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Current developments in automotive industry such as hybrid powertrains and the continuously increasing demands on emission control systems, are pushing complexity still further. Validation of such systems lead to a huge amount of test cases and hence extreme testing efforts on the road. At the same time the pressure to reduce costs and minimize development time is creating challenging boundaries on development teams. Therefore, it is of utmost importance to utilize testing and validation prototypes in the most efficient way. It is necessary to apply high levels of instrumentation and collect as much data as possible. And a streamlined data pipeline allows the fleet managers to get new insights from the raw data and control the validation vehicles as well as the development team in the most efficient way. In this paper we will demonstrate a data-driven approach for validation testing.

The European emission legislation for two-wheeler vehicles driven by engines of ≤ 50 cm₃ is continuously developing. One of the most important issues in the near future will be the finalization of the European Commission's proposals for future steps in the emissions regulations as well as the verification of the impacts of current standards on the market. To have a basis for the discussion about these topics, the Association for Emissions Control by Catalyst (AECC) with the Institute for Internal Combustion Engines and Thermodynamics of Graz University of Technology (IVT) carried out an extensive test program to show the actual emission situation of state-of-the-art mopeds including mass and number of particulate matter as well as unregulated gaseous components. One of the main goals of these tests was to measure exhaust emissions without any modifications to the engines of standard production vehicles available on the European market.

During recent years the accident simulation program PC-Crash was developed. This software simulates vehicle movement before, during and after the impact, using 3D vehicle and scene models. When reconstructing car accidents, quite often questions arise regarding occupant movement and loading. Especially important is the influence of different types of restraint systems on the occupant. MADYMO® is a software tool which was developed by TNO in the Netherlands and which is well known in the automotive industry for the simulation of occupant movement. It allows the simulation of all kinds of modern restraint systems such as airbags and seatbelts with and without pretensioners. As the software is used in the automotive industry quite extensively, a huge validated database of dummy and human models is available. Since MADYMO® demands the setup of quite complicated input files, its use normally requires a high level of expertise.

With the increased number of variants, the preservation of a brand-specific vehicle DNA becomes more and more important. Paired with growing customer expectations, brand DNA can be a crucial point in the decision-making process of buying a new vehicle. Whereas the customer will assess the DNA subjectively during driving by evaluating the vehicle drive quality (“driveability”), most manufacturers are not merely relying on subjective evaluations by having test drivers perform maneuvers with prototype vehicles. Nowadays, the assessment is performed objectively during the vehicle development process. As a supporting measure, the Anstalt für Verbrennungskraftmaschinen List (AVL) has made the objective assessment tool AVL-DRIVE commercially available. Up to now, the AVL-DRIVE ratings had to be manually analyzed and checked for outliers. Low ratings and high deviations to a priori specified target values are a good starting point for the search of outliers.

Car driver’s behavior and its influence on driving characteristics play an increasing role in the development of modern vehicles, e.g. in view of efficient powertrain control and implementation of driving assistance functions. In addition, knowledge about actual driving style can provide feedback to the driver and support efficient driving or even safety-related measures. Driving patterns are caused not only by the driver, but also influenced by road characteristics, environmental boundary conditions and other traffic participants. Thus, it is necessary to take the driving circumstances into account, when driving patterns are studied. This work proposes a methodology to cluster and evaluate driving patterns under consideration of vehicle-related parameters (e.g. acceleration and jerk) in combination with additional influencing factors, e.g. road style and inclination. Firstly, segmentation of the trip in distance series is performed to generate micro cycles.

The latest legislation requires a dramatic reduction of motor vehicle exhaust emission. This is also a big challenge for emission measurement instrumentation, because of almost zero concentrations of certain components in the exhaust. For current measurement devices, which are recommended by the legislation, it is almost impossible to determine such low emission levels with adequate accuracy. The paper describes a new Constant Volume Sampling (CVS) system with reduced dilution, warming and quick flow rate changing capability. Possible solutions are discussed and the properties of data measured with test facilities which are prepared to cover S-ULEV and EURO IV applications are described. Also the selection of used materials is of rising importance. The tests were performed on a dynamic engine test bed which was equipped with such a CVS system and with emission analyzing systems for raw exhaust and diluted measurements.

Due to current progresses in the field of driver assistance systems and the continuously growing electrification of vehicle drive trains, the evaluation of driver behavior has become an important part in the development process of modern cars. Findings from driver analyses are used for the creation of individual profiles, which can be permanently adapted due to ongoing data processing. A benefit of data-based dynamic control systems lies in the possibility to individually configure the vehicle behavior for a specific driver, which can contribute to increasing customer acceptance and satisfaction. In this way, an optimization of the control behavior between driver and vehicle and the resulting mutual system learning and -adjustment hold great potential for improvements in driving behavior, safety and energy consumption.

The latest legislation requires the automotive industry to once again reduce the emission levels of their latest vehicles. This leads to a new challenge in the field of emission measurement, because the concentrations of certain components of the exhaust gases are extremely low. For current measurement devices, which are recommended by the legislation, it is almost impossible to determine such low emission levels with the necessary accuracy. This study evaluates the features of an improved CVS system (Constant Volume Sampling) with the possibility of heating and the ability of changing flow rates quickly. Possible solutions are discussed and the properties of data measured with test facilities which are prepared to cover S-ULEV and EURO IV applications are described. The tests were performed on a dynamic engine test bed which was equipped with such a CVS system and with emission analyzing systems for raw exhaust and diluted measurements.

In the next 20 years the share of small electric vehicles (SEVs) will increase especially in urban areas. SEVs show distinctive design differences compared to traditional vehicles. Thus the consequences of impacts of SEVs with vulnerable road users (VRUs) and other vehicles will be different from traditional collisions. No assessment concerning vehicle safety is defined for vehicles within European L7e category currently. Focus of the elaborated methodology is to define appropriate test scenarios for this vehicle category to be used within a virtual tool chain. A virtual tool chain has to be defined for the realization of a guideline of virtual certification. The derivation and development of new test conditions for SEVs are described and are the main focus of this work. As key methodology a prospective methodical analysis under consideration of future aspects like pre-crash safety systems is applied.

As a result of the ever-increasing application of cyber-physical components in the automotive industry, cybersecurity has become an urgent topic. Adapting technologies and communication protocols like Ethernet and WiFi in connected vehicles yields many attack scenarios. Consequently, ISO/SAE 21434 and UN R155 (2021) define a standard and regulatory framework for automotive cybersecurity, Both documents follow a risk management-based approach and require a threat modeling methodology for risk analysis and identification. Such a threat modeling methodology must conform to the Threat Analysis and Risk Assessment (TARA) framework of ISO/SAE 21434. Conversely, existing threat modeling methods enumerate isolated threats disregarding the vehicle’s design and connections. Consequently, they neglect the role of attack paths from a vehicle’s interfaces to its assets.

In an era characterized by the rapid proliferation and advancement of AI-based technologies across various domains, the spotlight is placed on the integration of these technologies into trustworthy autonomous systems. The integration into embedded systems necessitates a heightened focus on dependability. This paper combines the findings from the TEACHING project, which delves into the foundations of humanistic AI concepts, with insights derived from an expert workshop in the field of dependability engineering. We establish the body of knowledge and key findings deliberated upon during an expert workshop held at an international conference focused on computer safety, reliability and security. The dialogue makes it evident that despite advancements, the assurance of dependability in AI-driven systems remains an unresolved challenge, lacking a one-size-fits-all solution.

Vehicle driveability evolves more and more as a key decisive factor for marketability and competitiveness of passenger cars, since the final decision of customers to buy a car is usually taken after a more or less intensive test drive. Car manufacturers currently evaluate vehicle driveability with subjective assessments and by having their experienced test drivers fill out form sheets. These assessments are time and cost intensive, limited in repeatability and not objective. The real customer requirements cannot be recognized in detail with this method. This paper describes a completely new approach for an objective and real time evaluation of relevant driveability criteria, for use in a vehicle and on a high dynamic test bed. The vehicle application enables an objective comparison between vehicles and an application as a development tool in many development and calibration phases, where ever fast and objective driveability results are required.

The reduction of environmentally harmful gases and the ambitions to reduce the exploitation of fossil resources lead to stricter legislation for all mobile sources. Legislative development significantly affected improvements in emissions and fuel consumptions over the last years, mainly measured under laboratory conditions. But real world operating scenarios have a major influence on emissions and it is already well known that these values considerably differ from officially published figures [1]. There are regulated emissions by the European Commission by means of real driving scenarios for passenger cars. A methodology to measure real drive emissions RDE is therefore well approved for automotive applications but was not adapted for two-wheeler-applications yet [2]. Hence measurements have been performed on-road and on chassis dynamometer for motorcycles with the state of the art RDE measurement equipment to be prepared for possible future legislation.

Recently, a particle number (PN) limit was introduced in the European light-duty vehicles legislation. The legislation requires measurement of PN, and particulate mass (PM), from the full dilution tunnel with constant volume sampling (CVS). Furthermore, PN measurements will be introduced in the next stage of the European Heavy-Duty regulation. Heavy-duty engine certification can be done either from the CVS or from a partial flow dilution system (PFDS). For research and development purposes, though, measurements are often conducted from the raw exhaust, thereby avoiding the high installation costs of CVS and PFDS. Although for legislative measurements requirements exist regarding sampling and transport of the aerosol sample, such requirements do not necessarily apply for raw exhaust measurements. Thus, measurement differences are often observed depending on where in the experimental set up sampling occurs.

The future exhaust emission legislation limits and the procedures for running the test cycles will have an important influence on future range extender concepts. Due to the special steady state operation strategy of the range extender engines, it is possible to create a simple methodology for comparing engine test bench emissions with the emission limits of exhaust gas legislations. Therefore the energy demand of a predefined vehicle was simulated with PHEM, a longitudinal dynamic simulation tool. According to that, the influence of different exhaust gas after treatment systems and preheating options on the tolerated raw emission concentration will be analyzed. With this information, a few chosen range extender engine concepts will be compared concerning their suitability for future exhaust emission legislations. The selection of the range extender concepts was carried out with the methotology of a value benefit analysis.

Advanced Driver Assistance Systems (ADAS) for collision avoidance/mitigation have already demonstrated their benefit on vehicle safety. Often those systems have an additional functionality for comfort to assist the driver in non-critical driving. The verification of ADAS functionality using different test scenarios is currently investigated in many different projects worldwide. A harmonization of test scenarios and evaluation criteria is not yet accomplished. Often, these test scenarios focus on objective collision avoidance and not on the subjective interaction between driver and vehicle. The present study deals with the development of an experimental validation plan for the systems Automatic Cruise Control (ACC), Lane Departure Warning (LDW) and Lane Keeping Assist (LKA). Standardized driving maneuvers with two or more vehicles equipped with synchronized measurement are performed by professional test drivers.

The reduction of CO₂ emissions represents a major goal of governments worldwide. In developed countries, approximately 20% of the CO₂ emissions originate from transport, one third of this from commercial vehicles. CO₂ emission legislation is in place for passenger cars in a number of major markets. For commercial vehicles such legislation was also already partly published or is under discussion. Furthermore the commercial vehicles market is very cost sensitive. Thus the major share of fuel cost in the total cost of ownership of commercial vehicles was already in the past a major driver for the development of efficient drivetrain solutions. These aspects make the use of new powertrain technologies, specifically hybridization, mandatory for future commercial powertrains. While some technologies offer a greater potential for CO₂ reduction than others, they might not represent the overall optimum with regard to the total cost of ownership.

The safety of BEVs in driving, charging and parking condition is essential for the success of electrification in automotive industry as well as key driver of any future development of Li-Ion HV battery. AVL has developed a unique simulation approach in which the multi-physical behavior of the single cell in thermal runaway is modelled and applied to module, pack or vehicle level. In addition and beside this cell behavior, various more physical phenomena during thermal propagation on pack level are considered and predicted by the simulation method: component melting, ignition and flammibilty of venting gas and HV failures.

A large number of testing procedures have been developed to ensure vehicle safety in common and extreme driving situations. However, these conventional testing procedures are insufficient for testing autonomous vehicles. They have to handle unexpected scenarios with the same or less risk a human driver would take. Currently, safety related systems are not adequately tested, e.g. in collision avoidance scenarios with pedestrians. Examples are the change of pedestrian behaviour caused by interaction, environmental influences and personal aspects, which cannot be tested in real environments. It is proposed to use augmented reality techniques. This method can be seen as a new (Augmented) Pedestrian in the Loop testing procedure.