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A reliable environment perception is a requirement for safe automated driving. For evaluating and demonstrating the reliability of the vehicle’s environment perception, field tests offer testing conditions that come closest to the vehicle’s driving environment. However, establishing a reference ground truth in field tests is time-consuming. This motivates the development of a procedure for learning the vehicle’s perception reliability from fleet data without the need for a ground truth, which would allow learning the perception reliability from fleet data. In Berk et al. (2019), a method based on Bayesian inference to determine the perception reliability of individual sensors without the need for a ground truth was proposed. The model utilizes the redundancy of sensors to learn the sensor’s perception reliability. The method was tested with simulated data.

One main goal of the automotive industry is to reduce the aerodynamic drag of passenger vehicles. Therefore, a deeper understanding of the flow field is necessary. Time-resolved data of the flow field is required to get an insight into the complex unsteady flow phenomena around passenger vehicles. This data helps to understand the temporal development of wake structures and enables the analysis of the formation of vortical structures. Numerical simulations are an efficient method to analyze the time-resolved data of the unsteady flow field. The analysis of the steady and unsteady numerical data is only relevant for aerodynamic developments in the wind tunnel, if the predicted temporal evolving structures of a passenger vehicle’s simulated flow field correspond to the structures of the flow field in the wind tunnel. In this study, time-resolved measurements of the empty wind tunnel and a notchback passenger vehicle in the wind tunnel are conducted.

The performance of environment perceiving sensors such as e.g. lidar, radar, camera and ultrasonic sensors is safety critical for automated driving vehicles. Therefore, one has to assess the sensors’ performance to assure the automated driving system’s safety. The performance of these sensors is however to some degree sensitive towards adverse weather conditions. A challenge is to quantify the effect of adverse weather conditions on the sensor’s performance early in the development of an automated driving system. This challenge is addressed in this work for lidar sensors. The lidar equation was previously employed in this context to derive estimates of a lidar’s maximum range in different weather conditions. In this work, we present a stochastic simulation framework based on a probabilistic extension of the lidar equation, to quantify the effect of adverse rainfall conditions on a lidar’s raw detection performance.

Minimizing the lap time for a given race track is the main target in racecar development. In order to achieve the highest possible performance of the vehicle configuration the mutual interaction at the level of assemblies and components requires a balance between the advantages and disadvantages for each design decision. Especially the major shift in the focus of racecar powerunit development to high efficiency powertrains is driving a development of lean boosted and rightsized engines. In terms of dynamic engine behavior the time delay from requested to provided torque could influence the lap time performance. Therefore, solely maximizing the full load behavior objective is insufficient to achieve minimal lap time. By means of continuous predictive virtual methods throughout the whole development process, the influence on lap time by dynamic power lags, e.g. caused by the boost system, can be recognized efficiently even in the early concept phase.

With increasing levels of driving automation, the perception provided by automotive environment sensors becomes highly safety relevant. A correct assessment of the sensors’ perception reliability is therefore crucial for ensuring the safety of the automated driving functionalities. There are currently no standardized procedures or guidelines for demonstrating the perception reliability of the sensors. Engineers therefore face the challenge of setting up test procedures and plan test drive efforts. Null Hypothesis Significance Testing has been employed previously to answer this question. In this contribution, we present an alternative method based on Bayesian parameter inference, which is easy to implement and whose interpretation is more intuitive for engineers without a profound statistical education. We show how to account for different environmental conditions with an influence on sensor performance and for statistical dependence among perception errors.

Driven by the growing internet and remote connectivity of automobiles, combined with the emerging trend to automated driving, the importance of security for automotive systems is massively increasing. Although cyber security is a common part of daily routines in the traditional IT domain, necessary security mechanisms are not yet widely applied in the vehicles. At first glance, this may not appear to be a problem as there are lots of solutions from other domains, which potentially could be re-used. But substantial differences compared to an automotive environment have to be taken into account, drastically reducing the possibilities for simple reuse. Our contribution is to address automotive electronics engineers who are confronted with security requirements. Therefore, it will firstly provide some basic knowledge about IT security and subsequently present a selection of automotive specific security use cases.

Longitudinal models are used to evaluate different vehicle-engine concepts with respect to driving behavior and emissions. The engine is generally map-based. An explicit calculation of both fluid dynamics inside the engine air path and cylinder combustion is not considered due to long computing times. Particularly for dynamic certification cycles (WLTC, US06 etc.), dynamic engine effects severely influence the quality of results. Hence, an evaluation of transient engine behavior with map-based engine models is restricted to a certain extent. The coupling of detailed 1D-engine models is an alternative, which rapidly increases the model computation time to approximately 300 times higher than that of real time. In many technical areas, the Fourier transformation (FT) method is applied, which makes it possible to represent superimposed oscillations by their sinusoidal harmonic oscillations of different orders.

The open jet wind tunnel is a widespread test section configuration for developing full scale passenger cars in the automotive industry. However, using a realizable nozzle cross section for cost effective aerodynamic development is always connected to the presence of wind tunnel effects. Wind tunnel wall interferences which are not present under open road conditions, can affect the measurement of aerodynamic forces. Thus, wind tunnel corrections may be required. This work contains the results of a CFD (Computational Fluid Dynamics) approach using unsteady Delayed Detached Eddy Simulations (DDES) to evaluate wind tunnel interferences for open jet test sections. The Full Scale DrivAer reference geometry of the Technical University of Munich (TUM) using different rear end shapes has been selected for these investigations.

Automotive engineering processes are dynamic, iterative and driven by changes. Reasons for changes on development artifacts are manifold, but the result is a new evolution step which may influence all, some, or just a single development artifact. Consequently, research on impact analysis put forth approaches to assess the adverse effects of changes. However, understanding and implementing functional changes and its consequences in the safety domain is often aggravated by dependencies between different types of development artifacts, scattered in various (tool) formats. Safety properties may change depending on the type of a modification. Thereby, connected analyses like fault trees, Failure Modes and Effects Analysis (FMEA), and safety concepts cannot be reused easily if the artifacts on which they are based on are affected by changes. In this paper we suggest a new difference analysis approach which allows a (semi-)automated comparison of safety work products based on models.

The trend towards even more sophisticated driver assistance systems and growing automation of driving sets new requirements for the robustness and availability of the involved automotive systems. In case of an error, today it is still sufficient that safety related systems just fail safe or silent to prevent safety related influence of the driving stability resulting in a functional deactivation. But the reliance on passive mechanical fallbacks in which the human driver taking over control, being inevitable in such a scenario, is expected to get more and more insufficient along with a rising degree of driving automation as the driver will be given longer reaction time. The advantage of highly or even fully automated driving is that the driver can focus on other tasks than controlling the car and monitoring it’s behavior and environment.

In this work we extended the findings from a previous study by the authors on the mechanisms and influence factors of deposit formation in urea-based selective catalytic reduction systems (SCR) [1]. A broader range of operating conditions was investigated in detail. In order to quantify the boundary conditions of deposition, a representative set of deposits was studied during formation and decomposition. A box of heat resisting glass was equipped with a surrogate mixing element to monitor solidification timescales, temperatures and deposit growth. A chemical analysis of the deposits was performed using thermogravimetry. The depletion timescales of individual deposit components were systematically investigated. A moderate temperature increase to 350 °C was deemed sufficient to trigger fast decomposition of deposits formed below 250 °C.

The German funded project ARAMiS included work on several demonstrators one of which was a multicore approach on large scale software integration (LSSI) for the automotive domain. Here BMW and Audi intentionally implemented two different integration platforms to gain both experience and real life data on a Hypervisor based concept on one side as well as using only native AUTOSAR-based methods on the other side for later comparison. The idea was to obtain figures on the added overhead both for multicore as well as safety, based on practical work and close-to-production implementations. During implementation and evaluation on one hand there were a lot of valuable lessons learned about multicore in conjunction with safety. On the other hand valuable information was gathered to make it finally possible to set up a cost model for estimation of potential overhead generated by different integration approaches for safety related software functions.

Strategies for weight reduction have driven the noise treatment advanced developments with a great success considering the already mastered weight decreases observed in the last years in the automotive industry. This is typically the case for all soft trims parts. In the early 2010's a typical european B-segment car soft trims weights indeed 30 to 40% less than in the early 2000's years. The main driver behind such a gap has been to combine insulation and absorption properties on a single part while increasing the number of layers. This product-process evolution was conducted using a significant improvement in the simulation capacities. In that sense, several studies presenting very good correlation results between Transmission Loss measurements and finite elements simulations on dashboard or floor insulators were presented. One may consider that those kinds of parts have already achieved a considerable improvement in performance.

This study deals with the experimental investigation of the mechanical properties of a lithium-ion pouch cell and its modelling in an explicit finite element simulation code. One can distinguish between ‘macroscopic’ and ‘microscopic’ modelling approaches. In the ‘macroscopic’ approach, one material model approximates the behaviour of multiple inner cell layers. In the ‘microscopic’ approach, which is used in the present study, all layers and their interactions are modelled separately. The cell under study is a pouch-type lithium-ion cell with a liquid electrolyte. With its cell chemistry, design, size and capacity it is usable for automotive applications and can be assembled into traction batteries. One cell sample was fully discharged and disassembled, and its components (anode, cathode, separator and pouch) were examined and measured by electron microscopy. Components were also tensile tested.

The increased pressure for power, space, and cost reduction in automotive applications together with the availability of high performance, automotive qualified multicore microcontrollers has lead to the ability to engineer Domain Controller ECUs that can host several separate applications in parallel. The standard automotive constraints however still apply, such as use of AUTOSAR operating system, support for legacy code, hosting OEM supplied code and the ability to determine warranty issues and responsibilities between a group of Tier 1 and Tier 2 vendors who all provide Intellectual Property to the final production ECU. Requirements for safety relevant applications add even more complexity, which in most current approaches demand a reconfiguration of all basic software layers and a major effort to redesign parts of the application code to enable co-existence on the same hardware platform. This paper outlines the conflicting requirements of hosting multiple applications.

Multicore-based ECUs are increasingly used in embedded automotive software systems to allow more demanding automotive applications at moderate cost and energy consumption. Using a high number of parallel processors together with a high number of executed software components results in a practically unmanageable number of deployment alternatives to choose from. However correct deployment is one important step for reaching timing goals and acceptable latency, both also a must to reach safety goals of safety-relevant automotive applications. In this paper we focus at reducing the complexity of deployment decisions during the phases of allocation and scheduling. We tackle this complexity of deployment decisions by a mixed constructive and analytic approach.

It is the objective of this work to characterize mixture formation in the sprays emanating from Multi-Layer (ML) nozzles under approximately engine-like conditions by quantitative, spatially, and temporally resolved fuel-air ratio and temperature measurements. ML nozzles are cluster nozzles which have more than one circle of orifices. They were introduced previously, in order to overcome the limitations of conventional nozzles. In particular, the ML design yields the potential of variable spray interaction, so that mixture formation could be controlled according to the operating condition. In general, it was also a primary aim of the cluster-nozzle concepts to combine the enhanced atomization and pre-mixing of small nozzle holes with the longer spray penetration lengths of large holes. The applied diagnostic, which is based on 1d spontaneous Raman scattering, yields the quantitative stoichiometric ratio and the temperature in the vapor phase.

The anti-lock braking system (ABS) is a widespread driver assistance system which allows a short braking distance while simultaneously maintaining the stability and steerability of the car. Vehicles with electric single-wheel drive offer many possibilities of improving the energy efficiency and the braking performance during ABS braking. In this paper, two different ways of including the electric machines in the ABS are analyzed in detail: the damping of torsional drive train vibrations in combination with recuperation and the dynamic split of the braking torque, where the hydraulic braking torque is kept constant and the dynamic modulation of the braking torque is performed by the electric machines. The damping algorithm is developed on the basis of a linearized model of the drive train and the tire-road contact by using state feedback and pole placement methods. Simulation results with a detailed multi-body system show the effectiveness of the control algorithms.

ISO 26262 is the actual standard for Functional Safety of automotive E/E (Electric/Electronic) systems. One of the challenges in the application of the standard is the distribution of safety related activities among the participants in the supply chain. In this paper, the concept of a Safety Element out of Context (SEooC) development will be analyzed showing its current problematic aspects and difficulties in implementing such an approach in a concrete typical automotive development flow with different participants (e.g. from OEM, tier 1 to semiconductor supplier) in the supply chain. The discussed aspects focus on the functional safety requirements of generic hardware and software development across the supply chain where the final integration of the developed element is not known at design time and therefore an assumption based mechanism shall be used.

Both, the continuous strengthening of the exhaust emission legislation and the striving for a substantial reduction of carbon dioxide output in the traffic sector depict substantial requirements for the development of future diesel engines. These engines will comprise not only the mandatory diesel oxidation catalyst (DOC) and particulate filter DPF but a NOx aftertreatment system as well - at least for heavier vehicles. The oxidation catalysts as well as currently available NOx aftertreatment technologies, i.e., LNT and SCR, rely on sufficient exhaust gas temperatures to achieve a proper conversion. This is getting more and more critical due to the fact that today's and future measures for CO₂ reduction will result in further decrease of engine-out temperatures. Additionally this development has to be considered in the light of further engine electrification and hybridization scenarios.