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Internal audits are a requirement of the AS9100, AS 13100 and RM 13005 and are intended to verify the compliance and effectiveness of an organization's quality management system. The methods and techniques for performing internal audits have significantly changed in the aviation, space and defense industries, and internal auditors must be knowledgeable of these requirements and the expectations as identified in the standard.

Crash reconstruction is a scientific process that utilizes principles of physics and empirical data to analyze the physical, electronic, video, audio, and testimonial evidence from a crash to determine how and why the crash occurred. This course will introduce this reconstruction process as it gets applied to various crash types - in-line and intersection collisions, pedestrian collisions, motorcycle crashes, rollover crashes, and heavy truck crashes. Methods of evidence documentation will be covered. Analysis methods will also be presented for electronic data from event data recorders and for video.

The UN R155 regulation is the first automotive cyber security regulation and has made security a mandatory approval criterion for new vehicle types. This establishes internationally harmonized security requirements for market approval. As a result, the application of the regulation presents manufacturers and suppliers with the challenge of demonstrating compliance. At process level the implementation of a Cyber Security Management System (CSMS) is required while at product level, the Threat Assessment and Risk Analysis (TARA) forms the basis to identify relevant threats and corresponding mitigation strategies. Overall, an issued type approval is internationally recognized by the member states of the UN 1958 Agreement. International recognition implies that uniform assessment criteria are applied to demonstrate compliance and to decide whether security efforts are sufficient.

The conventional process of last-mile delivery logistics often leads to safety problems for road users and a high level of environmental pollution. Delivery drivers must deal with frequent stops, search for a convenient parking spot and sometimes navigate through the narrow streets causing traffic congestion and possibly safety issues for the ego vehicle as well as for other traffic participants. This process is not only time consuming but also environmentally impactful, especially in low-emission zones where prolonged vehicle idling can lead to air pollution and to high operational costs. To overcome these challenges, a reliable system is required that not only ensures the flexible, safe and smooth delivery of goods but also cuts the costs and meets the delivery target.

In the evolving landscape of automated driving systems, the critical role of vehicle localization within the autonomous driving stack is increasingly evident. Traditional reliance on Global Navigation Satellite Systems (GNSS) proves to be inadequate, especially in urban areas where signal obstruction and multipath effects degrade accuracy. Addressing this challenge, this paper details the enhancement of a localization system for autonomous public transport vehicles, focusing on mitigating GNSS errors through the integration of a LiDAR sensor. The approach involves creating a 3D map using the factor graph-based LIO-SAM algorithm based on GNSS, vehicle odometry, IMU and LiDAR data. The algorithm is adapted to the use-case by adding a velocity factor and altitude data from a Digital Terrain model. Based on the map a state estimator is proposed, which combines high-frequency LiDAR odometry based on FAST-LIO with low-frequency absolute multiscale ICP-based LiDAR position estimation.

The global time that is propagated and synchronized in the vehicle E/E architecture is used in safety-critical, security-critical, and time-critical applications (e.g., driver assistance functions, intrusion detection system, vehicle diagnostics, external device authentication during vehicle diagnostics, vehicle-to-grid and so on). The cybersecurity attacks targeting the global time result in false time, accuracy degradation, and denial of service as stated in IETF RFC 7384. These failures reduce the vehicle availability, robustness, and safety of the road user. IEEE 1588 lists four mechanisms (integrated security mechanism, external security mechanism, architectural solution, and monitoring & management) to secure the global time. AUTOSAR defines the architecture and detailed specifications for the integrated security mechanism "Secured Global Time Synchronization (SGTS)" to secure the global time on automotive networks (CAN, FlexRay, Ethernet).

In the context of urban smart mobility, vehicles have to communicate with each other, surrounding infrastructure, and other traffic participants. By using Vehicle2X communication, it is possible to exchange the vehicles’ position, driving dynamics data, or driving intention. This concept yields the use for cooperative driving in urban environments. Based on current V2X-communication standards, a methodology for cooperative driving of automated vehicles in mixed traffic scenarios is presented. Initially, all communication participants communicate their dynamic data and planned trajectory, based on which a prioritization is calculated. Therefore, a decentralized cooperation algorithm is introduced. The approach is that every traffic scenario is translatable to a directed graph, based in which a solution for the cooperation problem is computed via an optimization algorithm.

Previous studies have shown that dosing AdBlue into the exhaust system of diesel engines to reduce nitrogen oxides can lead to an increase in the number of particles (PN). In addition to the influencing factors of exhaust gas temperature, exhaust gas mass flow and dosing quantity, the dosed medium itself (AdBlue) is not considered as a possible influence due to its regulation in ISO standard 22241. However, as the standard specifies limit value ranges for the individual regulated properties and components for newly sold AdBlue, in reality there is still some margin in the composition. This paper investigates the particle number increase due to AdBlue dosing using several CPCs. The increase in PN is determined by measuring the number of particles after DPF and thus directly before dosing as well as tailpipe. Several AdBlue products from different sources and countries are measured and their composition is also analyzed with regard to the limit values regulated in the standard.

The optimization and further development of automated driving functions offers great potential to relieve the driver in various driving situations and increase road safety. Simulative testing in particular is an indispensable tool in this process, allowing conclusions to be drawn about the design of automated driving functions at a very early stage of development. In this context, the use of driving simulators provides support so that the driving functions of tomorrow can be experienced in a very safe and reproducible environment. The focus of the acceptance and optimization of automated driving functions is particularly on vehicle lateral control functions. As part of this paper, a test person study was carried out regarding manual vehicle lateral control on the dynamic vehicle road simulator at the Institute of Automotive Engineering.

In recent years, the urgent need to fully exploit the fuel economy potential of the Electrified Vehicles (xEVs) through the optimal design of their Energy Management System (EMS) have led to an increasing interest in Machine Learning (ML) techniques. Among them, Reinforcement Learning (RL) seems to be one of the most promising approaches thanks to its peculiar structure, in which an agent is able to learn the optimal control strategy through the feedback received by a direct interaction with the environment. Therefore, in this study, a new Soft Actor-Critic agent (SAC), which exploits a stochastic policy, was implemented on a digital twin of a state-of-the-art diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. The SAC agent was trained to enhance the fuel economy of the PHEV while guaranteeing its battery charge sustainability.

Reductions in powertrain noise have led to an increased proportion of road noise, prompting various studies aimed at mitigating it. Road excitation primarily traverses through the vehicle suspension system, necessitating careful optimization of the characteristics of bushings at connection points. However, optimizing at the vehicle assembly stage is both time-consuming and costly. Therefore, it is essential to proceed with optimization at the subsystem level using appropriate objective functions. In this study, the blocked force and energy-based index derived from complex power were used to optimize the NVH performance. Calculating the complex power in each bushing enables computing the power flow, thereby providing a basis for evaluating the NVH performance. Through stiffness injection, the frequency response functions (FRF) of the system can be predicted according to arbitrary changes in the bushing stiffness.

With the electrification of powertrains, the noise level inside vehicles reach high levels of silence. The dominant engine noise found in traditional vehicles is now replaced by other sources of noise such as rolling noise and aeroacoustic noise. These noises are encountered during driving on roads and highways and can cause significant fatigue during long journeys. Regarding aeroacoustic phenomena, the noise transmitted into the cabin is the result of both turbulent pressure and acoustic pressure created by the airflow. Even though it is lower in level, the acoustic pressure induces most of the noise perceived by the occupants. Its wavelength is closer to the characteristic vibration wavelengths of the glass, making its propagation more efficient through the vehicle's windows. The accurate modeling of these phenomena requires the coupling of high-frequency computational fluid dynamics (CFD) simulations and vibro-acoustic simulations.

The NVH performance of electric vehicles is a key indicator of vehicle quality, being the structure-borne transmission predominating at low frequencies. Many issues are typically generated by high vibrations, transmitted through different paths, and then radiated acoustically into the cabin. A combined analysis, with both finite-element and multi-body models, enables to predict the interior vehicle noise and vibration earlier in the development phases, to reduce the development time and moreover to optimize components with an increased efficiency level. In the present work, a simulation of a Hyundai electric vehicle has been performed in IDIADA VPG with a full vehicle multibody (MBD) model, followed by vibration/acoustic simulations with a Finite elements model (FEM) in MSC. Nastran to analyze the comfort. Firstly, a full vehicle MBD model has been developed in MSC. ADAMS/Car including representative flexible bodies (generated from FEM part models).