Search Results

In this paper, we propose algorithms for cost minimization of physical wires that are used to connect electronic devices in the vehicle. The wiring cost is one of the most important drivers of electrical architecture selection. Our algorithms perform wire routing from a source device to a destination device through harnesses, by selecting the optimized wire size. In addition, we provide optimized splice allocation with limited constraints. Based on the algorithms, we develop a tool which is integrated into an off-the-shelf optimization and workflow system-level design tool. The algorithms and the tool provide an efficient, flexible, scalable, and maintainable approach for cost analysis and architecture selection.

To provide a feasible transitional solution from all-by-human driving style to fully autonomous driving style, this paper proposed concept and its control algorithm of a robust lane-keeping ‘co-pilot’ system. In this a semi-autonomous system, Learning based Model Predictive Control (LBMPC) theory is employed to improve system's performance in target state tracking accuracy and controller's robustness. Firstly, an approximate LTI model which describes driver-vehicle-road closed-loop system is set up and real system's deviations from the LTI system resulted by uncertainties in the model are regarded as bounded disturbance. The LTI model and bounded disturbances make up a nominal model. Secondly, a time-varying model which is composed of LTI model and an ‘oracle’ component is designed to observe the possible disturbances numerically and it is online updated using Extended Kalman Filter (EKF).

Heavy trucks are produced with a great variety of vehicle configurations, operate over a wide range of gross vehicle weight and sometimes function in extreme duty environments. Frontal crashes of heavy trucks can pose a threat to truck occupants when the vehicle strikes another large object such as bridge works, large natural features or another heavy-duty vehicle. Investigations of heavy truck frontal crashes indicate that the factors listed above all affect the outcome for the driver and the resulting damage to the truck Recently, a new chassis was introduced for on-highway heavy truck models that feature frontal airbag occupant protection. This introduction presented an opportunity to incorporate the knowledge gained from crash investigation into the process for developing the crash sensor's parameter settings.

Accident statistics show that rollover accidents contribute to a large proportion of fatal traffic accidents in the U.S.. In the past it has been documented that some light passenger cars showed tendencies to roll over in evasive lane change maneuvers. In 1997, a newly developed mini van rolled over in a severe double lane change test called “moose-test”. Recently (2001), a new SUV showed similar tendencies in the Consumers Union Short Course test. It is not immediately clear why these evasive test maneuvers are so strongly related to untripped rollover of light passenger vehicles. Therefore, the goal of current research is to understand the circumstances and effects causing modern passenger vehicles to roll over in evasive maneuvers on the road. This paper discusses research activities concerning the following questions: How do critical steering strategies lead to untripped rollover? Are resonant frequencies excited during maneuvers leading to rollover?

The following document offers insight into the work of the MOST Cooperation. Now that MOST is on the road, a short overview of five years of successful collaborative work of the partners involved and the results achieved will be given. Emphasis is put on the importance of a shared vision in combination with shared values as a prerequisite for targeted collaborative work. It is also about additional key success factors that led to the success of the MOST Cooperation. Your attention will be directed to the way the MOST Cooperation sets and achieves its goals. And you will learn about how the organization was set-up to support a fast progression towards the common goal. The document concludes with examples of recent work as well as an outlook on future work.

Development times in the automotive industry are becoming increasingly shorter. For this reason, design decisions based on simulation results must be made at an early development stage. The dynamic simulation of an automotive refrigeration cycle with Dymola/Modelica as part of the design process will be described in the following paper. The component supplier's expertise as well as the automotive manufacturer's knowledge of vehicle parameters in one simulation platform will also be discussed.

Combatting the modification of automotive control systems is a current and future challenge for OEMs and suppliers. ‘Chip-tuning’ is a manifestation of manipulation of a vehicle's original setup and calibration. With the increase in automotive functions implemented in software and corresponding business models, chip tuning will become a major concern. Recognizing and reporting of tuned control units in a vehicle is required for technical as well as legal reasons. This work approaches the problem by capturing the behavior of relevant control units within a machine learning system called a recognition module. The recognition module continuously monitors vehicle's sensor data. It comprises a set of classifiers that have been trained on the intended behavior of a control unit before the vehicle is delivered. When the vehicle is on the road, the recognition module uses the classifier together with current data to ascertain that the behavior of the vehicle is as intended.