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The mobility industry and the entire ecosystem is currently striving towards sus-tainable mobility which leads to continuous production ramp-up of electrified vehicles. The parallel increase of the charging infrastructure is faced with various challenges regarding needed investments and the connection into the electricity grid. MAHLE chargeBIG offers centralized and large scaled charging infrastruc-ture with more than 1,800 already installed charging points. This presentation and paper is evaluating the functionality of the system by ana-lyzing backend real data of various employer parking installations. It can be shown and proven that a single-phase charging concept is sufficient and able to manage most customer relevant charging events by considering the needs and limitations of the related electricity grid infrastructure. Smart charging algorithms enable the integration of the charging infrastructure in smart grid company environments.

In vehicle ego-motion estimation, vehicle control, and advanced driver assist systems the vehicle dynamics are described by a few key parameters. The side-slip gradient, being one of them, is used to model the lateral behavior of the vehicle. This parameter is rarely known precisely, since it depends on the vehicle’s mass distribution, its tires, and even the chassis setup. Thus, an online-estimation of the side-slip gradient is beneficial, especially in serial applications. Estimating the side-slip gradient with conventional vehicle sensors such as wheel-speed, steering, and inertial sensors poses a significant challenge since considerable dynamic excitation of the vehicle is required, which is uncommon in normal driving. Here, radar sensors open new opportunities in the estimation of such vehicle dynamics parameters since they allow for an instantaneous measurement of the lateral velocity.

An essential component in the approval of advanced driver assistance systems (ADAS) and automated driving systems (ADS) is the quantification of residual risk, which demonstrates that hazardous behavior (HB) occurs less frequently than specified by a corresponding acceptance criterion. In the case of HB with high potential impact severity, only very low accepted frequencies of occurrence are tolerated. To avoid uncertainties due to abstractions and simplifications in simulations, the proof of the residual risk in systems such as advanced emergency braking systems (AEBS) is often partially or entirely implemented as system-level field test. However, the low rates and high confidence required, common for residual risk demonstrations, result in a significant disadvantage of these field tests: the long driving distance required.

Autonomous Emergency Braking (AEB) systems are critical in preventing collisions, yet their effectiveness hinges on accurately estimating the distance between the vehicle and other road users, as well as understanding road conditions. Errors in distance estimation can result in premature or delayed braking and varying road conditions alter road-tire friction coefficients, affecting braking distances. Advancements in sensor technology and deep learning have improved vehicle perception and real-world understanding. The integration of advanced sensors like LiDARs has significantly enhanced distance estimation. Cameras and deep neural networks are also employed to estimate the road conditions. However, AEB systems face notable challenges in urban environments, influenced by complex scenarios and adverse weather conditions such as rain and fog. Therefore, investigating the error tolerance of these estimations is essential for the performance of AEB systems.

As part of the safety validation of advanced driver assistance systems (ADAS) and automated driving (AD) functions, it is necessary to demonstrate that the frequency at which the system exhibits hazardous behavior (HB) in the field is below an acceptable threshold. This is typically tested by observation of the system behavior in a field operational test (FOT). For situations in which the system under test (SUT) actively intervenes in the dynamic driving behavior of the vehicle, it is assessed whether the SUT exhibits HB. Since the accepted threshold values are generally small, the amount of data required for this strategy is usually very large. This publication proposes an approach to reduce the amount of data required for the evaluation of emergency intervention systems with a state machine based intervention logic by including the time periods between intervention events in the validation process.

Advanced Air Mobility (AAM) envisions heterogenous airborne entities like crewed and uncrewed passenger and cargo vehicles within, and between urban and rural environment. To achieve this, a paradigm shift to a cooperative operating environment similar to Extensible Traffic Management (xTM) is needed. This requires the blending of Traditional Air Traffic Services (ATS) with the new generation AAM vehicles having their unique flight dynamics and handling characteristics. A hybrid environment needs to be established with enhanced shared situational awareness for all stakeholders, enabling equitable airspace access, minimizing risk, optimized airspace use, and providing flexible and adaptable airspace rules. This paper introduces a novel concept of distributed airspace management which would be apt for all kinds of operational scenarios perceived for AAM. The proposal is centered around the efficiency and safety in air space management being achieved by self-discipline.

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