Vehicle Electronic Control Units Research Articles

Gradient correlation based detection of adversarial attacks on vehicular networks

The controller area network (CAN) bus, which controls real-time communication and data transmission among vehicle electronic control units, lacks security mechanisms and is highly vulnerable to attacks. Existing in-vehicle network intrusion detection systems (IDSs) typically rely on deep learning models for detection, which are susceptible to interference from adversarial attacks owing to the vulnerability of the models themselves, thereby compromising the detection performance. In this study, we propose an adversarial attack detection method based on gradient correlation that achieves a high accuracy rate using a linear approach to detect adversarial samples. The experimental results show that the proposed model does not require retraining of the original detection model and demonstrates better detection performance for multiple adversarial attacks.

A Cyber-Physical System Based on On-Board Diagnosis (OBD-II) for Smart City

This paper proposes designing and structuring a Cyber-Physical System (CPS) with a specific focus on vehicles equipped with on-board diagnosis (OBD-II). The purpose of the CPS is to collect and assess data pertaining to the vehicle's Electronic Control Unit (ECU), such as engine RPM, speed, and other relevant parameters. The OBD-II scanner utilizes the obtained data on mass airflow (MAF) and vehicle speed to compute CO2 gas emissions and fuel consumption. The data is wirelessly communicated using a GSM module to a Semantic Web. The CPS also uses GPS tracking to ascertain the vehicle's whereabouts. A Semantic Web is utilized to construct a database management system that stores and manages sent data. A graphical user interface (GUI) is created to facilitate data analysis. It undergoes a sequence of qualification tests to verify the system's functionality. The results demonstrate that the system can accurately read parameters, process data, transfer information, and display readings.

IoVCipher: A low-latency lightweight block cipher for internet of vehicles

The data security of CAN bus system is receiving increasing attention with the rapid development of Internet of Vehicles (IoV). However, traditional ciphers are not the best choice due to the limitations of computation, real-time, and resources of Electronic Control Units in vehicles. Thus, this paper proposes a lightweight block cipher IoVCipher to protect the security of IoV. It is designed focus on the latency and area in round-based architectures (both encryption and decryption) to meet this resource-constrained environments. For this purpose, two S-boxes with low latency and tiny area are constructed in this paper, one involution and one non-involution. Considering the decryption latency, a low latency subkey generation method is designed. In addition, this paper proposes a new extended MISTY structure that makes the encryption and decryption of hardware implementations similar. In comparison to other low-latency lightweight block ciphers such as PRINCE, QARMA, MANTIS and LLLWBC, IoVCipher achieves an effective balance between latency and area in the round-based architecture, and IoVCipher has low latency, low area, and low energy in the fully unrolled architecture. Finally, IoVCipher is implemented on a real-time speed acquisition and encryption testbed to simulate encrypted transmission of real-time speed in a CAN bus environment.

Implement Standard UDS Diagnostics over can for Automotive Actuator ECU

Electronic control units (ECUs) are an integral part of modern vehicles, controlling everything from safety procedures and system diagnostics to engine optimization. These ECUs need to work flawlessly to keep the vehicle reliable and efficient. The aim of this project is to use Controller Area Network (CAN) and Unified Diagnostic Services (UDS) communication systems to diagnose ECUs in vehicles (specifically those that control the engine). The main goal of this project is to create a comprehensive diagnostic system specifically designed for vehicle ECUs with the aim of improving engine diagnostics and improving vehicle maintenance procedures. The project combines UDS and CAN, two communication methods widely used in the automotive industry, to achieve these goals and provide effective communication between diagnostic tools and ECUs.

Prevention of Controller Area Network (CAN) Attacks on Electric Autonomous Vehicles

The importance of vehicle security has increased in recent years in the automotive field, drawing the attention of both the industry and academia. This is due to the rise in cybersecurity threats caused by (1) the increase in vehicle connectivity schemes, such as the Internet of Things, vehicle-to-x communication, and over-the-air updates, and (2) the increased impact of such threats because of the added functionalities that are controlled by vehicle software. These causes and threats are further amplified in autonomous vehicles, which are generally equipped with more electronic control units (ECUs) that are connected through controller area networks (CANs). Due to the holistic nature of CANs, attacks on the networks can affect the functionality of all vehicle ECUs and the whole system. This can lead to a breach of privacy, denial of services, alteration of vehicle performance, and exposure to safety threats. Although cryptographic encryption and authentication algorithms and intrusion detection systems (IDS) are currently being used to detect and prevent CAN bus attacks, they have certain limitations. Therefore, this study proposed a mitigation scheme that can detect and prevent such attacks at the ECU level, which could address the limitations of existing algorithms. This study proposed the usage of a secure boot scheme to detect and prevent the execution of malicious codes, as the presence of one or more ECUs with a malicious code is the root cause of most CAN bus attacks. Secure boot schemes apply cryptographic data integrity algorithms to ensure that only authentic and untampered software can run on the vehicle’s ECUs. The selection of an appropriate cryptographic algorithm is important because it affects the secure boot schemes’ security level and performance. Therefore, this study also tested and compared the performance of the proposed secure boot scheme with five different data security algorithms implemented using the hardware security module (HSM) of the TC399 32-bit AURIX™ TriCore™ microcontroller through an electric autonomous vehicle’s control unit. The tests showed that the two most favorable schemes with the selected hardware are the secure boot scheme with the cipher-based message authentication code (CMAC), because it possesses the highest performance with an execution rate of 26.07 (ms/MB), and the secure boot scheme with the elliptic curve digital signature algorithm (ECDSA), because it provides a higher security level with an acceptable compromise in speed. This study also introduced and tested a novel variation of the ECDSA algorithm based on the CMAC algorithm, which was found to have a 19% performance gain over the standard ECDSA-based secure boot scheme.

V-SOC4AS: A Vehicle-SOC for Improving Automotive Security

Integrating embedded systems into next-generation vehicles is proliferating as they increase safety, efficiency, and driving comfort. These functionalities are provided by hundreds of electronic control units (ECUs) that communicate with each other using various protocols that, if not properly designed, may be vulnerable to local or remote attacks. The paper presents a vehicle-security operation center for improving automotive security (V-SOC4AS) to enhance the detection, response, and prevention of cyber-attacks in the automotive context. The goal is to monitor in real-time each subsystem of intra-vehicle communication, that is controller area network (CAN), local interconnect network (LIN), FlexRay, media oriented systems transport (MOST), and Ethernet. Therefore, to achieve this goal, security information and event management (SIEM) was used to monitor and detect malicious attacks in intra-vehicle and inter-vehicle communications: messages transmitted between vehicle ECUs; infotainment and telematics systems, which provide passengers with entertainment capabilities and information about the vehicle system; and vehicular ports, which allow vehicles to connect to diagnostic devices, upload content of various types. As a result, this allows the automation and improvement of threat detection and incident response processes. Furthermore, the V-SOC4AS allows the classification of the received message as malicious and non-malicious and acquisition of additional information about the type of attack. Thus, this reduces the detection time and provides more support for response activities. Experimental evaluation was conducted on two state-of-the-art attacks: denial of service (DoS) and fuzzing. An open-source dataset was used to simulate the vehicles. V-SOC4AS exploits security information and event management to analyze the packets sent by a vehicle using a rule-based mechanism. If the payload contains a CAN frame attack, it is notified to the SOC analysts.

Real-Time Measurement of NOx Emissions from Modern Diesel Vehicles Using On-Board Sensors

In this work, the performance of on-board vehicle exhaust emission sensors is investigated and compared to reference laboratory and on-road instrumentation for two modern diesel light-duty commercial vehicles, type-approved as Euro 6d-TEMP-EVAP-ISC and Euro 6d-ISC-FCM. The first step of the analysis was to perform emissions tests in the laboratory and compare the NOx concentrations registered by the vehicle sensors available at the engine-out and tailpipe positions with those recorded by reference laboratory instrumentation. In a second step, tests were also conducted on road, comparing the performance of on-board sensors with those of Portable Emission Measurement System (PEMS) analysers, which were taken as references. The uncertainty related to exhaust flow measurements was also addressed. In particular, emissions factors calculated using the flow rates measured either in the laboratory or on-road were compared to those obtained by computing exhaust flows with on-board recorded data available from the vehicle electronic control unit. Results showed maximum deviations on the order of 34% in laboratory tests and of 21% during on-road measurements. Finally, measurements were also carried out during a diesel particulate filter regeneration event, showing the good performance of the on-board sensors even when high NOx concentrations were present. These conditions can be similar to those experienced in the case of an after-treatment system malfunction or of a high-emitting event, and can thus be of interest for real-time malfunction identification and monitoring.

Domain Adversarial Neural Network-Based Intrusion Detection System for In-Vehicle Network Variant Attacks

A controller area network (CAN) bus that controls real-time communication and data transmission of electronic control units in vehicles lacks security mechanisms and is highly vulnerable to attacks. The detection effectiveness of existing In-Vehicle network intrusion detection systems (IDSs) is usually reliant on training samples of known attacks. Owing to the simple structure of the CAN bus protocol, attackers can easily create variants based on known attacks. In this study, we propose a CAN bus IDS based on a domain adversarial neural network, which can achieve high detection performance on unlearned variant attack data. In addition, we used feature fusion techniques to synthetically extract the features of the attack data to improve detection capability. The experimental results demonstrate that our proposed model has high performance and generalization ability in attack detection.

Intrusion Detection Method for In-Vehicle CAN Bus Based on Message and Time Transfer Matrix

As the number and computational power of electronic computing units installed in standard automobiles continue to increase, contemporary motor vehicles face more cybersecurity threats than previous designs, while providing greater convenience and various useful features. Although vehicles are attacked at various entry points, eventually, attacks are injected into the in-vehicle controller area network (CAN) to cause vehicle anomalies. Currently, OEMs and research fields have implemented protection for the CAN bus in terms of external interfaces, internal protocols, and intrusion detection. Although the deployment of intrusion detection solutions is the most effective approach, the main challenges currently faced by automobile intrusion detection algorithms in practice involve limited computing resources, insufficient real-time responsiveness, and low recognition accuracy. In this study, we propose a novel intrusion detection method based on the message and time transfer matrix to address these difficulties, which can be applied to the vehicle Electronic Control Unit (ECU) to achieve real-time attack signal identification with high accuracy. Experiments on actual vehicles show that the proposed algorithm identified various attacks with high accuracy while consuming less computational and storage resources than previous methods. Moreover, the efficiency of the proposed algorithm is not affected by the attack injection frequency. Compared with other methods, the proposed method achieved better attack identification performance. Additionally, the message and time transfer matrix used by the algorithm can be used as a message transfer fingerprint of the CAN bus to discover anomalies.

Real-Time Adaptive Velocity Optimization for Autonomous Electric Cars at the Limits of Handling

With the evolution of self-driving cars, autonomous racing series like Roborace and the Indy Autonomous Challenge are rapidly attracting growing attention. Researchers participating in these competitions hope to subsequently transfer their developed functionality to passenger vehicles, in order to improve self-driving technology for reasons of safety, and due to environmental and social benefits. The race track has the advantage of being a safe environment where challenging situations for the algorithms are permanently created. To achieve minimum lap times on the race track, it is important to gather and process information about external influences including, e.g., the position of other cars and the friction potential between the road and the tires. Furthermore, the predicted behavior of the ego-car's propulsion system is crucial for leveraging the available energy as efficiently as possible. In this paper, we therefore present an optimization-based velocity planner, mathematically formulated as a multi-parametric Sequential Quadratic Problem (mpSQP). This planner can handle a spatially and temporally varying friction coefficient, and transfer a race Energy Strategy (ES) to the road. It further handles the velocity-profile-generation task for performance and emergency trajectories in real time on the vehicle's Electronic Control Unit (ECU).

A Lightweight Authentication and Attestation Scheme for In-Transit Vehicles in IoV Scenario

With the rise of new technological paradigms such as the Internet of Things (IoT) and the Internet of Vehicles (IoV), we are going to see an unprecedented growth of connected vehicles on the roads. Also, with the ever-increasing complexity of vehicular electronics and with the increasing number of Electronic Control Units (ECUs) inside these next-generation vehicles, the need for verification of the firmware and software running on these ECUs using attestation techniques is heightened all the more. In this paper, we propose a lightweight and secure authentication and attestation scheme for attesting vehicles while they are on the roads. Since this attestation is proposed to be carried out on moving vehicles, there is also a need for authenticating the vehicles with the Road Side Units (RSUs) first before carrying out attestation. Therefore, a combined attestation and authentication scheme for verification of the vehicle ECU firmware is presented here. The ECU firmware running on the vehicles can be attested from the edge servers connected to the RSUs while the vehicles are in-transit and passing through these RSUs. We perform a security analysis of the proposed attestation and authentication protocol and compare it with other similar existing protocols. We also do a performance analysis of the proposed protocol and show the feasibility of its deployment.

Evaluating system architectures for driving range estimation and charge planning for electric vehicles

AbstractDue to sparse charging infrastructure and short driving ranges, drivers of battery electric vehicles (BEVs) can experience range anxiety, which is the fear of stranding with an empty battery. To help eliminate range anxiety and make BEVs more attractive for customers, accurate range estimation methods need to be developed. In recent years, many publications have suggested machine learning algorithms as a fitting method to achieve accurate range estimations. However, these algorithms use a large amount of data and have high computational requirements. A traditional placement of the software within a vehicle's electronic control unit could lead to high latencies and thus detrimental to user experience. But since modern vehicles are connected to a backend, where software modules can be implemented, high latencies can be prevented with intelligent distribution of the algorithm parts. On the other hand, communication between vehicle and backend can be slow or expensive. In this article, an intelligent deployment of a range estimation software based on ML is analyzed. We model hardware and software to enable performance evaluation in early stages of the development process. Based on simulations, different system architectures and module placements are then analyzed in terms of latency, network usage, energy usage, and cost. We show that a distributed system with cloud‐based module placement reduces the end‐to‐end latency significantly, when compared with a traditional vehicle‐based placement. Furthermore, we show that network usage is significantly reduced. This intelligent system enables the application of complex, but accurate range estimation with low latencies, resulting in an improved user experience, which enhances the practicality and acceptance of BEVs.

ASFIT: AUTOSAR-Based Software Fault Injection Test for Vehicles

With recent increases in the amount of software installed in vehicles, the probability of automotive software faults that lead to accidents has also increased. Because automotive software faults can lead to serious accidents or even mortalities, vehicle software design and testing must consider safety a top priority. ISO 26262 recommends fault injection testing as a measure to verify the functional safety of vehicles. However, the standard does not clearly specify when and where faults should be injected, and the tools to support fault injection testing for automotive software are also insufficient. In the present study, we define faults that may occur in Automotive Open System Architecture (AUTOSAR)-based automotive software and propose a fault injection method to be applied during the software development process. The proposed method can inject different types of faults that may occur in AUTOSAR-based automotive software, such as access, asymmetric, and timing errors, while minimizing performance degradation due to fault injection, and without using any separate hardware devices. The superior performance of the proposed method is demonstrated through empirical studies applied to fault injection testing of a range of vehicle electronic control unit software.

Vehicle data management with specific wear-levelling and fault tolerance for hybrid DRAM-NVM memory

Abstract The emerging Non-Volatile Memory (NVM) attracts attentions in recent years because of its fast reading/writing speed, high density, low idle power and persistence. With such characteristics, NVM can be applied to application-specific embedded systems to improve performance such as automotive systems. However, NVM has limitation on writing operations and is consequently not suitable to be applied in writing sensitive systems. In this paper, we propose a Vehicle Data Management (VDM) approach based on a hybrid DRAM and NVM architecture for automotive systems. VDM approach includes automotive dynamic data management, vehicle-specific wear-levelling and vehicle fault tolerance. Automotive dynamic data management can distinguish the hot or cold data from vehicle Electronic Control Units (ECUs) and sensors, and then manage the data to reduce the writing operations on NVM for the hybrid main memory. Vehicle-specific wear-levelling can balance the wear to prolong the lifetime of NVM. A fault tolerance mechanism is also provided for NVM to recover the fault blocks. The experimental results show that both automotive dynamic data management and vehicle-specific wear-levelling can effectively reduce the writing operations compared with other approaches. Vehicle fault tolerance can achieve a high recovery rate and a low time overhead when error rate is not very high.

Two Dimensional Electronic Nose for Vehicular Central Locking System (E-Nose-V)

A new approach to vehicle security is proposed, tried, tested. The designed and tested system comprises an odor detection system (E-Nose) that sends signals corresponding to selected odors to the smart vehicle Electronic Control Unit (ECU), which is interfaced to a smart system with neural networks. The signal is interpreted in time and space, whereby a certain ordered number of samples should be obtained before the vehicle functions are unlocked. Correlation of rise and decay times and amplitudes of the signal is carried out to ensure security. The proposed system is highly secured and could be further developed to become a vital and integrated part of Intelligent Transportation Systems (ITS) through the addition of driver’s body odor smell as an extra measure of security and in cases of accidents to auto call emergency services with driver identification and some diagnostics. Such system can be utilized in smart cities.

Flame ionization testing in an internal combustion engine to measure the speed of the flame for gaseous fuels

The study was carried for combustion ionization flame testing on a K3-VE engine cylinder head. The main purpose of this experiment is to measure the speed of flame by using multiple ionization (ION) probe systems. The aim is to determine the feedback from the probe for engine control systems. The fuel that is use in this experiment is Liquified Petroleum Gas (LPG), because LPG gas causes a lot misfire during combustion due to its gaseous state and mixing. The testing probe was installed inside the engine combustion chamber with specific distance for the ION Probe. When the fuel is supplied in to the engine and the spark plug lights up the fuel, it produces a flame which is ionised, at the same time, when this flame touches the probe, it will generate a signal before quenching. The probe is connected to a circuit, when the signal is generated by the flame, the circuit will process the signal. A user interface was created to display the result of the experiment. Also, a camera was used to catch the flame to validate with the result. By using calculation, the speed of the flame can be determined to 20.60 m/s. The signal of the flame can be further used to improve the efficiency of the engine by adding a chip to the circuit to convert the signal and send to the vehicle Electronic Control Unit (ECU) to control the ignition time for better engine performance.

Software Platform for Heterogeneous In-Vehicle Environments

Modern technologies lead to more sophisticated hardware, while software is becoming more complex. These trends are widely present in consumer electronics and do not bypass automotive electronics either. There is an evident recent growth in in-vehicle infotainment, telematics, advanced driver assistance systems (ADASs) and cluster development. The number of electronic control units (ECUs) in vehicle constantly grows. Since typical vehicle ECU is providing one function per vehicle, it becomes harder for manufacturers to manage these ECUs due to diverse nature of the system, hence a rising demand for ECU consolidation exists. With the availability of sophisticated hardware, powerful system-on-chips (SoCs) can be used for multiple functions inside a vehicle. The transition toward less ECUs is an ongoing process, in which software needs to be aligned first and then transferred to the same SoC. This paper presents the software platform for heterogeneous immersive in-vehicle environments, providing a step in software consolidation, by allowing same abstractions for diverse applications executing on various hardware platforms. It proposes a framework for the scalable development of ADAS from consumer level to different automotive safety levels, provides unified access toward algorithm building blocks, multi-sensor real-time environment and easy integration of algorithms, thus enabling shorter development time.

Development of a hardware-in-the-loop simulation system for power seat and power trunk electronic control unit validation

This paper describes a hardware-in-the-loop simulation system for the validation of a vehicle body electronic control unit. The hardware-in-the-loop simulation system consists of three parts: a real-time target machine, an electronic control unit, and a signal conditioning unit, which regulates the voltage levels between the real-time target and the electronic control unit. The real-time target machine generates switch and feedback signals to the electronic control unit. The software model, representing body electronics hardware, such as a power seat and power trunk, runs inside a real-time target machine. The software model is composed of a mechanical part that represents the dynamic behaviors and an electronic part to calculate the motor speeds, current, and electronic loads under various conditions. The hardware-in-the-loop test was carried out for two different large passenger vehicle electronic control units, since the purpose of this research is to validate the various electronic control units by just simply modifying the corresponding vehicle model, the power seat, and the power trunk. Test results indicate that the developed software model can effectively replace the real hardware, and that this virtual model can be used to validate the signal logic between the electronic control unit and the model. In addition, the electrical robustness of the electronic control unit was validated by applying surge currents to the electronic control unit.

Tuning of Look-ahead Cruise Control in HIL Vehicle Simulator

The paper introduces a hardware-in-the-loop (HIL) driving simulator with the implementation of a look-ahead cruise control considering forward road information. The vehicle dynamics are simulated real-time in the high fidelity heavy duty vehicle simulation environment TruckSim, while the proposed look-ahead control algorithm also runs real-time on dSPACE MicroAutoBox II. The latter functions as a vehicle electronic control unit (ECU) and is used for rapid control prototyping (RCP), hence the proposed look-ahead driver assistance system can be tested and tuned in a real-time HIL vehicle simulator before installing dSPACE MicroAutoBox II in a real vehicle.

Methods of Examining Vehicle Electronic Systems in the Course of Automotive Forensic Expert Examinations

The objects of the article: to publish the results of the analysis of methodical ware, tools and software used by automotive expert witnesses when examining vehicle electronic systems in the course of forensic expert examination; to substantiate the required and sufficient set of diagnostic devices used in automotive forensic expert examinations of vehicles equipped with electronic control systems; to specify diagnostic techniques used while examining vehicles with electronic control systems to solve the questions set for automotive expert witnesses by judicial authorities. The urgency of this publication consists in the necessity of developing an algorithm of actions to be used by forensic expert witnesses while examining parameters of vehicle technical state, parameters of operational processes of assemblies and systems as well as diagnostic codes stored in the vehicle electronic control unit (ECU) memory. In this publication, we draw attention to the fact that information, which has been set forth in technical literature, study materials, and publications over recent years, does not contain specific guidelines on how to organize the work of automotive forensic expert witnesses. The scientific novelty of this publication consists in that it suggests general techniques for an expert's operation with diagnosis reports made as a result of using electronic diagnostic systems, inspection lines and specialized test benches to solve specific tasks, such as determining hidden causes of failures of separate vehicle units based on the data on malfunctions of other mechanisms and systems recorded by electronic units; determining defect (failure) location in vehicle electronic control systems in cases when there is no diagnostic code or the diagnostic code does not indicate a specific type of failure.