CAN Messages Research Articles

Synthetic image data generation via rendering techniques for training AI-based Instance Segmentation

Within the scope of the development of the project, the acceleration and gyro signal, VR implementation, and User Control for VR were realized. The starting point was a list of MPU9250 data, which are acceleration and gyro data. The goal is to do the signal processing of the signal that is got from MPU9250 and implement the data with a specific format so that the LINAK LA36 actuator can be controlled in such a way that it performs as desired position. For this purpose, a multi-axis acceleration and position sensor is mounted on a surfing board which then collects the acceleration and Gyro data. A MATLAB Program had been written to convert the desired position into a CAN message. Before that, the raw acceleration data and gyro must be converted and filtered into actual data. First, the acceleration data was collected from another group. A signal processing method has been implemented. This includes gravitational force filter, noise filter, integration, and complementary filter method was used in order to get the accurate actual data that is able to be implemented into the system. For those methods, it was necessary to deal extensively with the characteristics of signal processing. VR application was implemented in this project to give the user a sensational surfing experience in the virtual world. User control has been implemented as well to allow users to control the video. Unity with Arduino work together so that both Arduino and Unity can communicate with each other, and signal messages will be sent to the Arduino from Unity and vice versa to achieve the synchronization of the video signal and Unity VR application.

A Model for CAN Message Timestamp Fluctuations to Accurately Estimate Transmitter Clock Skews

A Model for CAN Message Timestamp Fluctuations to Accurately Estimate Transmitter Clock Skews

Acceleration/Deceleration Detection System - Creation of Driving Behavior Profiles for Efficiency

: Driving behavior is a term used to describe deliberate or accidental activities while a driver drives a vehicle. Driving behavior data is useful for efficiency in the automotive domain, including insurance companies, aftermarkets, production planning, and vehicle servicing. Therefore, the aim of this study is to develop and investigate an Acceleration/Deceleration(A/D) detection system. A/D can be measured using a 3-axis accelerometer. The accelerometer on the vehicle cannot precisely reflect the driver's actual A/D behavior due to its undetermined orientation. Additionally, the accelerometer detects sudden movements. In order to prevent unexpected acceleration and deceleration changes and to achieve the desired driving behavior, a gyroscope has been incorporated into the system. Thus, a driving aid system was developed using the combined data from the gyroscope and accelerometer. Moreover, the A/D detection system reads and combines the vehicle's CAN messages to more stabilize the system. As a result, driving behavior data during vehicle trips is collected and recorded to inform the driver how to have a more efficient driving experience and as well as making the related companies aware of the trips that have been made. Furthermore, they can be combined with service feedback and support the manufacturer's production strategy to increase efficiency.

CAVIDS: Real time intrusion detection system for connected autonomous vehicles using logical analysis of data

In recent times, Connected Autonomous Vehicles (CAVs) have been gaining popularity and have already begun to revolutionize the transport industry, mainly using real-time monitoring, analysis and exchange of data through In-Vehicle Networks (IVN). IVN can also interact with the outside world and roadside infrastructures. With all these advantages, certain weaknesses also creep in as the communication in IVN lacks proper security measures like encryption and authentication. These lack of security measures in IVN makes CAVs vulnerable to cyber-attacks which may have fatal consequences. The de facto standard for communication in IVN is known as Controller Area Network or CAN, and it also lacks said security features to protect CAN from outside threats like cyber attacks, sensor tempering etc. Real-time intrusion detection with minimal computational resources is the need of the hour to prevent disruptive consequences and fatal crashes. In this paper, we propose an intrusion detection method based on Logical Analysis of Data (LAD) which facilitates real-time intrusion detection in CAVs using minimal computational resources. LAD is a two-class classification technique which uses the concept of a partially defined Boolean function (pdBf) to extract different rules for classification from a historical dataset consisting of CAN messages only. Usually, the normal behaviour of any CAV is specified using the extracted rules, and any unusual behaviour that does not conform with the extracted rules is treated as an anomaly. In this paper, we have extracted rules for anomaly and treated all behaviours that do not conform with these extracted rules as normal. The proposed method outperforms many state-of-the-art approaches, which include other rule-based methods, machine learning and deep learning methods both in terms of F1 score and detection time. For CarChallenge 2020 dataset, the proposed method requires lesser than 47.5 μs for detection using a Raspberry Pi device, which is far less than the average time difference between two consecutive CAN messages and thus can be considered as real-time intrusion detection. Real-time intrusion detection is important in preventing any untoward incidents in CAVs.

Second generation Monitoring of Pixel System (MOPS) chip for the Detector Control System (DCS) of the ATLAS ITk Pixel detector

The MOPSv2 chip is an Application Specific Integrated Circuit (ASIC) to provide the temperature and the voltage monitoring data of individual front-end detector modules to the DCS of the ATLAS ITk Pixel detector. The chip implements CANopen in a hardwired logic, provides the possibility of remote reset without a power cycle and automated on-chip frequency trimming using CAN messages. The chip has proven to be radiation hard during testing up to an ionizing dose of 500 Mrad, immune to Single Event Upsets (SEUs) and works reliably under irradiation at high operating temperatures of up to 40 °C. In this paper, the functionality and performance of the second version of the chip will be discussed, and also results from the irradiation campaigns will be presented.

Federated Graph Neural Network for Fast Anomaly Detection in Controller Area Networks

Due to the lack of CAN frame encryption and authentication, CAN bus is vulnerable to various attacks, which can in general be divided into message injection, suspension, and falsification. Existing CAN bus anomaly detection mechanisms either can only detect one or two of these attacks, or require numerous CAN messages during predictions, which can hardly realize real-time performance. In this paper, we propose a CAN bus anomaly detection system that can detect all these attacks simultaneously in as short as 3 milliseconds (ms) based on Graph Neural Network (GNN). This work generates directed attributed graphs based on CAN message streams in given message intervals. Node attributes denote data contents in CAN messages while each edge attribute represents the frequency of a typical CAN ID pair in the given interval. Afterwards, a GNN is trained based on generated CAN message graphs. Considering highly imbalanced training data, a two-stage classifier cascade is developed in this paper, which is composed of a one-class classifier for anomaly detection and a multi-class classifier for attack classification. An openmax layer is further introduced to the multi-class classifier to tackle new anomalies from unknown classes. To take advantage of crowdsourcing while protecting user data privacy, we adopt federated learning to train a universal model that covers different driving scenarios and vehicle states. Extensive experiment results show the effectiveness and efficiency of our methodology.

DAGA: Detecting Attacks to In-Vehicle Networks via N-Gram Analysis

Recent research showcased several cyber-attacks against unmodified licensed vehicles, demonstrating the vulnerability of their internal networks. Many solutions have already been proposed by industry and academia, aiming to detect and prevent cyber-attacks targeting in-vehicle networks. The majority of these proposals borrow security algorithms and techniques from the classical ICT domain, and in many cases they do not consider the inherent limitations of legacy automotive protocols and resource-constrained microcontrollers. This paper proposes <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DAGA</b> , an anomaly detection algorithm for in-vehicle networks exploiting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n-$</tex-math></inline-formula> gram analysis. DAGA only uses sequences of CAN message IDs for the definition of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n-$</tex-math></inline-formula> grams used in the detection process, without requiring the content of the payload or other CAN message fields. The DAGA framework allows the creation of detection models characterized by different memory footprints, allowing their deployment on microcontrollers with different hardware constraints. Experimental results based on three prototype implementations of DAGA showcase the trade off between hardware requirements and detection performance. DAGA outperforms the state-of-the-art detectors on the most performing microcontrollers, and can execute with lower performance on simple microcontrollers that cannot support the vast majority of IDS approaches proposed in literature. As additional contributions, we publicly release the full dataset and our reference DAGA implementations.

CAN FD Data Link Layer with Message Authentication Using Secure Hash Algorithm (SHA-256)

Abstract: CAN stands for Controller Area Network. CAN is the major protocol used in Automobile industry. Through the CAN protocol, all the critical electronics devices exchange the messages to smoothly perform the actions within the automobiles/vehicles. As the technology is the integral part of our life, the vehicles also connected to the networks through the SW clouds or IoT applications. When any system is exposed to network, there exists the scope for cyber attack.As the vehicles use the classical CAN protocol for all the node to node communication using CAN messages, the CAN message field is the interesting part to study &amp; improvise. If the CAN message field is protected using Cryptography, then any attacks will change the CAN messages. But it is not possible to change the authentication filed as per the changed message since only the authorized nodes will have the proper keys or Hashes (based on the Algorithm used). So, the receiver node will neglect the messages which fails with Authentication. As the receiving node discards the message which fails in the authentication, that node will be protected although any of the messages it received might be compromised.

CAN message unpack and pack based on simulink automatic code generation technology

Aiming at the problem of a large amount of CAN communication message data of automobile controllers and low efficiency of traditional software development, this paper proposes a method of CAN message unpack and pack based on Simulink automatic code generation technology. S-Function is used to design CAN message sending and receiving model that can import DBC files, and use RTW (real-time workshop) to realize Simulink model to automatically generate C code. This method effectively solves the unpack and pack of CAN messages, simplifies the code development process, and improves the efficiency of software development, especially when there are DBC files, which simplifies the work of sending and receiving and testing.

Design and Development of a GPS Working Set based on ISOBUS Protocol for Agricultural Machinery in South Korea

Agriculture has been continuously evolving especially in terms of mechanization and automation. An important aspect of these is the ECU or electronic control unit which is an embedded system that is responsible for controlling a specific function or subsystem in a vehicle, tractor or implement. There are numerous manufacturers of agricultural machinery in South Korea, but only a few develop electronic control units (ECUs) that are compliant with ISO 11783. This research was carried out to address the important need to develop such an ECU for agricultural machinery in Korea. This study presents the design of a compliant Global Positioning System (GPS) ECU working set for the automation of agricultural equipment that is fully plug-and-play and non-GNSS specific. An STM32F107 ARM 32-bit Cortex M3 was designed as an ECU using IAR Embedded Workbench with a uBlox M8 GNSS for GPS data. It was then connected to a Virtual Terminal (VT), and the exchanged CAN messages were tested using a KVASER PCIEcan HS CAN Analyzer. Experimental and analytical data confirm that the embedded GPS working set system uses the ISOBUS communication protocol when transmitting GPS data required by an agricultural machine. The GPS working set’s applicability to agricultural work is demonstrated, along with compliance to protocols for plug-and-play functionality for manufacturers from various countries, which is an additional convenience for users. The methodology presented could be used when designing new ISO 11783-compliant working sets for other sources of sensor or environmental data.

Remote NOx emission prediction model based on LSTM neural network

Test results from many researchers show that NOx emission from many on-broad heavy-duty diesel vehicles is higher than which been registered. Therefore, CN_VI emission regulations clearly proposes that the heavy-duty diesel vehicles should be supervised by a T-BOX which can transmit CAN message from vehicle OBD interface to the remote monitoring platform. Based on the formation mechanism of NOx emission and the variety of OBD data flow, the LSTM (Long Short-Term Memory) neural network model inputs such as engine speed, torque, atmospheric pressure, coolant temperature, fuel consumption rate and intake air mass flow are selected by using partial least square method (PLS). 19877 groups of data from engine test results were used for model training and verification, the root mean square error of training and test are RTR = 29.7 × 10-6 and RTE = 19.9 × 10-6,with a high prediction accuracy which can fully meet the requirements of the SCR system DeNOx performance diagnosis module in the OBD remote monitoring system.

An Adaptive Intrusion Detection Algorithm for In-vehicle CAN Bus Based on Periodicity of Message

Alongside with the convergence of In-vehicle network (IVN) and wireless communication technology, In-vehicle CAN communication with external networks reinforces the connectivity among systems of a vehicle. Vehicle communication is facing severe challenges. Intrusion detection technology is one of the most widely used technologies to ensure the safety of In-vehicle CAN communication, so an adaptive intrusion detection method for In-vehicle CAN bus based on message periodicity is proposed. Communication load of CAN bus in the vehicle, the unique priority mechanism and transmission waiting mechanism of CAN message cause a certain fluctuation in the message cycle. The influence of this fluctuation on the detection accuracy of intrusion detection algorithm is analysed while rules are determined by establishing and optimizing the detection threshold in order to further improve the accuracy. Experimental results show that the adaptive intrusion detection based on message periodicity can effectively detect injection and interruption attacks.

Remote supervision strategy based on in-use vehicle OBD data flow

Test results from many researchers show that NOx emissions from parts of on-road heavy-duty diesels are higher than which been registered. Therefore, CN_VI emission regulations clearly proposes that the heavy-duty diesels should be supervised by a T-BOX which can transmit CAN message from vehicle OBD interface to the remote monitoring platform. Recognition of cheating behaver and calculation method of NOx emission were developed and verified by platform data from on-line vehicle. The modules in the platform can effectively monitor urea consumption rationality, injection system working state, exhaust temperature sensor signal reasonability, NOx sensor signal reasonability and NOx emission level, which can reduce the cost of supervision and governance, improve the effectiveness of regulation and provide basic data for policy making.

Perception Through Image Processing Applications for Situation Awareness

This paper introduces a concept for the perception level of situation awareness through image processing applications. The perception of the information from the environment is lacking to achieve highly autonomous vehicles. This approach mainly focuses on collecting information from the environment using camera sensors. The frames from the camera are processed using multiple image processing algorithms, outputs converted into CAN message, and sent to the expert system. CE-Box custom hardware that consists of a Raspberry Pi 3b model counted with PiCAN 2 used to implement and evaluate this approach. The goal of this paper is to provide a conceptual method to make decisions based on the extracted information from the environment using image processing algorithms.

A Deep-Learning-Based Framework for Supporting Analysis and Detection of Attacks on CAN Buses

Abstract Modern vehicles contain a plethora of electronic units aimed to send and receive data by exploiting the serial communication provided by the CAN bus. CAN packets are broadcasted to all components and it is in charge of the single component to decide if it is the receiver of the packets. Furthermore, this protocol does not provide source identification of authentication: for these reason it clear that the CAN bus can be easily exposed to attacks. In this paper we propose a method to detect CAN bus targeting attacks. We take into account deep learning algorithms and we evaluate the proposed method by exploiting CAN messages obtained from a real vehicle injecting four different attacks (i.e. dos, fuzzy, gear and rpm), with interesting results in CAN bus attacks detection.

SecMonQ: An HSM based security monitoring approach for protecting AUTOSAR safety-critical systems

Many attacks on vehicle systems that result in a safety hazard follow a general pattern in which ECU firmware is modified, or code is injected in order to send spoofed CAN messages to safety-critical components causing an unsafe driving situation. There is a general consensus that protecting vehicles requires a defense in depth approach where protections are added at each layer of the vehicle data architecture. At the vehicle CAN bus layer, two powerful countermeasures exist: message authentication/encryption and network intrusion detection systems. The two approaches assume that an attacker has already managed to reach the CAN bus and therefore attempt to limit his impact. To defend against CAN injection attacks, we propose an alternative approach which aims at stopping a CAN injection attack before it reaches the vehicle bus. The proposed approach, working at the ECU level, leverages the embedded hardware security module (HSM), available in modern automotive ECUs, to implement four security monitors (SecMonQ) that run within the HSM firmware. SecMonQ performs continuous monitoring activities of the ECU firmware integrity, communication peripherals, periodic task timing, and flow sequence of certain critical functions. It is designed to detect an active attack and bring the system back to a safe state within the safety defined fault tolerant time. We implement SecMonQ on an automotive development environment which consists of an Elektrobit AUTOSAR stack and a Renesas RH850 F1KM micro-controller. We evaluate SecMonQ against the CAN masquerading attack to demonstrate efficacy while maintaining compatibility with AUTOSAR.

Design of wind tunnel temperature and humidity data acquisition system based on CAN/SimpliciTI hybrid network

In order to obtain the temperature and humidity information of the Test section in the low-speed flue gas wind tunnel in real time, a low-power wind tunnel temperature and humidity data acquisition system based on CAN/SimpliciTI hybrid network is designed. The CAN network is used as the main network, while the SimpliciTI low-power wireless sensor network as the sub-network. The terminal ED nodes of sub-network acquire the ambient temperature and humidity information of the location and then transmit them to the gateway node by wireless mode. In the gateway node，the SimpliciTI messages are converted into CAN messages, and finally, they are delivered to the main network. The host computer communicates with the gateway node through the CANalyst-II protocol analyzer, and finally gets the temperature and humidity information of each ED terminal. Compared with the traditional bus type communication system, the system has the advantages of less wiring, simple network hardware, low power consumption, and convenient maintenance.

Optimal priority assignment for messages on controller area network with maximum system robustness

SummaryController Area Network (CAN) is broadly used for in‐vehicle networking. Each message on the CAN bus is given a unique identifier as its priority. When more than one node starts to transmit at the same time, the message with the highest priority will be sent. The schedulability of the message set is significantly affected by the priority assignment algorithms employed. To ensure that the CAN system tolerate additional interference, we propose system robustness, a concept to represent the total robustness of the message set. Maximum system robustness of message set problem is NP‐hard. We find that assigning a higher priority to the message with small transmission time or low utilization may lead to more system robustness. Based on this, this paper presents four optimal priority assignment approximation algorithms using the Audsley's priority assignment method: TMPA, UMPA, GTMPA, and GUMPA. TMPA and UMPA are heuristic algorithms. GTMPA and GUMPA are greedy algorithms. Experimental results show that, compared with the existing optimal algorithms, TMPA, GTMPA, and GUMPA can improve the system robustness of CAN message sets effectively.

Automotive Security on Abnormal Vehicle Behavior Using Only Fabricated Informative CAN Messages

Automotive Security on Abnormal Vehicle Behavior Using Only Fabricated Informative CAN Messages

MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction With Automation

For the foreseeble future, human beings will likely remain an integral part of the driving task, monitoring the AI system as it performs anywhere from just over 0% to just under 100% of the driving. The governing objectives of the MIT Autonomous Vehicle Technology (MIT-AVT) study are to (1) undertake large-scale real-world driving data collection that includes high-definition video to fuel the development of deep learning based internal and external perception systems, (2) gain a holistic understanding of how human beings interact with vehicle automation technology by integrating video data with vehicle state data, driver characteristics, mental models, and self-reported experiences with technology, and (3) identify how technology and other factors related to automation adoption and use can be improved in ways that save lives. In pursuing these objectives, we have instrumented 23 Tesla Model S and Model X vehicles, 2 Volvo S90 vehicles, 2 Range Rover Evoque, and 2 Cadillac CT6 vehicles for both long-term (over a year per driver) and medium term (one month per driver) naturalistic driving data collection. Furthermore, we are continually developing new methods for analysis of the massive-scale dataset collected from the instrumented vehicle fleet. The recorded data streams include IMU, GPS, CAN messages, and high-definition video streams of the driver face, the driver cabin, the forward roadway, and the instrument cluster (on select vehicles). The study is on-going and growing. To date, we have 122 participants, 15,610 days of participation, 511,638 miles, and 7.1 billion video frames. This paper presents the design of the study, the data collection hardware, the processing of the data, and the computer vision algorithms currently being used to extract actionable knowledge from the data.