary linear regression models. VFCFs on working days were significantly affected by the departure time of the first trip, temperature, weather, home location, gender, age, and occupation. All explanatory variables, except weather and temperature, also significantly affected VFCFs on holidays. Predictions made with the estimated parameters showed that the expected VFCFs were about double the corresponding actual vehicle fuel consumption expenditures.

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