Analytische und experimentelle Untersuchung der Mensch-Maschine-Schnittstellen von Pkw-Bremsanlagen

Moderne Pkw zeichnen sich durch einen hohen technischen Stand aus und sind für den Kunden technologisch immer weniger differenzierbar. Deshalb sind Eigenschaften, die in der unmittelbaren Interaktion mit dem Fahrzeug erlebt werden, bedeutende Differenzierungsmerkmale. Der Betriebsbremse kommt hierbei besondere Bedeutung zu, weil sie den Fahrprozess und damit die aktive Fahrzeugsicherheit unmittelbar betrifft. Da es der Fahrzeugforschung bisher nicht gelungen ist, zuverlässige Methoden zur zielgerichteten Entwicklung des „Bremspedalgefühls“ bereitzustellen, wird die kritische Auseinandersetzung mit der bisherigen Methodik und den erarbeiteten Ergebnissen zunehmend gefordert. Die vorliegende Arbeit greift diese Forderung auf und verfolgt sie systematisch. Zunächst wird der veröffentlichte Stand kritisch analysiert. Nach der grundsätzlichen Auseinandersetzung mit der mechanischen Mensch-Maschine-Interaktion folgt die Charakterisierung der Teilsysteme „Fahrer“ und „Fahrzeug“ sowie der Fahrer- Fahrzeug-Interaktion beim Abbremsen. Auf dieser Grundlage werden drei Schwerpunkte verfolgt. Zunächst wird eine objektive Methode zur Beschreibung der Pedalund Bremscharakteristik moderner Mittelklasse-Pkw entwickelt. Resultat sind u. a. eine Pedalbetätigungsautomatik zur experimentellen Identifikation sowie ein parametrisches Modell, das die Pedal- und Bremscharakteristik bei Komfortbremsungen umfassend beschreibt. Den zweiten Schwerpunkt bilden Subjektiv-Objektiv- Untersuchungen. Eingesetzt wird das Forschungsfahrzeug PEGASYS, dessen besonderes Merkmal die Fähigkeit zur modellbasierten Veränderung der Pedal- und Bremscharakteristik ist. Nach der Beschreibung wesentlicher Anforderungen an eine authentische Haptiksimulation und deren technische Umsetzung in PEGASYS werden Probandenfahrversuche vorgestellt, die Auskunft über das Betätigungsverhalten des Normalfahrers, die subjektive Wirkung objektiver Parameter und das Potenzial neuartiger Konzepte für die Pedal- und Bremscharakteristik geben. Aus den Ergebnissen werden Gestaltungsrichtlinien für komfortoptimale Pedal- und Bremscharakteristiken abgeleitet. Der dritte Schwerpunkt ist schließlich die Benennung konstruktiver Einflussgrößen auf die Pedal- und Bremscharakteristik. Zu diesem Zweck wird ein detailliertes physikalisches Modell der Bremsanlage eines Mittelklasse-Pkw entwickelt. Anhand von Messungen und Simulationsrechnungen werden die Merkmale der Schnittstellencharakteristik bis zur Einzelkomponente zurückverfolgt. Die Arbeit schließt mit Vorschlägen für Schwerpunkte zukünftiger Forschungstätigkeit zur Mensch-Maschine-Interaktion beim Abbremsen.Investigation of the human-machine interface in the case of car braking systems, by analysis and experiment Modern cars are always highly technological and it is ever more difficult for the customer to tell the types of technology apart. The qualities that are experienced by the driver in direct interaction with the car mechanics have thus come to be significant distinguishing features. The brake system is particularly important because it belongs to the essence of driving and of keeping the vehicle under control. Because automotive research has failed so far to find reliable ways of developing the technology of a particular braking sensation, there is a need to view previous methodology and its outcomes with a critical eye. The present work rises to the challenge in a systematic manner. There is first a critical analysis of the status quo in published research. After discussion of the mechanical issues in the driver-vehicle interaction when the brakes are applied, “driver” and “vehicle” are presented as elements of a system in which these two elements interact during braking. There are then three foci. First, a means of objective representation of the brake and pedal characteristic for a middle-sized family car is developed. Results are a brake pedal robot to help with experimental characterisation and a parametric model which constitutes a full description regarding normal traffic conditions. Secondly, there are investigations focussing on both the subjective and the objective. The PEGASYS research vehicle is employed. It is capable of being modified to give different (model-based) varieties of brake and pedal feel. The challenges to the production of an authentic simulation of the feel, and how they were overcome in the PEGASYS vehicle, are described. There follows an account of drive tests which provide information on drivers’ normal braking behaviour and the subjective effect of certain objective parameters, and on what might have innovative design potential as far as the pedal and braking characteristics are concerned. From the results, guidelines are derived by which braking and pedal feel characteristics which are associated with optimum comfort may be configured. Thirdly, the dimensions which will at the design stage be vital to the eventual brake and pedal feel are listed. A detailed physical model of the brake system in a middlesized car is developed for the purpose. From actual measurements and calculations from simulation, the cause of the feel at the human-machine interface is traced to the individual car parts or components. Finally, suggestions are made as to potentially productive future research on human-machine interaction during braking

Estimating Fatigue from Predetermined Speech Samples Transmitted by Operator Communication Systems

We present an estimation of fatigue level within individual operators using voice analysis. One advantage of voice analysis is its utilization of already existing operator communications hardware (2-way radio). From the driver viewpoint it’s an unobtrusive, non-interfering, secondary task. The expected fatigue induced speech changes refer to the voice categories of intensity, rhythm, pause patterns, intonation, speech rate, articulation, and speech quality. Due to inter-individual differences in speech pattern we recorded speaker dependent baselines under alert conditions. Furthermore, sophisticated classification tools (e.g. Support Vector Machine, Multi-Layer Perceptron) were applied to distinguish these different fatigue clusters. To validate the voice analysis predetermined speech samples gained from a driving simulator based sleep deprivation study (N=12; 01.00-08.00 a.m.) are used. Using standard acoustic feature computation procedures we selected 1748 features and fed them into 8 machine learning methods. After each combining the output of each single classifier we yielded a recognition rate of 83.8% in classifying slight from strong fatigue

Microsleep Episodes and Related Crashes During Overnight Driving Simulations

Microsleep (MS) episodes and related crashes were studied in an overnight driving simulation study. A new definition of MS proposed recently was applied and the mean number as well as the mean length of MS was calculated. MS occurred much more frequently than crashes. Within all pre-crash intervals (length 1 minute) the percentage of MS was calculated. Results showed that there are numerous MS episodes before every crash. The mean length of MS was between 5 and 9 seconds and did not change significantly during the night. The mean MS percentage was high within pre-crash intervals (60-80%) and is a predictor for crashes

Steering Wheel Behavior Based Estimation of Fatigue

This paper examined a steering behavior based fatigue monitoring system. The advantages of using steering behavior for detecting fatigue are that these systems measure continuously, cheaply, non-intrusively, and robustly even under extremely demanding environmental conditions. The expected fatigue induced changes in steering behavior are a pattern of slow drifting and fast corrective counter steering. Using advanced signal processing procedures for feature extraction, we computed 3 feature set in the time, frequency and state space domain (a total number of 1251 features) to capture fatigue impaired steering patterns. Each feature set was separately fed into 5 machine learning methods (e.g. Support Vector Machine, K-Nearest Neighbor). The outputs of each single classifier were combined to an ensemble classification value. Finally we combined the ensemble values of 3 feature subsets to a of meta-ensemble classification value. To validate the steering behavior analysis, driving samples are taken from a driving simulator during a sleep deprivation study (N=12). We yielded a recognition rate of 86.1% in classifying slight from strong fatigue

Driver Drowsiness Immediately before Crashes – A Comparative Investigation of EEG Pattern Recognition

Periodogram and other spectral power estimation methods are established in quantitative EEG analysis. Their outcome in case of drowsy subjects fulfilling a sustained attention task is difficult to interpret. Two novel kind of EEG analysis based on pattern recognition were proposed recently, namely the microsleep (MS) and the alpha burst (AB) pattern recognition. We compare both methods by applying them to the same experimental data and relating their output variables to two independent variables of driver drowsiness. The latter was an objective lane tracking performance variable and the first was a subjective variable of self-experienced sleepiness. Results offer remarkable differences between both EEG analysis methodologies. The expected increase with time since sleep as well as with time on task, which also exhibited in both independent variables, was not identified after applying AB recognition. The EEG immediately before fatigue related crashes contained both patterns. MS patterns were remarkably more frequent before crashes; almost every crash (98.5 %) was preceded by MS patterns, whereas less than 64 % of all crashes had AB patterns within a 10 sec pre-crash interval

The Zero-n Gap Soliton

Periodic structures consisting of alternating layers of positive index and negative index materials possess a novel band gap at the frequency at which the average refractive index is zero. We show that in the presence of a Kerr nonlinearity, this zero-n gap can switch from low transmission to a perfectly transmitting state, forming a nonlinear resonance or gap soliton in the process. This zero-n gap soliton is omnidirectional in contrast to the usual Bragg gap soliton of positive index periodic structure

Asymmetric Properties of Heart Rate Variability to Assess Operator Fatigue

The aim of this study is to evaluate the suitability of heart rate recordings for establishing a reliable connection to well-defined fatigue and performance measures in order to estimate fatigue in industrial and transportation applications. An overnight driving simulation scenario with partial sleep deprivation was utilized to induce strong fatigue. An experiment trial was divided into repeated sessions, each of which consisted of a driving performance and two vigilance tasks. Heart rate (HR) was recorded over the entire experiment; HRmeasures were derived and correlated against measures that were established from driving and vigilance task performance and that represent various aspects of operator fatigue. In a previous report (Hefner et al. 2009) we presented on the basis of the data of one volunteer that multiple fatigue measures correlate well with different expressions of heart rate variability (HRV), especially with longterm HRV derived from Poincaré plots. In this work, we intensify the Poincaré analysis by dividing the distribution of HR data in different accelerating and decelerating segments and by establishing properties of asymmetry between these segments. We also show that most of the properties of long-term HRV correlate well with specific fatigue measures for a group of 5 volunteers despite their large inter-individual differences in HR-to-fatigue correlations

Technologies for the Monitoring and Prevention of Driver Fatigue

A series of driving simulation pilot studies on various technologies for alertness monitoring (head position sensor, eye-gaze system), fitness-for-duty testing (two pupil-based systems), and alertness promotion (in-seat vibration system) has been conducted in Circadian Technologies’ Alertness Testbed. The results indicate that, all tested technologies show promise for monitoring/testing or preventing driver fatigue, respectively. However, particularly for fatigue monitoring, no single measure alone may be sensitive and reliable enough to quantify driver fatigue. Since alertness is a complex phenomenon, a multi-parametric approach needs to be used. Such a multi-sensor approach imposes challenges for online data interpretation. We suggest using a neural-fuzzy hybrid system for the automatic assessment of complex data streams for driver fatigue. The final system output can then be used to trigger the activation of alertness countermeasures

A Measure of Strong Driver Fatigue

Strong fatigue during sustained operations is difficult to quantify because of its complex nature and large inter-individual differences. The most evident and unambiguous sign is the occurrence of microsleep (MS) events. We aimed at detecting MS utilizing computational intelligence methods. Our analysis was based on biosignal and video recordings of 10 healthy young adults who completed 14 sessions over two nights in our real-car driving simulation lab. Visual scoring by trained raters led to 2,290 examples of MS. Only evident events accompanied by prolonged eyelid closures, roving eye movements, head noddings, major driving incidents, and drift-out-of-lane accidents were regarded as MS. All other cases with signs of fatigue were regarded as dubious. The same amount of counterexamples (Non-MS) where continued driving was still possible were picked out from the recordings. Non-MS and MS examples covered only 15% of the whole time. Support-Vector Machines were utilized as classifiers and were adapted to these two classes of examples. If such classifiers were applied consecutively, then 100% of time is covered. Validation analysis demonstrated that the classifier gained high selectivity and high specificity. Based on this complete coverage, the percentage of MS in a predefined time span can be calculated. This measure was highly correlated to deteriorations in driving performance and to subjective self-ratings of sleepiness. We conclude that reliable detection of MS is possible despite large intra- and inter-individual differences in behaviour and in biosignal characteristics. Therefore, the percentage of detected MS gives an objective measure of strong driver fatigue