Browse Topic: Connectivity
This study focuses on the dynamic behavior and ride quality of three different modes of oil-gas interconnected suspension systems: fully interconnected mode, left-right interconnected mode, and independent mode. A multi-body dynamics model and a hydraulic model of the oil-gas suspension were established to evaluate the system's performance under various operating conditions. The research includes simulations of pitch and roll excitations, as well as ride comfort tests on different road surfaces, such as Class B roads and gravel roads. The analysis compares the effectiveness of the modes in suppressing pitch and roll movements and their impact on overall ride comfort. Results show that the independent mode outperforms the other two in minimizing roll, while the fully interconnected mode offers better pitch control but at the cost of reduced comfort. These findings provide valuable insights for the future design and optimization of oil-gas interconnected suspension systems, especially in
Plug-in Hybrid Electric Vehicles (PHEVs) combine the benefits of electric propulsion and storage with the extended range of conventional internal combustion engines to reduce fuel consumption and greenhouse gas emissions. However, optimizing the efficiency of PHEVs in real-world driving conditions remains a challenge due to the uncertainties of environmental and driving conditions. Connectivity and automation technologies can offer a unique opportunity to enhance the efficiency of PHEVs by enabling real-time interaction with surrounding vehicles and infrastructure. By leveraging these technologies, significant reductions in energy consumption for PHEVs can be achieved. However, most existing works primarily rely on simulation-based analyses to evaluate energy savings offered by connected and automated PHEVs. This study advances the understanding of the energy-saving potential of connected and automated PHEVs by incorporating experimental validation alongside simulation-based analyses
With the rapid development of intelligent connected vehicles, their open and interconnected communication characteristics necessitate the use of in-vehicle Ethernet with high bandwidth, real-time performance, and reliability. DDS is expected to become the middleware of choice for in-vehicle Ethernet communication. The Data Distribution Service (DDS), provided by the Object Management Group (OMG), is an efficient message middleware based on the publish/subscribe model. It offers high real-time performance, flexibility, reliability, and scalability, showing great potential in service-oriented in-vehicle Ethernet communication. The performance of DDS directly impacts the stable operation of vehicle systems, making accurate evaluation of DDS performance in automotive systems crucial for optimizing system design. This paper proposes a latency decomposition method based on DDS middleware, aiming to break down the overall end-to-end latency into specific delays at each processing stage
The rapid development of intelligent and connected vehicles is transforming them into data-rich information carriers, which generate and store vast amounts of sensitive information. However, the frequent sharing of resources within these vehicles poses substantial risks to user privacy and data security. Should sensitive resources be accessed maliciously, the consequences could be severe, leading to significant threats to the safety, property, and reputation of both drivers and passengers. To address these risks, this paper proposes an adaptive risk-based access control with Trusted Execution Environment (TEE) specifically designed for vehicles, aimed at managing and restricting access permissions based on risk assessments. Firstly, this paper designs an adaptive risk model in accordance with ISO/SAE 21434, taking into account factors such as the security levels of subjects and objects, context, and the risk history of subjects to separately quantify threats and impacts. By adjusting
The trends of intelligence and connectivity are continuously driving innovation in automotive technology. With the deployment of more safety-critical applications, the demand for communication reliability in in-vehicle networks (IVNs) has increased significantly. As a result, Time-Sensitive Networking (TSN) standards have been adopted in the automotive domain to ensure highly reliable and real-time data transmission. IEEE 802.1CB is one of the TSN standards that proposes a Frame Replication and Elimination for Reliability (FRER) mechanism. With FRER, streams requiring reliable transmission are duplicated and sent over disjoint paths in the network. FRER enhances reliability without sacrificing real-time data transmission through redundancy in both temporal and spatial dimensions, in contrast to the acknowledgment and retransmission mechanisms used in traditional Ethernet. However, previous studies have demonstrated that, under specific conditions, FRER can lead to traffic bursts and
This paper examines the challenges and mechanisms for ensuring Freedom from Interference in Adaptive AUTOSAR-based platforms, with a focus on managing Memory, Timing, and Execution challenges. It explores the robust safety mechanisms in Classic AUTOSAR that ensure Freedom from Interference and the significant challenges in achieving interference-free operation in Adaptive AUTOSAR environments while adhering to ISO26262 standards. The study emphasizes strategies for managing complexities and outlines the multifaceted landscape of achieving interference-free operation. Additionally, it discusses ASIL-compliant Hypervisor, memory partitioning, and Platform Health Management as mechanisms for ensuring safety execution. The paper also raises open questions regarding real-time problems in live projects that are not solved with existing safety mechanisms. Adaptive AUTOSAR plays a crucial role in the development of autonomous and connected vehicles, where functional safety is of utmost
Abstract Real-world driving data is an invaluable asset for several types of transportation research, including emissions estimation, vehicle control development, and public infrastructure planning. Traditional methods of real-world driving data collection use expensive GPS-based data logging equipment which provide advanced capabilities but may increase complexity, cost, and setup time. This paper focuses on using the Google Maps application available for smartphones due to the potential to scale-up real-world driving data logging. Samples of the potential data processing and information that can be gathered by such a logging methodology is presented. Specifically, two months of Google Maps driving data logged by a rural Michigan resident on their smartphone may provide insights on their driving range, duration, and geographic area of coverage (AOC) to guide them on future vehicle purchase decisions. Aggregating such statistics from crowd-sourcing real-world driving data via Google
To alleviate the problem of reduced traffic efficiency caused by the mixed flow of heterogeneous vehicles, including autonomous and human-driven vehicles, this article proposes a vehicle-to-vehicle collaborative control strategy for a dedicated lane in a connected and automated vehicle system. First, the dedicated lane’s operating efficiency and formation performance are described. Then, the characteristics of connected vehicle formations are determined, and a control strategy for heterogeneous vehicle formations was developed. Subsequently, an interactive strategy was established for queueing under the coordination of connected human-driven and autonomous vehicles, and the queue formation, merging, and splitting processes are divided according to the cooperative interaction strategy. Finally, the proposed lane management and formation strategies are verified using the SUMO+Veins simulation software. The simulation results show that the dedicated lane for connected vehicles can
Time Sensitive Networking (TSN) Ethernet is a real-time networking capability that is being developed by a growing number of embedded computing companies for the earliest stages of adoption by aerospace and defense manufacturers and their suppliers. According to the Institute of Electrical and Electronics Engineers (IEEE) TSN working group, it is a set of standards that provides deterministic connectivity within IEEE 802-aligned networks. Nigel Forrester is the Director of Product Strategy for Concurrent Technologies, a UK-based provider of high performance embedded computing solutions for aerospace, defense and many other industries. Check out our interview with Forrester about the potential impact of TSN Ethernet on new and legacy aerospace and defense applications, and how it is being adopted by manufacturers and system integrators below.
This SAE Technical Information Report identifies use cases for AI technology applications to ground vehicles and transportation infrastructure. Whenever applicable, functional definitions and noted issues and concerns are provided in consistent with the current industry mobility practices and published peer-reviewed literature.
Introducing connectivity and collaboration promises to address some of the safety challenges for automated vehicles (AVs), especially in scenarios where occlusions and rule-violating road users pose safety risks and challenges in reconciling performance and safety. This requires establishing new collaborative systems with connected vehicles, off-board perception systems, and a communication network. However, adding connectivity and information sharing not only requires infrastructure investments but also an improved understanding of the design space, the involved trade-offs and new failure modes. We set out to improve the understanding of the relationships between the constituents of a collaborative system to investigate design parameters influencing safety properties and their performance trade-offs. To this end we propose a methodology comprising models, analysis methods, and a software tool for design space exploration regarding the potential for safety enhancements and requirements
The technology of advanced intelligent driving systems equipped in intelligent and connected vehicles evolves rapidly, while the corresponding testing and evaluation standards lag behind. To propose new testing methods for advanced intelligent driving systems, this study first identifies typical intelligent driving functions through extensive research. Based on the distribution characteristics of natural driving scenarios, a series of test cases for these typical functions are developed. By employing simulation methods and the "logical scenario-critical scenario-experimental scenario" mechanism, the generalized test cases are filtered, enabling the rapid extraction of high-value test cases for real-world field testing and validation. Finally, the typical intelligent driving functions of advanced driving systems are analyzed and evaluated from the perspectives of perception and decision-making, as well as control operations. The experimental results demonstrate that the proposed testing
For the mismatching defects of vertical projection method, this paper proposes an improved map matching algorithm based on road geometric features. For GNSS data, static repeated data is eliminated, dynamic high frequency data is compressed by light bar method. For network map data, extract motorized road segment, break road segment curve at the turning point, and establish network topology relationship. During map matching, determine the candidate road segment through the circular error area, and determine the matching path through the heading angle, connectivity and projection distance, and determine the projection points through the historical trajectory and driving speed. The effectiveness of the proposed algorithm is verified by case study.
This paper presents a novel variable speed limit control strategy based on an Improved METANET model aimed at addressing traffic congestion in the bottleneck areas of expressways while considering the impact of an intelligent connected environment. Traffic flow simulation software was employed to compare the outcomes of the traditional variable speed limit model with those derived from the proposed strategy. The results indicated that under three scenarios—main road, ramp, and lane closure—with a 100% penetration rate of intelligent connected vehicles, the average delay for vehicles utilizing the new model decreased by 9.37%, 11.11%, and 7.22%, respectively. This study offers an innovative approach to highway variable speed limits under an intelligent connected environment.
With the acceleration of urbanization, developing public transportation is an important means to alleviate travel pressure and traffic congestion in cities. Work zones that occupy urban road resources affect normal vehicle operations, leading to reduced vehicle efficiency. Based on this, the paper conducts research on traffic flow modeling and simulation analysis for work zones in a vehicle-road coordination environment. Based on the Gipps model and the SCAT model, optimizations and improvements were made to the following and lane-changing rules for three types of vehicles: human-driven vehicles (HVs), autonomous and connected vehicles (CAVs), and buses. Using cellular automata theory, it constructs a running model suitable for mixed traffic flow vehicles in work zones. MATLAB software is utilized to simulate the operation process of vehicles under work zone scenarios, analyzing changes in traffic flow from two directions: road geometric conditions (speed limits) and traffic flow
The transition from manual to autonomous driving introduces new safety challenges, with road obstacles emerging as a prominent threat to driving safety. However, existing research primarily focuses on vehicle-to-vehicle risk assessment, often overlooking the significant risks posed by static or dynamic road obstacles. In this context, developing a system capable of real-time monitoring of road conditions, accurately identifying obstacle positions and characteristics, and assessing their associated risk levels is crucial. To address these gaps, this study proposes a comprehensive process for rapid obstacle identification and risk quantification, composed of three main components: road obstacle event detection and feature extraction, risk quantification and level assessment, and output of warning information and countermeasures. First, a rapid detection method suited for highway scenarios is proposed based on the YOLOv5 model, enabling fast detection and classification of obstacles in
Connected and autonomous vehicles (CAVs) rely on communication channels to improve safety and efficiency. However, this connectivity leaves them vulnerable to potential cyberattacks, such as false data injection (FDI) attacks. We can mitigate the effect of FDI attacks by designing secure control techniques. However, tuning control parameters is essential for the safety and security of such techniques, and there is no systematic approach to achieving that. In this article, our primary focus is on cooperative adaptive cruise control (CACC), a key component of CAVs. We develop a secure CACC by integrating model-based and learning-based approaches to detect and mitigate FDI attacks in real-time. We analyze the stability of the proposed resilient controller through Lyapunov stability analysis, identifying sufficient conditions for its effectiveness. We use these sufficient conditions and develop a reinforcement learning (RL)-based tuning algorithm to adjust the parameter gains of the
The increased connectivity of vehicles expands the attack surface of in-vehicle networks, enabling attackers to infiltrate through external interfaces and inject malicious traffic. These malicious flows often contain anomalous semantic information, potentially leading to misleading control instructions or erroneous decisions. While most semantic-based anomaly detection methods for in-vehicle networks focus on extracting semantic context, they often overlook interactions and associations between multiple semantics, resulting in a high false positive rate (FPR). To address these challenges, the Adaptive Structure Graph Attention Network Model (AS-GAT) is proposed for in-vehicle network anomaly detection. Our approach combines a semantic extractor with a continuously updated graph structure learning method based on attention weight similarity constraints. The semantic extractor identifies semantic features within messages, while the graph structure learning module adaptively updates the
The term Software-Defined Vehicle (SDV) describes the vision of software-driven automotive development, where new features, such as improved autonomous driving, are added through software updates. Groups like SOAFEE advocate cloud-native approaches – i.e., service-oriented architectures and distributed workloads – in vehicles. However, monitoring and diagnosing such vehicle architectures remain largely unaddressed. ASAM’s SOVD API (ISO 17978) fills this gap by providing a foundation for diagnosing vehicles with service-oriented architectures and connected vehicles based on high-performance computing units (HPCs). For service-oriented architectures, aspects like the execution environment, service orchestration, functionalities, dependencies, and execution times must be diagnosable. Since SDVs depend on cloud services, diagnostic functionality must extend beyond the vehicle to include the cloud for identifying the root cause of a malfunction. Due to SDVs’ dynamic nature, vehicle systems
In an era where automotive technology is rapidly advancing towards autonomy and connectivity, the significance of Ethernet in ensuring automotive cybersecurity cannot be overstated. As vehicles increasingly rely on high-speed communication networks like Ethernet, the seamless exchange of information between various vehicle components becomes paramount. This paper introduces a pioneering approach to fortifying automotive security through the development of an Ethernet-Based Intrusion Detection System (IDS) tailored for zonal architecture. Ethernet serves as the backbone for critical automotive applications such as advanced driver-assistance systems (ADAS), infotainment systems, and vehicle-to-everything (V2X) communication, necessitating high-bandwidth communication channels to support real-time data transmission. Additionally, the transition from traditional domain-based architectures to zonal architectures underscores Ethernet's role in facilitating efficient communication between
Cybersecurity, particularly in the automotive sector, is of paramount importance in today’s digital age. With the advent of connected commercial vehicles, which leverage telematics for efficient fleet management, the landscape of automotive cybersecurity is rapidly evolving. These vehicles, integral to logistics and transportation businesses, are becoming increasingly connected, thereby escalating the risks associated with cybersecurity threats. These commercial vehicles are becoming prime targets for cyber-attacks due to their connectivity and the valuable data they hold. The potential consequences of these cyber-attacks can range from data breaches to disruptions in fleet operations, and even safety risks. This paper analyses the unique challenges faced by the commercial vehicle sector, such as the need for robust telematics systems, secure communication channels, and stringent data protection measures. Case studies of notable cybersecurity incidents involving commercial vehicles are
Virtualization features such as digital twins and virtual patching can accelerate development and make commercial vehicles more agile and secure. There is one sure-fire way to secure commercial vehicles from cyber-attacks. “You just remove the connectivity,” quipped Brandon Barry, CEO of Block Harbor Cybersecurity and the moderator of a panel session on “cybersecurity of virtual machines” at the SAE COMVEC 2024 conference in Schaumburg, Illinois. Obviously, that train has left the station - commercial vehicles of all types, including trains, are only becoming more automated and connected, which increases the risks for cyber-attacks. “We have very connected vehicles, so attacks can be posed not just through powertrain solutions but also through telemetry, infotainment systems connected to different applications and services, and also through cloud platforms,” said Trisha Chatterjee, current product support and data specialist for fuel cell and hydrogen technology at Accelera by Cummins.
Original equipment manufacturers, Tier 1 suppliers, and the rest of the value chain, including the semiconductor industry, are reshaping their product portfolios, development processes, and business models to support this transformation to software-defined vehicles (SDVs). The focus on software is rippling out through the automotive sector, forcing the industry to rethink organization, leadership, processes, and future roadmaps. The Software-defined Vehicle: Its Current Trajectory and Execution Challenges assesses the state of SDVs and explores the potential hurdles to execution and examines the work being done in the industry. The goal is to evaluate whether the implementation of SDVs will encounter the same fate as electrification or autonomous technologies, which after some level of disillusionment, are expected to pick up momentum in a more mature way. Click here to access the full SAE EDGETM Research Report portfolio.
Many organizations have data stored in differing formats and various locations throughout the organization and often outside the organization. It is often difficult to access such data and to determine and access interconnected data and data derivatives. Developed at NASA Ames Research Center is a novel data management platform for managing interconnected data and its derivatives.
Based on advanced Automotive functionality, Vehicle networks has enabled the exchange of data to multiple domains and to meet these demands, more complex software applications, some of which require service-based cloud are developed. Exposure of data creates multiple threats for attacker to tamper security and privacy. Automotive cybersecurity topic has gained momentum based on multiple gaps identified in Automotive In vehicle and around the vehicle networks. In this paper, we provide an extensive overview on V2C (Vehicle to Cloud) and In-vehicle data protection, we also highlight methods to identify threats on any vehicle network connected to V2C and identify methods to verify security functionality using Fuzz or Penetration test protocol, we have identified gaps in existing security solutions and outline possible open issues and probable solution.
In recent times there has been an upward trend in “Connected Vehicles”, which has significantly improved not only the driving experience but also the “ownership of the car”. The use of state-of-the-art wireless technologies, such as vehicle-to-everything (V2X) connectivity, is crucial for its dependability and safety. V2X also effectively extends the information flow between the transportation ecosystem pedestrians, public infrastructure (traffic management system) and parking infrastructure, charging and fuel stations, Etc. V2X has a lot of potential to enhance traffic flow, boost traffic safety, and provide drivers and operators with new services. One of the fundamental issues is maintaining trustworthy and quick communication between cars and infrastructure. While establishing stable connectivity, reducing interference, and controlling the fluctuating quality of wireless transmissions, we have to ensure the Security and Privacy of V2I. Since there are multiple and diverse
Autonomous vehicle technologies have become increasingly popular over the last few years. One of their most important application is autonomous shuttle buses that could radically change public transport systems. In order to enhance the availability of shuttle service, this article outlines a series of interconnected challenges and innovative solutions to optimize the operation of autonomous shuttles based on the experience within the Shuttle Modellregion Oberfranken (SMO) project. The shuttle shall be able to work in every weather condition, including the robustness of the perception algorithm. Besides, the shuttle shall react to environmental changes, interact with other traffic participants, and ensure comfortable travel for passengers and awareness of VRUs. These challenging situations shall be solved alone or with a teleoperator’s help. Our analysis considers the basic sense–plan–act architecture for autonomous driving. Critical components like object detection, pedestrian tracking
The industrial internet of things (IIoT) is the nervous system in manufacturing facilities worldwide, with programmable logic controllers (PLCs) serving as its vital synapses. This digital neural network is transforming isolated machines into interconnected ecosystems of unprecedented intelligence and efficiency. PLCs have evolved from simple control devices into sophisticated nodes in a vast, responsive network.
The deployment of autonomous urban buses brings with it the hope of addressing concerns associated with safety and aging drivers. However, issues related autonomous vehicle (AV) positioning and interactions with road users pose challenges to realizing these benefits. This report covers unsettled issues and potential solutions related to the operation of autonomous urban buses, including the crucial need for all-weather localization capabilities to ensure reliable navigation in diverse environmental conditions. Additionally, minimizing the gap between AVs and platforms during designated parking requires precise localization. Next-gen Urban Buses: Autonomy and Connectivity addresses the challenge of predicting the intentions of pedestrians, vehicles, and obstacles for appropriate responses, the detection of traffic police gestures to ensure compliance with traffic signals, and the optimization of traffic performance through urban platooning—including the need for advanced communication
Efficient fire rescue operations in urban environments are critical for saving lives and reducing property damage. By utilizing connected vehicle systems (CVS) for firefighting vehicles planning, we can reduce the response time to fires while lowering the operational costs of fire stations. This research presents an innovative nonlinear mixed-integer programming model to enhance fire rescue operations in urban settings. The model focuses on expediting the movement of firefighting vehicles within intricate traffic networks, effectively tackling the complexities associated with collaborative dispatch decisions and optimal path planning for multiple response units. This method is validated using a small-scale traffic network, providing foundational insights into parameter impacts. A case study in Sioux Falls shows its superiority over traditional “nearest dispatch” methods, optimizing both cost and response time significantly. Sensitivity analyses involving clearance speed, clearance time
The emergence of connected vehicles is driven by increasing customer and regulatory demands. To meet these, more complex software applications, some of which require service-based cloud and edge backends, are developed. Due to the short lifespan of software, it becomes necessary to keep these cloud environments and their applications up to date with security updates and new features. However, as new behavior is introduced to the system, the high complexity and interdependencies between components can lead to unforeseen side effects in other system parts. As such, it becomes more challenging to recognize whether deviations to the intended system behavior are occurring, ultimately resulting in higher monitoring efforts and slower responses to errors. To overcome this problem, a simulation of the cloud environment running in parallel to the system is proposed. This approach enables the live comparison between simulated and real cloud behavior. Therefore, a concept is developed mirroring
Modern cars and autonomous vehicles (AVs) use millimeter wave (mmWave) radio frequencies to enable self-driving or assisted driving features that ensure the safety of passengers and pedestrians. This connectivity, however, can also expose them to potential cyberattacks.
Following its annual report detailing the growing cybersecurity threats to vehicles, fleets, and the networks they rely on, Upstream Security announced the launch of a generative AI tool to enhance its ability to reduce the risk posted by global threats. Israel-based Upstream, which has a vehicle security operations center (VSOC) in Ann Arbor, Mich., monitors millions of connected vehicles and Internet of Things (IoT) devices and billions of API transactions monthly. Ocean AI is built into the company's detection and response platform, called M-XDR, enabling its analysts, as well as those from OEMs and IoT vendors, to efficiently detect threat patterns and automate investigations before prioritizing a response.
The pace of innovation in automotive and heavy-duty transportation is rapidly accelerating. Manufacturers are harnessing advancements in electrification and electronification, ushering in new levels of safety, comfort, infotainment, connectivity, performance, and sustainability.
With the rapid growth of automobile ownership, traffic congestion has become a major concern at intersections. In order to alleviate the blockage of intersection traffic flow caused by signals, reduce the length of vehicle congestion and waiting time, and for improving the intersection access efficiency, therefore, this article proposes a vehicle speed guidance strategy based on the intersection signal change by combining the vehicle–road cooperative technology. The randomness of vehicle traveling speed in the road is being considered. According to the vehicle traveling speed, a speed guidance model is established under different conditions. Finally, the effectiveness of the speed guidance strategy in this article is verified through experimental simulation, and the benefits of the intersection with intelligent control and traditional control are compared, and the experimental results show that the intelligent control method in this article can effectively reduce vehicle congestion and
A new revolution has taken place in the automobile industry in recent years, intelligent and connected vehicle (ICV) [1] has achieved a higher market share in recent years and relevant technologies have been quickly developed and widely accepted, so the auto industry needs to make regulations for automated driving system (ADS) on ICVs, mainly to assure the safety of ICV. To meet the requirements above, the definition of operational design domain (ODD) [2, 3] was put forward by the Society of Automotive Engineers (SAE) and International Organization for Standardization (ISO) a few years ago. ODD defines necessary external environment conditions for the ADS to operate, but the internal status of the vehicle is also a key part of judging whether ADS can operate safely. Based on that, we propose a novel definition named operational design condition (ODC), which can supersede ODD to play a bigger role in improving regulations and standards, and promoting vehicle safety and technological
The functions of modern intelligent connected vehicles are becoming increasingly complex and diverse, and software plays an important role in these advanced features. In order to decouple the software and the hardware and improve the portability and reusability of code, Service-Oriented Architecture (SOA) has been introduced into the automotive industry. Data Distribution Service (DDS) is a widely used communication middleware which provides APIs for service-oriented Remote Procedure Call (RPC) and Service-Oriented Communications (SOC). By using DDS, application developers can flexibly define the data format according to their needs and transfer them more conveniently by publishing and subscribing to the corresponding topic. However, current open source DDS protocols all use unicast communication during the transmission of user data. When there are multiple data readers subscribing to the same topic, the data writer needs to send a unicast message to each data reader individually
In the ever-evolving landscape of automotive technology, the need for robust security measures and dependable vehicle performance has become paramount with connected vehicles and autonomous driving. The Unified Diagnostic Services (UDS) protocol is the diagnostic communication layer between various vehicle components which serves as a critical interface for vehicle servicing and for software updates. Fuzz testing is a dynamic software testing technique that involves the barrage of unexpected and invalid inputs to uncover vulnerabilities and erratic behavior. This paper presents the implementation of fuzz testing methodologies on the UDS layer, revealing the potential vulnerabilities that could be exploited by malicious entities. By employing both open-source and commercial fuzzing tools and techniques, this paper simulates real-world scenarios to assess the UDS layer’s resilience against anomalous data inputs. Specifically, we deploy several open-source UDS implementations on a
The software installed in Electronic Control Units (ECUs) has witnessed a significant scale expansion as the functionality of Intelligent Connected Vehicles (ICVs) has become more sophisticated. To seek convenient long-term functional maintenance, stakeholders want to access ECUs data or update software from anywhere via diagnostic. Accordingly, as one of the external interfaces, Diagnostics over Internet Protocol (DoIP) is inevitably prone to malicious attacks. It is essential to note that cybersecurity threats not only arise from inherent protocol defects but also consider software implementation vulnerabilities. When implementing a specification, developers have considerable freedom to decide how to proceed. Differences between protocol specifications and implementations are often unavoidable, which can result in security vulnerabilities and potential attacks exploiting them. Considering the security risks and technology trends of vehicles, this paper uses model learning for the
The suspension system plays a crucial role in mitigating vehicle vibration, enhancing passenger comfort, and improving driving handling stability. While many mechanical experimental platforms exist for testing suspension system performance, they often need high costs and precision requirements. In the field of modern industrial product design, hardware-in-the-loop (HIL) simulation has become an invaluable tool. Electrically interconnected suspension (EIS) is a novel type of interconnected suspension by connecting various suspensions in an electrical way. The novel EIS avoids many drawbacks of traditional interconnected suspensions. The EIS is usually composed of electromagnetic motors and electrical networks (EN). By designing the structure of the EN reasonably, the EIS system can achieve decoupling control in multiple vibration modes. This paper introduces an HIL experimental platform established for a half-car EIS system based on an NI Compact RIO 9049. The half-car electrically
Vehicle-to-everything (V2X) communication, primarily designed for communication between vehicles and other entities for safety applications, is now being studied for its potential to improve vehicle energy efficiency. In previous work, a 20% reduction in energy consumption was demonstrated on a 2017 Prius Prime using V2X-enabled algorithms. A subsequent phase of the work is targeting an ambitious 30% reduction in energy consumption compared to a baseline. In this paper, we present the Eco-routing algorithm, which is key to achieving these savings. The algorithm identifies the most energy-efficient route between an Origin-Destination (O-D) pair by leveraging information accessible through commercially available Application Programming Interfaces (APIs). This algorithm is evaluated both virtually and experimentally through simulations and dynamometer tests, respectively, and is shown to reduce vehicle energy consumption by 10-15% compared to the baseline over real-world routes. This
To help ensure that engine components are as reliable as customers need them to be, we have thus far evaluated them by establishing development target values based on market requirements, having engineers design parts to meet these requirements, then performing durability tests. These durability requirements are calculated to provide a margin of safety for use in the marketplace. However, depending on the part, these evaluation criteria can be overly aggressive against how it is used in the market, having led to a decrease in development efficiency as engine systems become more advanced. Therefore, in this study, we focused on the subject of high-cycle fatigue, which affects numerous components and is highly scalable, and built up a process for estimating the life span of components that would enable us to conduct appropriate evaluations that reflect how parts are truly used in the market. Recently, more and more vehicles are equipped with Telematics Control Units, (TCUs) which are
The automotive industry is currently undergoing a significant transformation characterized by technological and commercial trends involving autonomous driving, connectivity, electrification, and shared service. Vehicles are becoming an integral part of a much broader ecosystem. In light of various new developments, the Software-Defined Vehicle (SDV) concept is gaining substantial attention and momentum. SDV emphasizes the central role of software in realizing and enhancing vehicle functions, enriching features, improving performance, adapting to surrounding environment and external conditions, customizing user experience, addressing changing customer needs, and enabling vehicles to dynamically evolve over their entire life cycle. The advancements in vehicle Electrical/Electronic (E/E) architecture and various key technologies serve as the technical foundation for the emergence of SDV. This paper gives a definition of the SDV concept, provides views from different aspects, discusses the
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