Hans-Peter Schöner

Model based Fault Detection for an Active Vehicle Suspension

Abstract An automobile fault detection and diagnosis method is described for an active vehicle suspension system. The method is based on mathematical models of the suspension system. It is shown how the unknown parameters of these mathematical models can be obtained experimentally by parameter estimation. Furthermore, the semi-physical approach of the local linear neuronal network LOLIMOT is applied for the generation of parity equations. Both, parameter estimation and parity equations are then used for model based fault detection and identification. Various faults are simulated with test rig data and it is shown, how these faults are detected.

Challenges and Approaches for Testing of Highly Automated Vehicles

Testing of highly automated vehicles has new challenges with respect to the questions to answer, the test cases, and the testing procedures. Main questions arise from the fact that highly automated vehicles are required to achieve high levels of availability and effectiveness of the vehicle functions; after all, their performance has to be compared to the performance of human drivers. Testing of such vehicles requires international consensus on the required level of safety and on the metrics to be applied. The main challenges for such testing are discussed and some new approaches are presented.

PdII: Piezo-electric motors and their applications

Abstract General properties of piezo-electric motors are explained. A short review of their historical development is given. Limiting factors for the power of piezo-electric motors are discussed in comparison to standard electromagnetic motors. Aspects for the choice of piezo-electric materials for motor applications result from these limits.

Can We Study Autonomous Driving Comfort in Moving-Base Driving Simulators? A Validation Study:

Objective: To lay the basis of studying autonomous driving comfort using driving simulators, we assessed the behavioral validity of two moving-base simulator configurations by contrasting them with a test-track setting. Background: With increasing level of automation, driving comfort becomes increasingly important. Simulators provide a safe environment to study perceived comfort in autonomous driving. To date, however, no studies were conducted in relation to comfort in autonomous driving to determine the extent to which results from simulator studies can be transferred to on-road driving conditions. Method: Participants (N = 72) experienced six differently parameterized lane-change and deceleration maneuvers and subsequently rated the comfort of each scenario. One group of participants experienced the maneuvers on a test-track setting, whereas two other groups experienced them in one of two moving-base simulator configurations. Results: We could demonstrate relative and absolute validity for one of the two simulator configurations. Subsequent analyses revealed that the validity of the simulator highly depends on the parameterization of the motion system. Conclusion: Moving-base simulation can be a useful research tool to study driving comfort in autonomous vehicles. However, our results point at a preference for subunity scaling factors for both lateral and longitudinal motion cues, which might be explained by an underestimation of speed in virtual environments. Application: In line with previous studies, we recommend lateral- and longitudinal-motion scaling factors of approximately 50% to 60% in order to obtain valid results for both active and passive driving tasks.

Simulation in development and testing of autonomous vehicles

On the first glance, autonomous vehicles seem to be just a simple continuation of the development of assistance systems which help the driver keeping the lane, holding the distance to other vehicles and avoiding accidents, with the vision of avoiding 80% of all accidents, because they are mainly caused by human errors. However, there is huge challenge with respect to the requirements on system performance and reliability for this step. As Herrtwich mentioned in [1], human drivers do quite well in driving a vehicle without accident, with statistically 7.5 million km between accidents on the German Autobahn network; if an assistance system helps a driver to avoid such accidents in (just for example) 9 out of 10 times, it does a good job by reducing the number of accidents by a factor of ten. However, autonomous vehicles with SAE level 3 or higher face the challenge to avoid or control any critical situation within a statistical distance of 75 million km between accidents, in order to achieve a similar performance compared to a level 2 (driver assisted) system. That includes many situations, which have traditionally been handled by human drivers easily, but might be difficult for automation.

Testen mit koordinierten automatisierten Fahrzeugen

Fahrerassistenzsysteme unterstutzen den Fahrer einerseits auf langen Fahrten bei Routineaufgaben, sie helfen dem Fahrer aber auch, in kritischen Situationen rechtzeitig und richtig zu reagieren. Die Assistenzsysteme der neuesten Generation reagieren sogar selbststandig, wenn der Fahrer vor einem absehbar unvermeidbaren Unfall nicht rechtzeitig reagiert. Dazu mussen die Systeme komplexe Verkehrssituationen beherrschen und Unfallsituationen von unkritischen Konstellationen unterscheiden – dies ist auch eine Herausforderung an die Pruftechnik, mit der solche Systeme abgesichert werden. In der Daimler-Forschung ist eine Prufmethodik entwickelt worden, mit der Assistenzsysteme prazise, reproduzierbar und sicher erprobt werden konnen.