Automotive Software Research Articles

Agile Methodologies In The Automotive Industry: A Comprehensive Survey

The automotive industry is undergoing a major transformation driven by trends such as autonomous driving, connectivity, electrification, and rapid digitalization. This results in embracing agile software development practices and new digital architectures to accelerate development cycles and manage increasing complexity. This paper discusses the insights on the agile methodologies into automotive software procedures, extending agility beyond software units , integrating agile with product lines, addressing security concerns, and constructing advanced digital platforms for automotive systems also assessment models like ASPLA (Agile Software Product Line Adoption) for agile adoption in software product lines, and future research directions towards realizing the vision of intelligent, self-driving, connected vehicles through continuous evolutions in agile-based development processes, digital architectures complying with quality standards, cross-domain collaborations, and empirical validations

Autism Spectrum Disorder DetectionUsingMachine Learning

Convolutional Neural Networks (CNNs) have made significant strides in various domains such as image classifica- tion, speech recognition, automotive software engineering and neuroscience. In this study we leverage CNNs for the automated detection of Autism Spectrum Disorder using brain imaging data,specificallywe utilize resting-state functional Magnetic Resonance Imaging (f-MRI) data from the Autism Brain Imaging Exchange (ABIDE) dataset. To identify ASD patients, our approach relies on analyzing patterns of functional connectivity to classify ASD and control subjects. Experimental results demonstrate that our proposed model achieves an accuracy of 96.40% on the ABIDEI dataset using the CC400 functional parcellation atlas of thebrain. Notably our CNN model exhibits computational efficiency by employing fewer parameters comparedtostate-of-the-art techniques. Our findings suggest that the developed model shows promise for prescreeningasdpatients and warrants further evaluation with additional datasets.

AUTOSAR-Compatible Level-4 Virtual ECU for the Verification of the Target Binary for Cloud-Native Development

The rapid evolution of automotive software necessitates efficient and accurate development and verification processes. This study proposes a virtual electronic control unit (vECU) that allows for precise software testing without the need for hardware, thereby reducing developmental costs and enabling cloud-native development. The software was configured and built on a Hyundai Autoever AUTomotive Open System Architecture (AUTOSAR) classic platform, Mobilgene, and Renode was used for high-fidelity emulations. Custom peripherals in C# were implemented for the FlexTimer, system clock generator, and analog-to-digital converter to ensure the proper functionality of the vECU. Renode’s GNU debugger server function facilitates detailed software debugging in a cloud environment, further accelerating the developmental cycle. Additionally, automated testing was implemented using a vECU tester to enable the verification of the vECU. Performance evaluations demonstrated that the vECU’s execution order and timing of tasks and runnable entities closely matched those of the actual ECU. The vECU tester also enabled fast and accurate verification. These findings confirm the potential of the AUTOSAR-compatible Level-4 vECU to replace hardware in development processes. Future efforts will focus on extending capabilities to emulate a broader range of hardware components and complex system integration scenarios, supporting more diverse research and development efforts.

Agile in Automotive: A Literature Review

Abstract The automotive industry faces increasing pressure to adapt to rapidly changing market demands while maintaining efficiency and quality standards. Agile methodologies have gained attention to enhance flexibility, responsiveness, and innovation in automotive manufacturing and software development. This paper provides a comprehensive review of recent research trends and gaps in the application of agile methodologies within the automotive sector. We analyzed a dataset comprising articles from Scopus, Google Scholar, and Crossref, identifying key themes, such as hybrid lean-agile manufacturing, supply chain management, and automotive software development. Furthermore, we highlight emerging trends and potential areas for future research, including resilience in the automotive industry, integration of green practices, and cross-cultural perspectives.

Promoting social and human factors through a gamified automotive software development environment

AbstractGamification is an attractive strategy for different contexts, including software process improvement, where it presents positive results associated with increased factors such as motivation and others classified into social and human factors. Such factors are required to improve software processes in the automotive industry due to the influence of changes in the conditions and the behavior of individuals. However, the treatment of gamification strategies requires rigor at a scientific level. Therefore, it is necessary to analyze critical dimensions such as the gamification maturity level, the ability to intervene, and the influence of social and human factors. Such analysis is motivated by the relationship between social and human factors and the success of a process improvement. The above justifies the researchers' interest in this article in analyzing a gamification strategy implemented in the automotive industry from such dimensions. Therefore, this article presents the analysis from the point of view of developing software‐controlled systems in automobiles. Besides, it uses a deductive approach to conduct this analysis to abstract all the design aspects of a strategy created and implemented in a software development automotive environment. One of the most representative findings of this study is the strategy's capacity to promote SHF, which identifies motivation, commitment, team cohesion, emotional intelligence, and autonomy.

A Virtual Testing Framework for Real-Time Validation of Automotive Software Systems Based on Hardware in the Loop and Fault Injection.

To validate safety-related automotive software systems, experimental tests are conducted at different stages of the V-model, which are referred as "X-in-the-loop (XIL) methods". However, these methods have significant drawbacks in terms of cost, time, effort and effectiveness. In this study, based on hardware-in-the-loop (HIL) simulation and real-time fault injection (FI), a novel testing framework has been developed to validate system performance under critical abnormal situations during the development process. The developed framework provides an approach for the real-time analysis of system behavior under single and simultaneous sensor/actuator-related faults during virtual test drives without modeling effort for fault mode simulations. Unlike traditional methods, the faults are injected programmatically and the system architecture is ensured without modification to meet the real-time constraints. Moreover, a virtual environment is modeled with various environmental conditions, such as weather, traffic and roads. The validation results demonstrate the effectiveness of the proposed framework in a variety of driving scenarios. The evaluation results demonstrate that the system behavior via HIL simulation has a high accuracy compared to the non-real-time simulation method with an average relative error of 2.52. The comparative study with the state-of-the-art methods indicates that the proposed approach exhibits superior accuracy and capability. This, in turn, provides a safe, reliable and realistic environment for the real-time validation of complex automotive systems at a low cost, with minimal time and effort.

Automating Fault Test Cases Generation and Execution for Automotive Safety Validation via NLP and HIL Simulation

The complexity and the criticality of automotive electronic implanted systems are steadily advancing and that is especially the case for automotive software development. ISO 26262 describes requirements for the development process to confirm the safety of such complex systems. Among these requirements, fault injection is a reliable technique to assess the effectiveness of safety mechanisms and verify the correct implementation of the safety requirements. However, the method of injecting the fault in the system under test in many cases is still manual and depends on an expert, requiring a high level of knowledge of the system. In complex systems, it consumes time, is difficult to execute, and takes effort, because the testers limit the fault injection experiments and inject the minimum number of possible test cases. Fault injection enables testers to identify and address potential issues with a system under test before they become actual problems. In the automotive industry, failures can have serious hazards. In these systems, it is essential to ensure that the system can operate safely even in the presence of faults. We propose an approach using natural language processing (NLP) technologies to automatically derive the fault test cases from the functional safety requirements (FSRs) and execute them automatically by hardware-in-the-loop (HIL) in real time according to the black-box concept and the ISO 26262 standard. The approach demonstrates effectiveness in automatically identifying fault injection locations and conditions, simplifying the testing process, and providing a scalable solution for various safety-critical systems.

Safety of Perception Systems for Automated Driving: A Case Study on Apollo

The automotive industry is now known for its software-intensive and safety-critical nature. The industry is on a path to the holy grail of completely automating driving, starting from relatively simple operational areas like highways. One of the most challenging, evolving, and essential parts of automated driving is the software that enables understanding of surroundings and the vehicle’s own as well as surrounding objects’ relative position, otherwise known as the perception system. Current generation perception systems are formed by a combination of traditional software and machine learning-related software. With automated driving systems transitioning from research to production, it is imperative to assess their safety. We assess the safety of Apollo, the most popular open-source automotive software, at the design level for its use on a Dutch highway. We identified 58 safety requirements, 38 of which are found to be fulfilled at the design level. We observe that all requirements relating to traditional software are fulfilled, while most requirements specific to machine learning systems are not. This study unveils issues that need immediate attention; and directions for future research to make automated driving safe.

Representative Real-Time Dataset Generation Based on Automated Fault Injection and HIL Simulation for ML-Assisted Validation of Automotive Software Systems

Recently, a data-driven approach has been widely used at various stages of the system development lifecycle thanks to its ability to extract knowledge from historical data. However, despite its superiority over other conventional approaches, e.g., approaches that are model-based and signal-based, the availability of representative datasets poses a major challenge. Therefore, for various engineering applications, new solutions to generate representative faulty data that reflect the real world operating conditions should be explored. In this study, a novel approach based on a hardware-in-the-loop (HIL) simulation and automated real-time fault injection (FI) method is proposed to generate, analyse and collect data samples in the presence of single and concurrent faults. The generated dataset is employed for the development of machine learning (ML)-assisted test strategies during the system verification and validation phases of the V-cycle development model. The developed framework can generate not only time series data but also a textual data including fault logs in an automated manner. As a case study, a high-fidelity simulation model of a gasoline engine system with a dynamic entire vehicle model is utilised to demonstrate the capabilities and benefits of the proposed framework. The results reveal the applicability of the proposed framework in simulating and capturing the system behaviour in the presence of faults occurring within the system’s components. Furthermore, the effectiveness of the proposed framework in analysing system behaviour and acquiring data during the validation phase of real-time systems under realistic operating conditions has been demonstrated.

ProTSA: A Testing Process for Automotive Software Domain

ProTSA: A Testing Process for Automotive Software Domain

Modelling centralised automotive E/E software architectures

The automotive domain is living in an exciting period triggered by challenging business and technology drivers, like electrification, autonomous driving, over-the-air software updates and connected vehicles, just to mention a few. This profoundly impacted the electric and electronic automotive architecture and pushed more and more manufacturers to shift towards more centralised electric and electronic architectures for their future automotive software systems. In fact, future centralised architectures are considered to be pivotal to meeting the above-mentioned market needs, as well as evolving customer requirements. In this work, we first analyse the readiness of four main automotive architectural languages to represent novel vehicle-centralised architectures. Based on the analysis results, we propose an extension to one of these languages to fully support the modelling of technical reference architectures for centralised vehicles. We validate the proposed extension using workshops with experts in the automotive domain and using an automotive use case describing an autonomous quarry.

Verification and validation of embedded software in an automotive context: a systematic literature review

In automotive context, the embedded software Verification and Validation (V&V) is always a critical step for each project that involves testing solutions for new function, system optimization and compliance with legal requirements. However, automotive software V&V is laborious and time-consuming. Activities such as planning workshops at test tracks and public roads and functionality and durability tests require significant effort and robust coordination. The rigorous management and storage of test results are also a challenge. This review consolidates the state of the art on automotive software V&V to realize the most common standards in the industry and understand current testing concepts. The consolidated knowledge will help in the future development of a flexible V&V framework for embedded automotive software. A Systematic Literature Review (SLR) was performed by searching four digital libraries from 2011 to March 2022. Sixty-two papers were selected, which indicated that the automotive software V&V process is usually based on the ISO 26262 standard and that the software development life cycle V-model is the most common test platform in the automotive domain. Automotive software for a specific domain has been developed to cover a wide variety of vehicles. Variables from specific regions or countries can influence the entire V&V process for automotive software, such as differences in homologation requirements, infrastructure, driver behavior, customer desires, and electromagnetic force interference. The SLR identified specific characteristics of automotive software and regional factors that can affect the V&V process, as well as significant considerations to ensure correct decision-making, resource allocation, and support of team members.

GRU-Based Denoising Autoencoder for Detection and Clustering of Unknown Single and Concurrent Faults during System Integration Testing of Automotive Software Systems.

Recently, remarkable successes have been achieved in the quality assurance of automotive software systems (ASSs) through the utilization of real-time hardware-in-the-loop (HIL) simulation. Based on the HIL platform, safe, flexible and reliable realistic simulation during the system development process can be enabled. However, notwithstanding the test automation capability, large amounts of recordings data are generated as a result of HIL test executions. Expert knowledge-based approaches to analyze the generated recordings, with the aim of detecting and identifying the faults, are costly in terms of time, effort and difficulty. Therefore, in this study, a novel deep learning-based methodology is proposed so that the faults of automotive sensor signals can be efficiently and automatically detected and identified without human intervention. Concretely, a hybrid GRU-based denoising autoencoder (GRU-based DAE) model with the k-means algorithm is developed for the fault-detection and clustering problem in sequential data. By doing so, based on the real-time historical data, not only individual faults but also unknown simultaneous faults under noisy conditions can be accurately detected and clustered. The applicability and advantages of the proposed method for the HIL testing process are demonstrated by two automotive case studies. To be specific, a high-fidelity gasoline engine and vehicle dynamic system along with an entire vehicle model are considered to verify the performance of the proposed model. The superiority of the proposed architecture compared to other autoencoder variants is presented in the results in terms of reconstruction error under several noise levels. The validation results indicate that the proposed model can perform high detection and clustering accuracy of unknown faults compared to stand-alone techniques.

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.

A Method for Managing Software Assets in the Automotive Industry (Focusing on the Case of Hyundai Motor Company and Parts Makers)

We propose a method for managing software assets in the automotive industry to enhance software competitiveness and to reduce development costs. The ownership of software assets in the automotive industry is held by automotive parts companies, making it challenging to exchange these technologies. Moreover, the criteria for determining software assets are often unclear, resulting in difficulties in integrating automotive software and implementing over-the-air updates. To address these issues, we suggest breaking down black-boxed software assets into tradable components, valuating them, and introducing the concept of exchanging software technology assets. Additionally, we provide a structured approach for recycling used software assets and establish a software asset management system for registration and tracking. Our proposed approach can help traditional automotive OEMs narrow the technology gap with automakers such as Tesla and improve their software competitiveness in the automotive industry. This paper contributes to the advancement of software asset management practices in the automotive industry, and provides insights into the integration of automotive software and over-the-air updates.

Applying declarative analysis to industrial automotive software product line models

Program analysis of automotive software has several unique challenges, including that the code base is ultra large, comprising over a hundred million lines of code running on a single vehicle; the code is structured as a software product line (SPL) for managing a family of related software products from a common set of artifacts; and the analysis results (despite being numerous and despite being variable) need to be presented to the engineer in a way that is manageable. In previous work, we reported on lifting declarative analyses to apply to a software product line, rather than to an individual product variant. This paper reports on milestone results from applying lifted declarative analyses (behaviour alteration, recursion analysis, simplifiable global variable analysis, and two of their variants) to automotive software product lines from General Motors and assessing the scalability of the analyses and the effectiveness of reporting to engineers conditional analysis results (i.e., results conditioned on SPL program variants). We also reflect on some of the lessons learned throughout this project.

CAD Support for Security and Robustness Analysis of Safety-critical Automotive Software

Modern vehicles contain a multitude of electronic control units that implement software features controlling most of the operational, entertainment, connectivity, and safety aspects of the vehicle. However, with security requirements often being an afterthought in automotive software development, incorporation of such software features with intra- and inter-vehicular connectivity requirements often opens up new attack surfaces. Demonstrations of such security vulnerabilities in past reports and literature bring in the necessity to formally analyze how secure automotive control systems really are against adversarial attacks. Modern vehicles often incorporate onboard monitoring systems that test the sanctity of data samples communicated among controllers and detect possible attack/noise insertion scenarios. The performance of such monitors against security threats also needs to be verified. In this work, we outline a rigorous methodology for estimating the vulnerability of automotive CPSs. We provide a computer-aided design framework that considers the model-based representation of safety-critical automotive controllers and monitoring systems working in a closed loop with vehicle dynamics and verifies their safety and robustness w.r.t. false data injection attacks. Symbolically exploring all possible combinations of attack points of the input automotive CPS, the proposed framework tries to find out which sensor and/or actuation signal is vulnerable by generating stealthy and successful attacks using a formal method-based counter-example guided abstract refinement process. We also validate the efficacy of the proposed framework using a case study performed in an industry-scale simulator.

Empirical Study for Open Source Libraries in Automotive Software Systems

Empirical Study for Open Source Libraries in Automotive Software Systems

Intelligent Identification of Simultaneous Faults of Automotive Software Systems Under Noisy and Imbalanced Data Using Ensemble LSTM and Random Forest

Intelligent Identification of Simultaneous Faults of Automotive Software Systems Under Noisy and Imbalanced Data Using Ensemble LSTM and Random Forest

Seven Lessons Learned From Automotive Software Supplier Collaborations

There are challenges when collaborating with automotive software suppliers. Seven lessons that alleviate these challenges are summed up in this article.