Controller Area Network Bus Data Research Articles

A Multivariate Time Series Prediction Method for Automotive Controller Area Network Bus Data

A Multivariate Time Series Prediction Method for Automotive Controller Area Network Bus Data

A Multivariate Time Series Prediction Method for Automotive Controller Area Network Bus Data

This study addresses the prediction of CAN bus data, a lesser-explored aspect within unsupervised anomaly detection research. We propose the Fast-Gated Attention (FGA) Transformer, a novel approach designed for accurate and efficient prediction of CAN bus data. This model utilizes a cross-attention window to optimize computational scale and feature extraction, a gated single-head attention mechanism in place of multi-head attention, and shared parameters to minimize model size. Additionally, a generalized unbiased linear attention approximation technique speeds up attention block computation. On three datasets—Car-Hacking, SynCAN, and Automotive Sensors—the FGA Transformer achieves predicted root mean square errors of 1.86 × 10−3, 3.03 × 10−3, and 30.66 × 10−3, with processing speeds of 2178, 2768, and 3062 frames per second, respectively. The FGA Transformer provides the best or comparable accuracy with a speed improvement ranging from 6 to 170 times over existing methods, underscoring its potential for CAN bus data prediction.

Optimal Control Design and Online Controller-Area-Network Bus Data Analysis for a Light Commercial Hybrid Electric Vehicle

In this article, a hybrid powertrain for the Volkswagen (VW) Crafter is designed using the Model-In-The-Loop (MIL) method. An enhanced Proportional-Integral (PI) control technique based on integral cost functions is developed by carrying out a time-based simulation in MATLAB/Simulink software to realize the optimal fuel economy of the vehicle. Moreover, a comparative study is conducted between the vehicle’s hybrid and pure electric versions to assess the optimal battery energy consumption per unit distance traveled. Communication within our vehicles’ Electronic Control Units (ECUs) is facilitated by a message-based protocol called a Controller Area Network (CAN). Consequently, this paper presents an online CAN Bus data analysis using the Hardware-In-The-Loop (HIL) method. This method uses a standard frame, J1939 CAN protocol, implemented with Net CAN Plus 110 hardware. A graphical user interface is developed on a host Personal Computer (PC) using LabVIEW for decoding the acquired raw CAN data to physical values. The simulation results reveal that the proposed controller is promising and suitable for realizing optimal performance over the HIL method.

Estimating Battery Size Requirements for Tractor Electrification of Row-Crop Planting Operations

Highlights Calculate and compare engine power needed for row-crop planting from ASAE standards and reported CAN data. Estimate energy requirements and the equivalent electric battery’s volume and mass for row-crop tractor. Discuss the feasibility for electrification of a fully electric row-crop tractor. Abstract. Power sources such as batteries, used for both on-road and off-road vehicles, are advancing at a rapid pace. Electric batteries are becoming more power dense, thus allowing them to be used as a power source to replace previous diesel or gasoline-powered systems. Efforts are underway to transition off-road agricultural vehicles from Internal Combustion Engine (ICE) vehicles to electric vehicles (EVs); however, the energy requirements of typical agricultural field operations need to be fully understood before such a transition can occur. Theoretical prediction equations available in the American Society of Agricultural and Biological Engineers (ASABE) standards or the use of engine load data from a tractor’s Controller Area Network (CAN) bus are two methods for determining the energy demands of implements on tractors. In this study, tractor CAN bus data was collected from multiple no-till corn planting operations to estimate the energy requirements of the planting operation in kWh. The estimated energy requirement was used to determine the equivalent physical mass and volume of a lithium-ion battery needed to power a hypothetical fully electric tractor for comparable planting operations. The estimated battery capacities using the worst-case field-use scenario in this study were 1117 kWh for operating a 16-row planter to plant 68 ha (168 acre) in a day at an average ground speed of 8.6 km h-1 (5.4 mph) for 14 hours, and 2658 kWh for operating a 48-row planter to plant 158 ha (391 acre) in a day at an average ground speed of 8.9 km h-1 (5.6 mph) for 15 hours. Given the current battery energy density requirements, the equivalent battery masses and volumes were found to be 5,319 kg and 3.33 m3, 12,657 kg and 7.93 m3, for 16-row planter and 48-row planter, respectively. These high kWh estimates needed to power future fully-electric tractor power units are based on worst-case scenarios that could be encountered in real field operations that use wide planters over long operating hours. Keywords: Battery, Controller Area Network, Electric Vehicles, Electrification, No-till, Planting, Row-crop, Tractor.

A Formal Modeling and Verification Scheme with an RNN-Based Attacker for CAN Communication System Authenticity

To enhance the attack resistance of the Controller Area Network (CAN) system and optimize the communication software design, a comprehensive model that combines a variable attacker with the CAN bus (VACB) is proposed to evaluate the bus communication risk. The VACB model consists of a variable attacker and the CAN bus model. A variable attacker is a visualized generation of the attack traffic based on a recurrent neural network (RNN), which is used to evaluate the anti-attack performance of the CAN bus; the CAN bus model combines the data link layer and the application layer to analyze the anomalies in CAN bus data transmission after the attack message. The simulation results indicate that the transmission accuracy and successful response rate decreased by 1.8% and 4.3% under the constructed variable attacker. The CAN bus’s authenticity was promoted after the developers adopted this model to analyze and optimize the software design. The transmission accuracy and the successful response rate were improved by 2.5% and 5.1%, respectively. Moreover, the model can quantify the risk of potential attacks on the CAN bus, prompting developers to avoid it in early development to reduce the loss caused by system crashes. The comprehensive model can provide theoretical guidance for the timing design of embedded software.

Third-Party Vehicle Data Collection for Advanced Driver Assistance Systems Analysis

Logs of vehicle controller area network (CAN) bus traffic supplemented by accelerometer and GPS data can provide valuable information about the use and operation of advanced driver assistance systems (ADAS) to the broader safety research community. Although CAN bus message codes are often manufacturer-specific, third-party libraries provide partial decoding of messages from many vehicle models, which can be augmented by reverse-engineering additional signals. This study explored the value of CAN bus, accelerometer, and GPS data that were logged on a variety of light vehicle models with an emphasis on availability of lane keep assist/lane departure warning, and adaptive cruise control. This study demonstrated that in-vehicle ADAS variables such as system on/off status and whether the system is actively controlling the vehicle could be determined on a variety of vehicle types. Associated control variables such as steering wheel angle, gas pedal state, and brake pedal state could also be determined from most vehicles tested. CAN messages, together with roadway features identified via GPS location, can provide a richer understanding of ADAS efficacy. Comparisons of message structure between models may also inform standardization efforts for electronic data recorders and telematics.

Identifying Big Five Personality Traits through Controller Area Network Bus Data

As adapting vehicles to drivers’ preferences has become an important focus point in the automotive sector, a more convenient, objective, real-time method for identifying drivers’ personality traits is increasingly important. Only recently has increased availability of driving signals obtained via controller area network (CAN) bus provided new perspectives for investigating personality differences. This study proposes a new methodology for identifying drivers’ Big Five personality traits through driving signals, specifically accelerator pedal angle, frontal acceleration, steering wheel angle, lateral acceleration, and speed. Data were collected from 92 participants who were asked to drive a car along a pre-defined 15 km route. Using statistical methods and the discrete Fourier transform, some time-frequency features related to driving were extracted to establish models for identifying participants’ Big Five personality traits. For these five personality trait dimensions, the coefficients of determination of effective predictive models were between 0.19 and 0.74, the root mean squared errors were between 2.47 and 4.23, and the correlations between predicted scores and self-reported questionnaire scores were considered medium to strong (0.56–0.88). The results showed that personality traits can be revealed through driving signals, and time-frequency features extracted from driving signals are effective in characterizing and identifying Big Five personality traits. This approach could be of potential value in the development of in-car integration or driver assistance systems and indicates a possible direction for further research on convenient psychometric methods.

Real-Time Control Based on a CAN-Bus of Hybrid Electrical Systems

Power management of a one-converter parallel structure with battery and supercapacitor is addressed in this paper. The controller is implemented on a DSP from a Microchip and uses a Controller Area Network (CAN) bus communication for data exchange. However, the low data transmission rate of the CAN bus data impacts the performances of regular power management strategies. This paper details an initial strategy with a charge sustaining mode for an application coupling a battery with supercapacitors, in which low performances have been witnessed due to the high sampling time of the CAN bus data. Therefore, a new strategy is proposed to tackle the sample time issue based on a depleting mode. Simulation and experimental results with a dsPIC33EP512MU810 DSP based on a 10 kW hybrid system proves the feasibility of the proposed approach.

Design and Implementation of an Elevator Power Failure Warning System

The elevator power failure is a serious safety accident and needs to be detected in time. Therefore, it is essential to monitor this situation via up-to-date technology. A novel warning system is proposed for the accident of the elevator power failure. Firstly, the data collector based on STM32 microcontroller is designed for collecting the elevator operation data from the Controller Area Network (CAN) bus interface of the elevator. Then these data are sent to the remote monitoring platform via the NB-IoT module. The platform extracts various operating signals of the elevator from the CAN bus data, such as door opening and closing signals, internal stroke signals, and stopping operation signals. Therefore, the warning system can further determine whether there is a power outage or the elevator is trapped.

Tractor CAN bus interface tools and application development for real-time data analysis

Embedded electronic controllers connected via controller area network (CAN) are used extensively in modern off-road machinery to broadcast machine information for on-board processes and diagnostics. A variety of tools are commercially available to monitor and record CAN data for research and commercial uses. Some plug-and-play options are easy to set up but have limited customization. Others are more complex to configure but can be used to create fully customized and deployable real-time software applications. Several CAN data tools and software packages were evaluated to monitor and record machine and engine data. A proof-of-concept application was created with Matlab Vehicle Network Toolbox for real-time monitoring of diesel engine speed and load, and generation of a cumulative speed/load histogram based on SAE J1939 parameters. This study investigated four hardware and software CAN tool configurations that could be used for tractor CAN bus data acquisition. This study is not intended to be an exhaustive evaluation of all available CAN tools or their capabilities, rather it was intended to present information on several readily available hardware and software platforms and a method for creating a stand-alone custom application as a proof-of-concept for tractor CAN data analysis. The custom application developed will facilitate in-cab decision making for improved tractor management during field operations.

Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment

Cars can nowadays record several thousands of signals through the controller area network (CAN) bus technology and potentially provide real-time information on the car, the driver, and the surrounding environment. This paper proposes a new methodology for near-real-time analysis and classification of driver behavior using a selected subset of CAN bus signals, specifically gas pedal position, brake pedal pressure, steering wheel angle, steering wheel momentum, velocity, RPM, longitudinal and lateral acceleration. Data have been collected in a completely uncontrolled experiment involving 54 people, where over 2000 trips have been recorded without any type of predetermined driving instruction on a wide variety of road scenarios. While only few works have analyzed the driving behavior of more than 50 drivers using CAN bus data, we propose an unsupervised learning technique that clusters drivers in different groups, and offers a validation method to test the robustness of clustering in a wide range of experimental settings. The minimal amount of data needed to preserve robust driver clustering is also computed, showing that by properly choosing a subsampling strategy it is possible to reduce the size of the database as much as 99% without impairing the clustering performance.

Comparing various hardware/software solutions and conversion methods for Controller Area Network (CAN) bus data collection

Various hardware and software solutions exist for collecting Controller Area Network (CAN) bus data. Digital data accuracy could vary based upon different data logging methods (e.g., hardware/software timing, processor timing, etc.). CAN bus data were collected from agricultural tractors using multiple data acquisition solutions to quantify differences among collection methods and demonstrate potential data accumulation rates.Two types of data were observed for this study. The first, CAN bus frame data, represents data collected for each line of hex data sent from an ECU. One issue with frame data is the resulting large file sizes, therefore a second logging format collected was an averaged frame signal, or waveform dataset. Because of its smaller file size, waveform data could be more desirable for long periods of collection. Percent difference was calculated from two sets of frame data logs using different hardware/software combinations, and a frame data log was also compared to a waveform data log.The resulting difference was less than 0.0025 RPM for engine speed comparisons, zero for fuel rate and fuel temperature comparisons, and the mean percent difference was less than 0.08% between the methods of data collection. The error production could have resulted from noise in hardware and processor times, but was not found to increase as time progressed. This showed that even though errors existed between logging methods, the magnitude of errors would not negatively impact any practical agricultural field research applications. Thus, data logged by the different devices was similar and files requiring less memory would be desired. Selecting a waveform CAN bus data logging option would likely maintain digital data accuracy while reducing file storage and processing needs.

Exploring Trip Fuel Consumption by Machine Learning from GPS and CAN Bus Data

This study aims to explore the trip fuel consumption from a large-scale dataset. To better understand how the multiple variables (e.g., average travel speed, trip distance) influence the trip fuel consumption, we propose the support vector machine (SVM) to learn the relationship between the trip fuel consumption and the corresponding factors. A large-scale global positioning system (GPS) and Controller Area Network (CAN) bus data provided by 153 probe vehicles during one month are used. Elasticity analysis indicates that trip distance and coefficient of variance of link speed have relatively great importance on the SVM model. To demonstrate the performance of the proposed method, three other regression methods, i.e., the multiple linear regression model, artificial neural network (ANN), and the link fuel summation SVM model (LSSVM) are also adopted for performance comparisons. The results show that SVM model is much closer to the target than the other three models.

Modeling extreme loads acting on steering components using driving events

Forces during steering events, such as curves and maneuvers, cause large stresses on steering components. In this paper, we formulate a model for the lateral loads causing fatigue damage of the steering components. Steering events are identified using a Hidden Markov model on the CAN (Controller Area Network) bus data. The CAN data is available on all vehicles, thus the model is applicable across many types of vehicles. To identify the events, the observation from CAN data is modeled by a multivariate generalized Laplace (GAL) distribution. An explicit formula for the expected fatigue damage is given. Results are validated using measured lateral acceleration.

Accuracy and Characteristics of 2012 Honda Event Data Recorders from Real-Time Replay of Controller Area Network (CAN) Traffic

Prior EDR testing methodologies required setting events in the airbag control module in the vehicle during controlled driving behavior. Duplicating events was nearly impossible, and it was difficult to separate how much differences in recorded speeds to reference speeds was due to measurement error, wheel slip, reporting time delays, or data truncation within the EDR. Recording thresholds have also increased making non-deployment and deployment events closer in magnitude, increasing the risk of accidentally exceeding the deployment threshold while setting events. The new methodology eliminates the risk of accidentally deploying airbags while gathering GPS and CAN bus data in the test vehicle. The techniques presented in this paper also allows gathering of data in vehicle without tampering with the airbag control module, which reduces the potential liability to testers using rental or borrowed test vehicles. The new methodology allows for repeatable testing and mapping the transfer function between the vehicle CAN bus data and the EDR. Should a manufacturer make a design change to an ACM EDR, identical inputs can be given to exemplar ACM's from before and after the changes to document any change in the transfer function. This methodology allows researchers the ability to re-create events of interest in a low-cost, repeatable manner. The accuracy of the 2012 Honda CR-V and 2012 Honda Civic event data recorders were tested using this new two part methodology. First, the test vehicles were instrumented with both a Racelogic VBOX differential GPS speed measurement system and a Vector CAN data logger. The measurements from the VBOX were transmitted onto the vehicle's Controller Area Network (CAN) bus that also contained messages reflecting indicated vehicle speed, brake status, accelerator pedal position, steering wheel angle, individual wheel speeds and other signals. This put the GPS speed data on the same time base as the vehicle CAN speed signal such that no additional synchronization was required. This permitted analysis of the accuracy and update rate of the vehicle speed CAN signal, which is the source for speed data used in the Event Data Recorder (EDR). Second, a system was developed to replay the recorded CAN data to an exemplar airbag control module in the laboratory, such that the exemplar was receiving data exactly as if it were in a moving vehicle. A pneumatic fixture with a slide was built to allow the exemplar module to be accelerated to nearly 10 km/h (6 mph) and then stopped in about 80 msec. to create a non-deployment event that met the minimum 5 mph delta-V over 150ms threshold. Actuation of the event setting fixture was computer controlled (using LabVIEW) and synchronized with the CAN replay system so that the desired test condition could be replicated precisely. An external accelerometer was mounted to the airbag control module to mark when the ACM began recording and the accelerometer data was matched to the acceleration and delta-V data in the EDR. The desired test conditions were replayed to the airbag control module and a series of non-deployment events were set. Each event on the EDR data was read using the Bosch Crash Data Retrieval system. The EDR data was compared to the inputs, and it was determined that the two byte vehicle CAN bus signal was truncated to the next lower whole km/h when recorded in the EDR. Under steady state conditions the speed data was accurate within 2%. The vehicle CAN signal published new values every 0.1 seconds, and the CR-V updated values every 0.1 seconds, but the Civic only updated every 0.6 seconds during hard brake events. During hard ABS braking events the expected wheel slip led to the vehicle speed signal under-reporting ground speed, but the delay in the CAN bus update rate resulted in over-reporting ground speed in many of the tests. Language: en

Feasibility Study of Fuel Consumption Prediction Model by Integrating Vehicle-Specific Power and Controller Area Network Bus Technology

With the development of automobile technology, it becomes possible to estimate fuel consumption by using the driving parameters from the electronic control unit (ECU) and output the calculation results via the controller area network (CAN) bus. As a result, it is feasible to design an approach for estimating fuel consumption by integrating vehicle-specific power (VSP) and CAN bus technology because of the accessibility and stability of the CAN bus. To determine whether the CAN-based measured data can be used to build the relationship between VSP and fuel consumption to replace the traditional fuel consumption meter, a comparison of fuel consumption data collected from the ECU and the fuel consumption meter is conducted in this study. Results show that the relationship between the fuel consumption rate and VSP bin built by the CAN bus data is consistent with the relationship derived from the fuel consumption meter; this finding indicates that the CAN bus technology can be used to describe the relationship between vehicle activities and fuel consumption rates for light-duty vehicles. In addition, a comparison of CAN-based measured data with VSP-based predicted data shows that the prediction approach that integrates VSP and CAN bus technology needs an aggregation level of 60 s or longer, which thus can be used to estimate a long period of fuel consumption accumulation and a fuel consumption factor for various travel speeds and road types.

Research and Development of Hybrid Electric Vehicles CAN-Bus Data Monitor and Diagnostic System through OBD-II and Android-Based Smartphones

With the rapid development of the smartphone market, future cars seem to have more connections with intelligent cell phone and Internet. Intelligent transportation system (ITS) and telematics system have become research focus in recent years. There is an increasing demand for remote monitoring and diagnostic system as the further research of hybrid electric vehicle (HEV) goes on. In this paper, a remote controller area network bus (CAN-Bus) data monitor and diagnostic system for HEV is presented using on board diagnostic version-II (OBD-II) and Android-based smartphone. It is low-cost, convenient, and extensible with smartphone used in the system to realize communication with ELM327 and remote monitoring center wirelessly. The prototype of client and server is developed in Java language, and it is proved by the test that the system works stably and the collected data have practical values.

Accuracy of Event Data in the 2010 and 2011 Toyota Camry During Steady State and Braking Conditions

Independent verification of the accuracy of data from Event Data Recorders (EDRs) is useful when using the information to help reconstruct a crash. To this end, the accuracy of the EDR function of the Airbag Control Module (ACM) was tested on 2010 and 2011 Toyota Camry sedans during straight line operation. During steady state operation, and maximum ABS-braking runs starting from approximately 80 km/h (50 mph), and 113 km/h (70 mph), non-deployment events were artificially induced to store event data. Following each run, the EDR was imaged using the Bosch Crash Data Retrieval (CDR) system. The CDR reported speed values were compared to Racelogic VBox differential GPS speed records. Data recorders were also used to monitor the vehicle Controller Area Network (CAN) bus traffic, including the indicated speed, brake pressure, engine RPM, and accelerator pedal position. The speed and RPM reporting algorithms stated in CDR Data Limitations were confirmed. Exemplar graphs of EDR-reported speed/brake/RPM/accel pedal data versus GPS speed and CAN bus data are presented and discussed. The timing of the reported data with respect to the event is also discussed. The difference between vehicle speed recorded by the EDR and the GPS speed during steady state operation varied from +0.4 to −2.3 km/h, with the EDR typically reporting lower than the GPS. During heavy braking the difference in speed was observed to be from −7 to +15 km/h, with wheel slip causing negative differences, and time delay since the last CAN bus update causing positive differences. Language: en