Search Results

When employing in-car active sound generation (ASG) and active noise cancellation (ANC), the accurate knowledge of the vehicle interior sound pressure distribution in magnitude as well as phase is paramount. Revisiting the ANC concept, relevant boundary conditions in spatial sound fields will be addressed. Moreover, within this study the controllability and observability requirements in case of ASG and ANC were examined in detail. This investigation focuses on sound pressure measurements using a 24 channel microphone array at different heights near the head of the driver. A shaker at the firewall and four loudspeakers of an ordinary in-car sound system have been investigated in order to compare their sound fields. Measurements have been done for different numbers of passengers, with and without a dummy head and real person on the driver seat. Transfer functions have been determined with a log-swept sine technique.

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.

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 paper describes a new methodology for a closed loop driving style detection, a vehicle driveability character evaluation and a control unit for an adaptation of the vehicle character according to the driving style. During driving the vehicle character is adapted to the driver, using the potential of modern torque based drive by wire engine control systems of gasoline and diesel engines. The methodology leads to a completely new human - vehicle interaction, the driver creates his own unique vehicle character. The vehicle owner is able to form a mass produced vehicle according to his demands. A typical drawback of globalisation, a loss of identification between owner and product can be avoided by the presented methodology. The basic structure of the evaluation and control strategies are shown as well as objective and subjective results of increased driving pleasure and higher driver identification due to increased sportiness and spontaneity up to 100%.

Future demands regarding emissions, fuel consumption and driveability lead to complex engine and power train control systems. The calibration of the increasing number of free parameters in the ECU's contradicts the demand for reduced time in the power train development cycle. This paper will focus on the automatic, unmanned closed loop optimization of driveability quality on a high dynamic engine test bed. The collaboration of three advanced methods will be presented: Objective real time driveability assessment, to predict the expected feelings of the buyers of the car Automatic computer assisted variation of ECU parameters on the basis of statistical methods like design of experiments (DoE). Thus data are measured in an automated process allowing an optimization based on models (e.g. neural networks).

Apart of other aspects, the interior sound of a passenger car brand has to meet customer expectations. For optimizing the sound of a passenger car, target sounds have first to be established via the operating range of the vehicle. For an effective sound engineering approach an objective description and evaluation of vehicle interior sound is beneficial. Such an objective description guarantees the effective and reproducible implementation of the required brand sound in the vehicle development process. In such a process it is necessary to reduce on the one hand annoying undesired noise aspects and to create on the other hand the relevant and necessary noise parameters to meet the target sounds head on.

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.

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.

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.

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.

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.

Modern safety and comfort features must behave country specific to the local environment and traffic conditions in order to gain end consumers’ trust and strengthening OEMs market success respectively. In order to achieve this, a new methodology was developed. In this paper, the approach for designing advanced driving assistance systems (ADAS) with a tailored controller behavior optimized for country specific market expectations like in India is described. Furthermore, the definition of objective performance and calibration targets with automated evaluation of target fulfillment will be deeply discussed. The method is focused on saving time at calibration and validation without compromising the quality of ADAS features. Local market specific driving behavior is investigated and measurement data from real-world driving collected. Data clustering via maneuver detection is performed automatically, which is saving time and effort.

India striving for carbon neutrality influences futures powertrain architecture of commercial vehicles. The use of CO2-free drives as battery electric have been demonstrated for various applications. The productivity still is a challenge due to missing high power charging infrastructure or limited range. This draws the attention to the use of sustainable fuels due to lower refueling times. The hydrogen engine got highest attention in the last couple of years. For markets as the EU the driver for hydrogen is the CO2 emission reduction, whereas for markets as India hydrogen offers the additional opportunity for more independence from fossil imports. Different OEMs all over the world have converted diesel engines to hydrogen operation with strong focus on performance and emission demonstration, so far with limited technology readiness of different key components.