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In the near future the automotive industry shall introduce a wide range of new, high technological functions and applications. These are being driven by the industries demand for a reduction in fuel consumption, pollutant emissions and an overall improvement to vehicle safety. These new demands go together with the spread of electronics and the implementation of more and more electrically driven systems, with medium and high power capabilities. These electrically driven systems either replace previous hydro-mechanical systems, for Power Steering for instance, or allow for the introduction of new functionalities like engine “Start & Stop”. They can even lead to completely new power train architecture like hybrid vehicles for example. With all these systems the demand for more equipment with power electronics, tailored to the automotive industry, rises tremendously.

Technical improvements recently brought to the main components for trucks and commercial vehicles (turbochargers, alternators, brakes,...) are reviewed in the paper. In conclusion it is stated that researches conducted in the heavy commercial vehicles field play an important role in technical automotive progress.

In the rapidly evolving era of software and autonomous driving systems, there is a pressing demand for extensive validation and accelerated development. This necessity arises from the need for copious amounts of data to effectively develop and train neural network algorithms, especially for autonomous vehicles equipped with sensor suites encompassing various specialized algorithms, such as object detection, classification, and tracking. To construct a robust system, sensor data fusion plays a vital role. One approach to ensure an ample supply of data is to simulate the physical behavior of sensors within a simulation framework. This methodology guarantees redundancy, robustness, and safety by fusing the raw data from each sensor in the suite, including images, polygons, and point clouds, either on a per-sensor level or on an object level. Creating a physical simulation for a sensor is an extensive and intricate task that demands substantial computational power.

Vibration from a mechanical system not only produces unwanted noises annoying to people around, but also runs a risk of fatigue failure that would actually hinder its functionality. There are several forms of vibration depending on the sources of excitation forms. Mechanical systems with rotating components can be subjected to sinusoidal excitation due to the fact the center of mass is not perfectly aligned with the rotating axis. If the rotating speed is strictly ramping up or ramping down, this can create an excitation whose frequency is changing with time in a frequency range corresponding to the speeds swept. Compared with a single sinusoidal excitation, the issue with fatigue at swept sinusoidal excitation, is that as it sweeps through a wide frequency range, some swept frequencies will definitely coincide with the natural frequencies of the system. Certainly, the stress response exactly at the resonant frequency becomes the highest and could account for a lot of fatigue damage.

This SAE EDGE Research Report addresses the unsettled topic of user acceptance of automated driving, analyzing the user experience for a more intuitive and safe driving experience. Unsettled Topics Concerning User Experience and Acceptance of Automated Vehicles examines the requirements for safer driver/user engagement with driving for the various SAE automation levels. It analyzes consumer sentiment toward automated driving - both consumer excitement about the perceived benefits and dislikes or concerns about the technology. The findings from surveys about drivers' experience with advanced driving assistance technologies and its application to automated driving is also brought to the surface of the discussion, together with driver profiles observed during a user-centric experience in an immersive automated driving cockpit.