Number Of Electronic Control Units Research Articles

A hybrid framework for latency compensation in remote testing of automotive electronic control units

As the number of electronic control units (ECUs) in vehicles continues to grow, exchanging physical components between original equipment manufacturers (OEMs) and ECU suppliers presents logistical challenges, impeding the pace of development and leading to accumulation of costs. In this work, we introduce a conceptual framework that enables remote testing of geographically dispersed ECUs over the Internet. Pertinent to the Internet of Things (IoT) sphere, where interconnection of distributed components is hampered by the inherent challenges of latency, we propose a hybrid synchronous methodology that combines asynchronous test management with time synchronization mechanisms to mitigate the delay impact within the distributed environment. Additionally, we discuss challenges and prospects associated with this approach.

ANALYZING THE CAN PROTOCOL: VULNERABILITIES, PROTECTIVE MEASURES AND IMPROVEMENTS

The Controller Area Network (CAN) bus, originally designed for internal communication among a limited number of Electronic Control Units (ECUs) within vehicles, faces significant security challenges due to the rapid expansion of ECUs in modern automobiles. This expansion necessitates accessibility for diagnostic purposes, yet the CAN protocol lacks fundamental security features such as encryption, authentication, and integrity checks, rendering it vulnerable to various attacks including message injection, Denial of Service (DoS), and masquerading ECUs. This paper surveys existing literature and investigates potential intrusions on the CAN bus, highlighting attacks ranging from GPS spoofing to remote sensor tampering. Various solutions are discussed, including network subdivision, encryption, authentication techniques, and intrusion detection systems (IDS). Recent research proposes IDS solutions utilizing machine learning algorithms such as Random Forest, Support Vector Machine, and Deep Neural Networks (DNN), demonstrating effectiveness in detecting known attacks. However, challenges persist in identifying unknown attacks and enhancing overall system performance. Innovative approaches, such as event-triggered detection and bloom filtering techniques, show promise in mitigating specific attack vectors but may introduce overhead or limitations in real-time response. Deep learning-based IDS systems exhibit high performance but struggle with the detection of novel attacks. Furthermore, while some solutions address specific vulnerabilities, others propose more comprehensive security measures, such as preventing remote manipulation of critical vehicle functions like steering and braking. Overall, the research emphasizes the critical need for robust security measures in automotive systems to safeguard against evolving threats to vehicle safety and integrity.

Beyond vanilla: Improved autoencoder-based ensemble in-vehicle intrusion detection system

Modern automobiles are equipped with a large number of electronic control units (ECUs) to provide safe, driver assistance and comfortable services. The controller area network (CAN) provides near real-time data transmission between ECUs with adequate reliability for in-vehicle communication. However, the lack of security measures such as authentication and encryption makes the CAN bus vulnerable to cyberattacks, which affect the safety of passengers and the surrounding environment. Detecting attacks on the CAN bus, particularly masquerade attacks, presents significant challenges. It necessitates an intrusion detection system (IDS) that effectively utilizes both CAN ID and payload data to ensure thorough detection and protection against a wide range of attacks, all while operating within the constraints of limited computing resources. This paper introduces an ensemble IDS that combines a gated recurrent unit (GRU) network and a novel autoencoder (AE) model to identify cyberattacks on the CAN bus. AEs are expected to produce higher reconstruction errors for anomalous inputs, making them suitable for anomaly detection. However, vanilla AE models often suffer from overgeneralization, reconstructing anomalies without significant errors, resulting in many false negatives. To address this issue, this paper proposes a novel AE called Latent AE, which incorporates a shallow AE into the latent space. The Latent AE model utilizes Cramér’s statistic-based feature selection technique and a transformed CAN payload data structure to enhance its efficiency. The proposed ensemble IDS enhances attack detection capabilities by leveraging the best capabilities of independent GRU and Latent AE models, while mitigating the weaknesses associated with each individual model. The evaluation of the IDS on two public datasets, encompassing 13 different attacks, including sophisticated masquerade attacks, demonstrates its superiority over baseline models with near real-time detection latency of 25ms.

Performance Enhancement of CAN/Ethernet Automotive Gateway with a CAN Data Reduction Algorithm

Data reduction (DR) techniques for the controller area network (CAN) are being developed to reduce the increased bus load caused by the growing number of electronic control units (ECUs) and automotive software complexity in modern automobiles. DR techniques enable the transmission of the same information with less bandwidth, effectively reducing the busload in CAN-based networks. Modern vehicles are composed of various in-vehicle network (IVN) protocols, such as CAN, local interconnect network (LIN), and Ethernet. However, existing DR techniques only consider the communication between CAN nodes. The application of DR techniques to a CAN bus may lead to compatibility issues when communicating with heterogeneous IVN protocols. This paper proposed a CAN/Ethernet gateway system for seamless communication with CAN DR. The proposed gateway system was implemented on a TC275-based embedded system, and its performance was evaluated and analyzed. The experimental results revealed that CAN DR compression effectively improves both the CAN bus load and end-to-end processing time of the gateway system. The CAN bus load was reduced by up to 33.68%, and the average end-to-end processing time was reduced to 336 μs.

Flooding attack mitigator for in-vehicle CAN using fault confinement in CAN protocol

For driver convenience and safety, a number of electronic control units (ECUs) have been installed on modern vehicles. To support communications among ECUs, the controller area network (CAN) is commonly used as in-vehicle network for several decades. However, the CAN protocol lacks security mechanisms, which means that it can be damaged by a number of cyber attacks. In particular, message flooding is one type of DoS attack known to be the easiest to perform because it continuously broadcasts a large number of CAN messages to the in-vehicle CAN without any CAN traffic analysis. To handle message flooding on the in-vehicle CAN, several countermeasures including intrusion detection and prevention have been studied, but unfortunately, these solutions could produce false positive detection rates or cause the communication failures of benign ECUs. In this paper, we introduce a message flooding attack mitigation method for the first time that does not accidentally cause the communication failures of benign ECUs. The proposed method can mitigate flooding attack attempts by using the fault confinement rule that is defined in the CAN protocol. Since the proposed method does not violate the rules of the CAN standard during mitigating, no system modifications are required. Experimental results show that the proposed mitigator guarantees a transmission rate up to 79.22% of normal messages that were not sent due to a flooding attack.

Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey

Recently, the advanced driver assistance system (ADAS) of autonomous vehicles (AVs) has offered substantial benefits to drivers. Improvement of passenger safety is one of the key factors for evolving AVs. An automated system provided by the ADAS in autonomous vehicles is a salient feature for passenger safety in modern vehicles. With an increasing number of electronic control units and a combination of multiple sensors, there are now sufficient computing aptitudes in the car to support ADAS deployment. An ADAS is composed of various sensors: radio detection and ranging (RADAR), cameras, ultrasonic sensors, and LiDAR. However, continual use of multiple sensors and actuators of the ADAS can lead to failure of AV sensors. Thus, prognostic health management (PHM) of ADAS is important for smooth and continuous operation of AVs. The PHM of AVs has recently been introduced and is still progressing. There is a lack of surveys available related to sensor-based PHM of AVs in the literature. Therefore, the objective of the current study was to identify sensor-based PHM, emphasizing different fault identification and isolation (FDI) techniques with challenges and gaps existing in this field.

AI-based classification of CAN measurements for network and ECU identification

Due to the constantly increasing number of functions offered by a modern vehicle, the complexity of vehicle development is also increasing as a result. A first indication of this connection is provided by the number of ECUs (electronic control units) used in current development vehicles. Furthermore, each ECU also performs more functions and is not only electrically networked with the other ECUs, but also logically and functionally. On this basis, new cooperative functions are being developed, which are used for example for autonomous driving. In vehicle development, more and more test sequences (diagnostic scripts) are established for function testing of individual components, systems and cross-functional methods. Due to decentralization and the modular approach, modern development vehicles consist of different numbers of ECUs. The high number of ECUs in purpose and number poses a challenge for test creation and updating. The ECU software is also developed in cycles within the vehicle cycle. This results in a very high software variance. This variance leads to the fact that in the vehicle development with global test conditions works. Global test conditions at this point mean that more ECUs are included in the measurement procedure than are installed in the vehicle. The vehicle structure (control unit and its software version) is not known to the person performing the measurement. He relies on the fact that his ECUs are inside in the global measurement task. This means that the vehicle network architecture is uncertain, which can lead to errors during test execution. Since the ECUs that are actually installed in the vehicle are first determined during test execution, this results in a longer script runtime than would be necessary. To support the development engineer and prevent avoidable errors, the diagnostic system should configure itself as far as possible. This means that individually customized measurements for each vehicle should be calculated in the cloud and not the global measurement tasks. For a diagnostic system to be able to configure itself independently, the vehicle network structure must be determined in a first step. This can be done by a simple CAN measurement (measurementXY.asc). An AI is able to analyze this measurement and classify the occurring ECUs as well as CAN networks. For larger measuring devices with more than one CAN interface, the user who analyzes the measurement is interested in which CAN was connected. Here, the AI is suitable to determine the name of the network and the communicating ECUs based on the communication that runs over the bus. For this purpose, the AI classifies the number of communicating ECUs based on the time intervals at which messages are sent. In addition, the AI can be supported by a special diagnostic script (global.pattern) to determine the vehicle structure at the OBD (on-board diagnostics) interface with maximum accuracy. Three AI approaches are presented, all connected in series and passing results to each other (pipeline mode). First comes the AI that separates vehicle communication from diagnostic communication. Based on the vehicle communication, the network name can be determined. Based on the diagnostic messages, the ECUs can be determined.

A Generalistic Approach to Machine-Learning-Supported Task Migration on Real-Time Systems

Consolidating tasks to a smaller number of electronic control units (ECUs) is an important strategy for optimizing costs and resources in the automotive industry. In our research, we aim to enable ECU consolidation by migrating tasks at runtime between different ECUs, which adds redundancy and fail-safety capabilities to the system. In this paper, we present a setup with a generalistic and modular architecture that allows for integrating and testing different ECU architectures and machine learning (ML) models. As part of a holistic testbed, we introduce a collection of reproducible tasks, as well as a toolchain that controls the dynamic migration of tasks depending on ECU status and load. The migration is aided by the machine learning predictions on the schedulability analysis of possible future task distributions. To demonstrate the capabilities of the setup, we show its integration with FreeRTOS-based ECUs and two ML models—a long short-term memory (LSTM) network and a spiking neural network—along with a collection of tasks to distribute among the ECUs. Our approach shows a promising potential for machine-learning-based schedulability analysis and enables a comparison between different ML models.

Efficient ECU Analysis Technology Through Structure-Aware CAN Fuzzing

Modern vehicles are equipped with a number of electronic control units (ECUs), which control vehicles efficiently by communicating with each other through the controller area network (CAN). However, the CAN is known to be vulnerable to cyber attacks because it does not have any security mechanisms. To find vulnerable CAN messages that can control safety-critical functions in ECUs, researchers have studied CAN fuzzing methods. In existing CAN fuzzing methods, fuzzing input values are generally generated at random without considering the structure of CAN messages, resulting in non-negligible CAN fuzzing time. In addition, existing fuzzing solutions have limited monitoring capabilities of the fuzzing results. In this paper, we propose a Structure-aware CAN Fuzzing protocol, in which the structure of CAN messages is considered and fuzzing input values are systematically generated to locate vulnerable functions in ECUs. Our proposed Structure-aware CAN Fuzzing system takes less time to run than existing solutions, meaning that problematic CAN messages that may have originated from SW implementation errors or CAN DBC (database CAN) design errors can be found quickly and, subsequently, appropriate action can be taken. Finally, we evaluated the performance of our Structure-aware CAN Fuzzing system on two real vehicles. We proved that our proposed method can find CAN messages that control safety-critical functions in ECUs faster than existing fuzzing solutions.

Performance optimization of task intensive real‐time applications on multicore ECUs—A hybrid scheduler

In the current approach of automotive electronic system design, the multicore processors have prevailed to achieve high computing performance at low thermal dissipation. Multicore processors offer functional parallelism that helps in meeting the safety critical requirements of vehicles. The number of Electronic Control Units (ECUs) in high-end cars could be reduced by conglomerating more functions into a multicore ECU. AUTOSAR stack has been designed to support the applications developed for multicore ECUs. The real challenges lie in adapting new design methods while developing sophisticated applications with multicore constraints. It is imperative to utilize the most of multicore computational capability towards enhancing the overall performance of ECUs. In this context, the scheduling of the real-time multitasking software components by the operating system is one of the challenging issues to be addressed. Here, the state-of-the-art scheduling algorithm is reviewed and its merits and limitations are identified. A hybrid scheduler has been proposed, tested and compared with the state-of-the-art algorithm that offers better performance in terms of CPU utilization, average response time and deadline missing rate both in normal and high load conditions.

Research and Practice of Testing Technology for the Use Characteristics of Electric Bus

Electric buses are widely used at present, and the social demand for vehicle maintenance and inspection technology is increasing. Compared with traditional cars, electric buses have a large number of electronic control units and complex structures. Traditional inspection ideas and methods are difficult to be accurate Reflect the status and failure of electric vehicles. In this paper, through the discussion of the main problems in the detection of electric buses, the high-voltage online detection process, the outage detection process and the research and practice of detection methods of using characteristics of electric bus vehicle are determined. The detection methods of characteristics such as consumption rate and power transmission and insulation between vehicles provide technical references for the correct use, technical support and technical integrity of electric buses.

Detection of Message Injection Attacks Onto the CAN Bus Using Similarities of Successive Messages-Sequence Graphs

The smart features of modern cars are enabled by a number of Electronic Control Units (ECUs) components that communicate through an in-vehicle network, known as Controller Area Network (CAN) bus. The fundamental challenge is the security of the communication link where an attacker can inject messages (e.g., increase the speed) that may impact the safety of the driver. Most of existing practical IDS solutions rely on the knowledge of the identity of the ECUs, which is proprietary information. This paper proposes a message injection attack detection solution that is independent of the IDs of the ECUs. First, we represent the sequencing of the messages in a given time-interval as a direct graph and compute the similarities of the successive graphs using the cosine similarity and Pearson correlation. Then, we apply threshold, change point detection, and Long Short-Term Memory (LSTM)-Recurrent Neural Network (RNN) to detect and predict malicious message injections into the CAN bus. The evaluation of the methods using a dataset collected from a moving vehicle under malicious RPM and speed reading message injections show a detection accuracy of 97.32% and detection speed of 2.5 milliseconds when using a threshold method. The performance metrics makes the IDS suitable for real-time control mechanisms for vehicle resiliency to cyber-attacks.

Decentralized Firmware Attestation for In-Vehicle Networks

Today’s vehicles are examples of Cyber-Physical Systems (CPS) controlled by a large number of electronic control units (ECUs), which manage everything from heating to steering and braking. Due to the increasing complexity and inter-dependency of these units, it has become essential for an ECU to be able to ensure the integrity of the firmware running on other ECU’s to guarantee its own correct operation. Existing solutions for firmware attestation use a centralized approach, which means a single point of failure. In this article, we propose and investigate a decentralized firmware attestation scheme for the automotive domain. The basic idea of this scheme is that each ECU can attest to the state of those ECU’s on which it depends. Two flavors of ECU attestation, i.e., parallel and serial solution, were designed, implemented, and evaluated. The two variants were compared in terms of both detection performance (i.e., the ability to identify unauthorized firmware modifications) and timing performance. Our results show that the proposed scheme is feasible to implement and that the parallel solution showed a significant improvement in timing performance over the serial solution.

Cyber–Physical Security of Powertrain Systems in Modern Electric Vehicles: Vulnerabilities, Challenges, and Future Visions

Power electronics systems have become increasingly vulnerable to cyber-physical threats due to their growing penetration in the Internet-of-Things (IoT)-enabled applications, including connected electric vehicles (EVs). In response to this emerging need, a cyber-physical-security initiative was recently launched by the IEEE Power Electronics Society (PELS). With increasing connectivity due to vehicle-to-everything (V2X) and the number of electronic control units, connected EVs are facing greater cyber-physical security challenges. However, existing research extensively focuses on the network security of internal combustion engine vehicles and fails to address the cyber-physical security of EVs specifically. In this article, the challenges and future visions of cyber-physical security are discussed for connected EVs from the perspective of firmware security, vehicle charging safety, and powertrain control security. The vulnerabilities of EVs are investigated under a variety of cyberattacks, ranging from energy-efficiency-motivated attacks to safety-motivated attacks. Simulation results, including hardware-in-the-loop (HIL) results, are provided to further analyze the cyberattack impacts on both converter (device) and vehicle (system) levels. More importantly, an architecture for the next-generation power electronics systems is proposed to address the cyber-physical security challenges of EVs. Finally, potential research opportunities are discussed in detail, including detection and migration for firmware security, model-based, and data-driven detection and mitigation. To the best of our knowledge, this is the first comprehensive study on cyber-physical security of powertrain systems in modern EVs.

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.

Multi-Level Data Arrangement Algorithm for Can Data Compression

Controller area network (CAN) has become the most widely used protocol in automotive networks due to its efficiency and robustness. The number of electronic control units (ECUs) in modern vehicles continually increases as more control functions are needed. CAN buses with limited bandwidths may be overloaded due to increases in bus load. Therefore, reducing the bus load is important in high-end vehicles. In this paper, we propose a multi-level data arrangement algorithm to effectively reduce the amount of transmitted data. Our experimental results show that the proposed algorithm improves the data compression ratio by approximately 25 % compared to previous algorithms.

SEDAN: Security-Aware Design of Time-Critical Automotive Networks

The increasing number of Electronic Control Units (ECUs) in today's vehicles and their greater connectivity with the outside environment has made vehicles more vulnerable to security attacks. Integrating security mechanisms in ECUs has become essential, but incurs overheads, which can delay safety-critical task execution and message transfers. In this work, we introduce a methodology to derive security requirements for tasks and messages in automotive systems based on the ISO 26262 standard. We then propose a framework ( SEDAN ) to increase the security of the system without violating the real-time constraints and security requirements of messages, or ECU utilization limits.

Actual Issues of Modern Digital Vehicle Forensic

Today, the development of the automotive industry is associated with the phenomena of electromobility, autonomous vehicles, digitisation and telematics. New vehicles contain a large number of electronic control units which process and preserve digital data on the vehicle’s activity and its occupants, including communication with the external environment. This data can also be used for forensic purposes, as described in this paper. Attention is also paid to current trends, problem areas and safety risks. The paper deals with sources, classification, characteristics and uses of digital information in vehicles for forensic purposes from various perspectives, paper describes main problematic issue of Digital Vehicle Forensic. The present-day development, advancement and operation of vehicles is inseparably linked with the introduction of electronic devices and digitisation. As a result of this trend, vehicles process, transmit and save a large quantity of operational data and data connected with the activities of the vehicle’s occupants. This trend has a sharply rising character, especially in connection with the development of electric engines and an effort to create and put into operation entirely autonomous vehicles that do not require the active involvement of a driver in order to reach their established destination.

Hardware Security Primitives for Vehicles

In the last few decades, advances in vehicular technology have necessitated large increases in the number of Electronic Control Units (ECUs) within vehicles. These ECUs are required to communicate with other ECUs in order to achieve full functionality. The communication networks that connect the ECUs have been shown to pose significant security risks, as they can be the target for attacks. Establishing security in vehicles against attacks is a complex topic that has generated much interest as a focus of research. In this article, we will highlight current security approaches in terms of hardware modules and physically unclonable functions (PUFs).

Automotive decentralized diagnosis based on CAN real-time analysis

Abstract Nowadays, modern automotive systems include a great number of Electronic Control Units (ECUs). These ECUs provide many sophisticated systems such as engine control, antilock braking systems, etc. This fact has increased the automotive embedded networks complexity. Another important issue in this field is the necessity to define a suitable diagnosis strategy to prevent faults propagation. The integration of diagnosis functions into the automotive embedded systems contributes to overload the communication protocols. In this context, solutions for checking latencies entailed by extra-data traffic are urgently needed. Throughout this paper, a novel approach for the automotive diagnosis design, which is based on the Controller Area Network (CAN) analysis, is detailed. The principal contribution of this work consists in developing a decentralized fault diagnosis and studying its data traffic effect on messages deadlines. Our novel method, “CAN real-time analysis based on decentralized fault diagnosis”, is a step ahead to a reliable early phase automotive design. As a proof of concept, the proposed approach is applied on a model of an advanced anti-crash system. The proposed design methodology presents several advantages. It optimizes the schedulability of tasks and facilitates the design validation. Moreover, a realistic hardware-in-the-loop simulation is carried out to validate our work.