Thomas Form

A generic architecture for hybrid intelligent test systems

The last decade has obviously witnessed an emerging interest in studying human intelligence aiming to solve complex problems in many industrial fields, e.g. the process of verification and validation. Therefore, in an effort to alleviate the challenges of this process, an idea to construct intelligent test systems from the viewpoints of learning, reasoning, and optimization paradigms is presented. The adopted approach is based on the ability of the test system to observe, model, and store the behavior of skilled human testers aiming to imitate their intelligence. Hence, the developed test system should be able to partially substitute the absence of the human factor during an automated test cycle, which consequently leads to a substantial reduction in the development time and cost; yet the test efficiency is not sacrificed.

A Heuristic Approach for Offboard-Diagnostics in Advanced Automotive Systems

As the complexity of current automobiles increases, new and innovative diagnostic methods for car maintenance and diagnostic inspection are greatly needed. This paper introduces a new diagnostic approach, which learns from previous repair cases with the help of neural networks in order to assist future diagnostic inspections. Practical experiments have shown that this approach is able to provide promising results even with the data that is already available today.

Agents learn from human experts: An approach to test reconfigurable systems

Faulty software is costly and possibly life threatening as software products permeate our daily life. Therefore, the test process formulates an indispensable component of the development cycle; yet it is a formidable task. In an effort to alleviate its challenges, this contribution outlines a novel approach to enrich traditional test techniques with intuition-based test strategies learned by observing skilled human testers during various test sessions. Consequently, the strategies learned would be verified, combined, and generalized to be further applied in similar test situations. Hence, a reasonable portion of the workload done by human testers would be shifted to the test system itself. This leads to a significant reduction in the development time and cost; yet the test efficiency is not sacrificed.

PEGASUS—First Steps for the Safe Introduction of Automated Driving

PEGASUS (Project for the Establishment of Generally Accepted Quality Criteria, Tools and Methods as well as Scenarios and Situations for the Release of Highly Automated Driving Functions) is a joint project funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) which seeks to close the gaps in the testing and release of automated vehicles (see Fig. 1) and supports the rapid transfer of existing functions and prototypes into series production (Fig. 2). PEGASUS intends to answer these central questions. What is the minimum performance level for an automated vehicle? How do human beings perform (as a reference value)? What can and must automation deliver (and what not)? How can it be demonstrated that the automated vehicle performs reliably? Open image in new window Fig. 1 Current state of development of highly automated driving Open image in new window Fig. 2 Advancement through PEGASUS

Automotive future - Looking beyond the powertrain

Tackling the challenges of future, cars and enclosed technologies enhance continuously. Altering society, adapted mobility, increased demand for safety and last but not least tightened energy requirements are changing our individual mobility. Future cars will have to comprise advanced driver assistance systems supporting the driver, by communicating, seeing and even feeling. Versatile driver information systems and tailored human machine interfaces will enable to incorporate the occupants. From this, cars gradually will interact more and more with environment, enabling drivers and passengers to enjoy and capitalize their ride. The presentation will give an insight into Volkswagens vision about automotive future technologies, 2020 and beyond.