Browse Topic: Vehicle to everything (V2X)
Electrical/Electronic Architectures (EEAs) are continuously evolving to meet newly emerging demands. In recent years, major drivers of this evolution have been the increasing software-defined nature of vehicles and the push toward automated driving. Key technologies such as edge-enhanced functions, vehicle-to-vehicle communication, and service-oriented architectures are therefore the focus of current research efforts. This paper presents a vision of how these technologies can be used to enable cooperation between vehicles, illustrated by using parked vehicles as edge nodes. These are typically seen as obstructions, as they significantly increase the risk of missing or misinterpreting vulnerable road users such as pedestrians or cyclists. Our proposed approach to counteract this problem is the use of the parked vehicles themselves as edge nodes that support object detection or even trajectory planning. Current research primarily considers smart traffic infrastructure, roadside units, and other vehicles as potential edge nodes. Including parked vehicles as edge nodes means that, instead of acting solely as obstacles, we leverage their built-in sensors to contribute to cooperative awareness. While such cooperation will enhance the safety of automated vehicles in urban areas, several challenges arise. In this paper, we discuss how data traceability, decision-making in the presence of conflicting information, and incentive mechanisms for owners of parked vehicles can be addressed. Based on these challenges, the paper outlines requirements for future cooperative architecture and highlights the role of edge-enhanced functions, Vehicle-to-Vehicle (V2V) communication, and service-oriented architectures in enabling fully automated driving.
Modern vehicles require sophisticated, secure communication systems to handle the growing complexity of automotive technology. As in-vehicle networks become more integrated with external wireless services, they face increasing cybersecurity vulnerabilities. This paper introduces a specialized Proxy based security architecture designed specifically for Internet Protocol (IP) based communication within vehicles. The framework utilizes proxy servers as security gatekeepers that mediate data exchanges between Electronic Control Units (ECUs) and outside networks. At its foundation, this architecture implements comprehensive traffic management capabilities including filtering, validation, and encryption to ensure only legitimate data traverses the vehicle's internal systems. By embedding proxies within the automotive middleware layer, the framework enables advanced protective measures such as intrusion detection systems, granular access controls, and protected over-the-air (OTA) update channels. This strategy enhances both data security and system isolation, creating protective boundaries between critical vehicle operations and potential external attacks. The architecture particularly excels in supporting Vehicle-to-Everything (V2X) connectivity, facilitating seamless information exchange between vehicles, roadside infrastructure, and pedestrians. This capability is essential for enhancing roadway safety, optimizing traffic flow, and supporting autonomous driving technologies. The system incorporates dedicated proxy modules for specialized protocols including Trivial File Transfer Protocol (TFTP), Diagnostic Over Internet Protocol (Doip), and Message Queuing Telemetry Transport (MQTT), each fulfilling specific functions in vehicle diagnostics, software updates, and telemetry data management. Performance evaluations will measure latency and throughput metrics to validate the architecture's efficiency and reliability. The framework's modular design aims to provide scalability and adaptability to accommodate both technological advancements and emerging security challenges. The proxy-based security framework presented offers a holistic and forward-looking approach to safeguarding in-vehicle networks. It provides automotive manufacturers with the tools to develop connected vehicles that combine intelligence and efficiency with robust protection against diverse cybersecurity threats.
This document provides vehicle-level data collection, data analysis, and data verification procedures that may be used to verify that an instrument under test (IUT) satisfies the vehicle-level requirements specified in SAE J3161/1. For the purposes of this report, “vehicle-level requirements” primarily consist of those requirements which can be verified external to the vehicle. The IUT for these procedures is a configured LTE-V2X vehicle-to-vehicle (V2V) device as defined in SAE J3161/1 and is installed on a vehicle of class 2, 3, 4, or 5. While the IUT is conceptually separated from the vehicle it is installed on, the tests outlined in this document are primarily vehicle level, so the terms “vehicle” and “IUT” can generally be considered interchangeable. Additionally, non-vehicle-level complementary tests, not included in this document, are required to verify that the entire set of requirements specified in SAE J3161/1 is satisfied. This document also includes a Traceability Matrix to provide traceability between SAE J3161/1 sections and the test procedures. This can be used to ensure thoroughness of testing coverage. The traceability matrix in Section 7 correlates test procedures from this document with the sections in SAE J3161/1 being tested. The SAE J3161/1 sections that are addressed in this document are indicated in Table 1. SAE J3161/1 major section numbers that are indicated as N/A do not contain any requirements (subsections may include requirements). Sections that are not in scope, such as standards profiles, are expected to be tested and verified as part of device-level certification, done prior to the vehicle-level testing described in this document.
This paper presents a comprehensive testing framework and safety evaluation for Vehicle-to-Vehicle (V2V) charging systems, incorporating advanced theoretical modeling and experimental validation of a modern, integrated 3-in-1 combo unit (PDU, DCDC, OBC). The proliferation of electric vehicles has necessitated the development of resilient and flexible charging solutions, with V2V technology emerging as a critical decentralized infrastructure component. This study establishes a rigorous mathematical framework for power flow analysis, develops novel safety protocols based on IEC 61508 and ISO 26262 functional safety standards, and presents comprehensive experimental validation across 47 test scenarios. The framework encompasses five primary test categories: functional performance validation, power conversion efficiency optimization, electromagnetic compatibility (EMC) assessment, thermal management evaluation, and comprehensive fault-injection testing including Byzantine fault scenarios. Through systematic experimental validation using advanced power electronics simulation and hardware-in-the-loop (HIL) testing, we demonstrate 98.2% power conversion efficiency, sub-50ms fault detection response times, and compliance with automotive safety integrity level ASIL-D requirements. Our results establish the theoretical foundations and practical validation methodologies essential for next-generation V2V charging infrastructure deployment.
The traditional hydraulic braking system with vacuum booster technology is very mature, but it is not suitable for use in electric vehicles due to the lack of a vacuum source. The brake system by wire is an innovative electronic controlled braking technology, and the Electro-Hydraulic Brake is currently the most widely used brake system by wire in electric vehicles. The classification, structure, working principle, and advantages of Electro-Hydraulic Brake as a braking system for electric automobiles and intelligent connected vehicles are studied. The structure, working principle, advantages and disadvantages of Pump-Electro - Hydraulic Brake and Integrated Electro-Hydraulic Brake are compared and analyzed.
A road simulator reproduction method was developed to reproduce the off-road conditions of utility vehicles in a laboratory setting. Off-road running behavior can be reproduced by considering the effects of inertial forces from jump landings owing to uneven terrain and slow-speed navigation. However, extremely low-frequency components and behaviors, including inertial forces from jumps, vehicle acceleration and deceleration, are difficult to reproduce with a normal road simulator in the limited test space of a laboratory. Therefore, it is common practice to intentionally remove input components below 1 Hz. Alternatively, inertial forces can be reproduced by adding a restraining device to the sprung mass of the vehicle along the wheel-axle inputs. Therefore, the former method excludes extremely low-frequency components, whereas the effects between actuators, which increase the test complexity and time required, should be canceled in the latter method. Furthermore, the restraining device on the sprung mass can be temporarily loaded differently from actual testing conditions, rendering the evaluation inaccurate. To address this problem, we focused on the vertical behavior of the sprung mass and actively incorporated it into moment input control in the extremely low-frequency range. Therefore, we successfully reproduced the behavior and input owing to the inertial forces. Additionally, using different frequency response functions for each running condition, it is possible to continuously reproduce sections with significantly different behaviors in a controlled setting. Consequently, RMS error values of less than 8% for the wheel force and 11% for the suspension stroke were achieved in road simulation. Moreover, the accumulated fatigue damage in the structural components closely matches the actual driving conditions with a high degree of accuracy. This enabled stable testing regardless of changes in weather and course conditions, thereby improving the accuracy of the evaluations.
Vehicle-to-whatever communication technologies continue to be put through their paces around the world. To point at just one example of the continued evolution of V2X technologies, let's take a quick visit to Japan and the 2025 JSAE Annual Spring Congress this May. That's where Toyota and Eye-Net Mobile Ltd., a subsidiary of Foresight Autonomous Holdings Ltd., presented a research paper on using vehicle-to-network technology to enhance ADAS systems by connecting to smartphones in the environment to address the inherent limitations of in-vehicle sensors. Titled, “Feasibility Study of a Hazard Avoidance Brake Control System Using V2N Technology,” the paper examined how smartphones could act as external sensors that could connect to an onboard ADAS system using vehicle-to-network (V2N) communications. The main purpose of these signals would be for enhanced hazard detection, Toyota said, adding that some of the top issues addressed in the paper were “communication latency, tracking accuracy, and the positioning precision of these devices in diverse urban environments,” where direct line-of-sight isn't always possible.
The U-Shift IV represents the latest evolution in modular urban mobility solutions, offering significant advancements over its predecessors. This innovative vehicle concept introduces a distinct separation between the drive module, known as the driveboard, and the transport capsules. The driveboard contains all the necessary components for autonomous driving, allowing it to operate independently. This separation not only enables versatile applications - such as easily swapping capsules for passenger or goods transportation - but also significantly improves the utilization of the driveboard. By allowing a single driveboard to be paired with different capsules, operational efficiency is maximized, enabling continuous deployment of driveboards while the individual capsules are in use. The primary focus of U-Shift IV was to obtain a permit for operating at the Federal Garden Show 2023. To achieve this goal, we built the vehicle around the specific requirements for semi-public road operations which includes narrow streets and pedestrians. This involved integrating necessary modifications across multiple domains, including the e/e-architecture, sensor setup, software stack, and even the design of the driveboard and capsule. By utilizing systematic methods to address regulatory and safety challenges, we ensured that the vehicle met the standards required for autonomous driving in semi-public environments. In this paper, we explore the methodologies employed to achieve regulatory compliance, focusing on sensor integration, software- and e/e-architecture. We discuss our multi-modal sensor setup, which combines camera, lidar and radar to archive redundancy and enhanced environmental perception. Additionally, we provide an overview of our software architecture, emphasizing its role in ensuring safe driving functions and enabling autonomous operations.
This article introduces a comprehensive cooperative navigation algorithm to improve vehicular system safety and efficiency. The algorithm employs surrogate optimization to prevent collisions with cooperative cruise control and lane-keeping functionalities. These strategies address real-world traffic challenges. The dynamic model supports precise prediction and optimization within the MPC framework, enabling effective real-time decision-making for collision avoidance. The critical component of the algorithm incorporates multiple parameters such as relative vehicle positions, velocities, and safety margins to ensure optimal and safe navigation. In the cybersecurity evaluation, the four scenarios explore the system’s response to different types of cyberattacks, including data manipulation, signal interference, and spoofing. These scenarios test the algorithm’s ability to detect and mitigate the effects of malicious disruptions. Evaluate how well the system can maintain stability and avoid collisions under compromised conditions. It also analyzes the impact of varying levels of attack severity on overall system performance. The cooperative navigation framework highlights its potential as a robust solution for secure, efficient, and safe autonomous vehicle operations in increasingly interconnected and potentially hostile environments. Case 1 simulates communication jamming, where all channels except vehicle-to-vehicle communication are compromised. Case 2 extends this to jamming in the smart traffic light system, creating a non-signalized environment. Case 3 represents an ideal scenario with seamless communication. Case 4 explores vulnerability to deliberate interference in actor vehicle velocities, amplifying collision risk. Surrogate optimization with radial functions ensures proactive collision avoidance, while model predictive control with the interior point solver optimizes trajectory planning, promoting collision-free operation, and improving traffic flow. The algorithm’s outputs are seamlessly integrated into the vehicle control system, with the ego vehicle’s dynamics modeled realistically. Through extensive simulations, the algorithm proves effective across diverse scenarios, including communication disruptions and intentional interference. The research contributes to cooperative navigation system advancement, showcasing potential improvements in safety, efficiency, and adaptability in contemporary vehicular environments. The algorithm’s ability to handle various scenarios presents promising prospects for future intelligent transportation systems research.
Letter from the Guest Editors
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