Browse Topic: Management and Organizations
The rapid introduction of new Automated Driving Systems (ADS) in the last years has led to an urge for robust methodologies for the type approval of vehicles equipped with such technologies. As a result, different Regulations addressing this field have been adopted. These Regulations are mainly based in the New Assessment and Testing Methodology (NATM) developed within the World Forum for the Harmonisation of Vehicle Regulations (WP29). However, the complexity of the regulatory ecosystem extends beyond type approval. This complexity requires a thorough analysis in order to avoid any possible gap which may jeopardise the feasibility of Automated Driving Vehicles deployment. This paper analyses the possible mismatches among the different regulations currently in place or under development and proposes a holistic approach, where the concept of the Operational Design Domain (ODD) takes a relevant role.
Driver-in-the-Loop (DIL) simulators have become crucial tools across automotive, aerospace, and maritime industries in enabling the evaluation of design concepts, testing of critical scenarios and provision of effective training in virtual environments. With the diverse applications of DIL simulators highlighting their significance in vehicle dynamics assessment, Advanced Driver Assistance Systems (ADAS) and autonomous vehicle development, testing of complex control systems is crucial for vehicle safety. By examining the current landscape of DIL simulator use cases, this paper critically focuses on Virtual Validation of ADAS algorithms by testing of repeatable scenarios and effect on driver response time through virtual stimuli of acoustic and optical warnings generated during simulation. To receive appropriate feedback from the driver, industrial grade actuators were integrated with a real-time controller, a high-performance workstation and simulation software called Virtual Test
The design and improvement of electric motor and inverter systems is crucial for numerous industrial applications in electrical engineering. Accurately quantifying the amount of power lost during operation is a substantial challenge, despite the flexibility and widespread usage of these systems. Although it is typically used to assess the system’s efficiency, this does not adequately explain how or why power outages occur within these systems. This paper presents a new way to study power losses without focusing on efficiency. The goal is to explore and analyze the complex reasons behind power losses in both inverters and electric motors. The goal of this methodology is to systematically analyze the effect of the switching frequency on current ripple under varying operating conditions (i.e., different combinations of current and speed) and subsequently identify the optimum switching frequency for each case. In the end, the paper creates a complete model for understanding power losses
This study introduces a novel in-cabin health monitoring system leveraging Ultra-Wideband (UWB) radar technology for real-time, contactless detection of occupants' vital signs within automotive environments. By capturing micro-movements associated with cardiac and respiratory activities, the system enables continuous monitoring without physical contact, addressing the need for unobtrusive vehicle health assessment. The system architecture integrates edge computing capabilities within the vehicle's head unit, facilitating immediate data processing and reducing latency. Processed data is securely transmitted via HTTPS to a cloud-based backend through an API Gateway, which orchestrates data validation and routing to a machine learning pipeline. This pipeline employs supervised classifiers, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) to analyze features such as temporal heartbeat variability, respiration rate stability, and heart rate. Empirical
This research paper offers a comprehensive evaluation of lithium-ion battery recycling methods, tracing the entire journey from global demand to the practical challenges and solutions for sustainable battery recycling. It starts with the analysis of worldwide LIB demand growth alongside the exponential growth in volumes of spent batteries and recycling rates. The study focuses on the imbalance in production and recovery of critical battery components and its environmental and economic effects. The paper then systematically examines six major recycling methodologies: mechanical, pyrometallurgical, hydrometallurgical, biotechnological, direct, and ion-exchange recycling. It goes into detail about their advantages, limitations, and roles in maximizing the recovery of valuable metals such as lithium, cobalt, and nickel. Traditional techniques like hydrometallurgical and pyrometallurgical methods, and emerging approaches including bioleaching and ion-exchange, are evaluated for their
In-vehicle communication among different vehicle electronic controller units (ECU) to run several applications (I.e. to propel the vehicle or In-vehicle Infotainment), CAN (Controller Area Network) is most frequently used. Given the proprietary nature and lack of standardization in CAN configurations, which are often not disclosed by manufacturers, the process of CAN reverse engineering becomes highly complex and cumbersome. Additionally, the scarcity of publicly accessible data on electric vehicles, coupled with the rapid technological advancements in this domain, has resulted in the absence of a standardized and automated methodology for reverse engineering the CAN. This process is further complicated by the diverse CAN configurations implemented by various Original Equipment Manufacturers (OEMs). This paper presents a manual approach to reverse engineer the series CAN configuration of an electric vehicle, considering no vehicle information is available to testing engineers. To
Autonomous vehicle (AV) regulatory frameworks vary significantly across global regions, with the United States, European Union (EU), and China exemplifying distinct approaches. The US adopts a decentralized model, allowing state-level regulation with federal guidance, fostering testing and commercial deployment of Level 4 automation. The EU enforces a harmonized, safety-focused framework under legislation like Regulation (EU) 2019/2144 and (EU) 2022/1426, emphasizing structured validation within defined operational domains. China employs a centralized regulatory hierarchy, integrating national standards with localized pilot programs and connected infrastructure. While the US leads in commercial deployment and China advances through coordinated efforts, the EU’s cautious framework is often perceived as a barrier to rapid AV adoption. This paper critically analyzes these regulatory models, emphasizing the need for a robust, harmonized framework that ensures safety and public trust
Accidents during lane changes are increasingly becoming a problem due to various human based and environment-based factors. Reckless driving, fatigue, bad weather are just some of these factors. This research introduces an innovative algorithm for estimating crash risk during lane changes, including the Extended Lane Change Risk Index (ELCRI). Unlike existing studies and algorithms that mainly address rear-end collisions, this algorithm incorporates exposure time risk and anticipated crash severity risk using fault tree analysis (FTA). The risks are merged to find the ELCRI and used in real time applications for lane change assist to predict if lane change is safe or not. The algorithm defines zones of interest within the current and target lanes, monitored by sensors attached to the vehicle. These sensors dynamically detect relevant objects based on their trajectories, continuously and dynamically calculating the ELCRI to assess collision risk during lane changes. Additionally
Functional Mock-up Units (FMUs) have become a standard for enabling co-simulation and model exchange in vehicle development. However, traditional FMUs derived from physics-based models can be computationally intensive, especially in scenarios requiring real-time performance. This paper presents a Python-based approach for developing a Neural Network (NN) based FMU using deep learning techniques, aimed at accelerating vehicle simulation while ensuring high fidelity. The neural network was trained on vehicle simulation data and trained using Python frameworks such as TensorFlow. The trained model was then exported into FMU, enabling seamless integration with FMI-compliant platforms. The NN FMU replicates the thermal behavior of a vehicle with high accuracy while offering a significant reduction in computational load. Benchmark comparisons with a physical thermal model demonstrate that the proposed solution provides both efficiency and reliability across various driving conditions. The
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