Browsing by Author "Friedrichs, Fabian"

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Open Access
Driver alertness monitoring using steering, lane keeping and eye tracking data under real driving conditions
(2020) Friedrichs, Fabian; Yang, Bin (Prof. Dr.-Ing.)
Since humans operate trains, vehicles, aircrafts and industrial machinery, fatigue has always been one of the major causes of accidents. Experts assert that sleepiness is among the major causes of severe road accidents. In-vehicle fatigue detection has been a research topic since the early 80’s. Most approaches are based on driving simulator studies, but do not properly work under real driving conditions. The Mercedes-Benz ATTENTION ASSIST is the ﬁrst highly sophisticated series equipment driver assistance system on the market that detects early signs of fatigue. Seven years of research and development with an unparalleled demand of resources were necessary for its series introduction in 2009 for passenger cars and 2012 for busses. The system analyzes the driving behavior and issues a warning to sleepy drivers. Essentially, this system extracts a single measure (so-called feature), the steering event rate by detecting a characteristic pattern in the steering wheel angle signal. This pattern is principally described by a steering pause followed by a sudden correction. Various challenges had to be tackled for the series-production readiness, such as handling individual driving styles and external inﬂuences from the road, trafﬁc and weather. Fuzzy logic, driving style detection, road condition detection, change of driver detection, ﬁxed-point parameter optimization and sensor surveillance were some of the side results from this thesis that were essential for the system’s maturity. Simply issuing warnings to sleepy drivers is faintly "experiencable" nor transparent. Thus, the next version 2.0 of the system was the introduction of the more vivid ATTENTION LEVEL, which is a permanently available bargraph monitoring the current driving performance. The algorithm is another result of this thesis and was introduced 2013 in the new S-Class. Fatigue is very difﬁcult to grasp since a ground truth reference does not exist. Thus, the presented ﬁndings about camera-based driver monitoring are included as fatigue reference for algorithm training. Concurrently, the presented results build the basis for eye-monitoring cameras of the future generation of such systems. The driver monitoring camera will also play a key role in "automated driving" since it is necessary to know if the driver looks to the road while the vehicle is driving and if he is alert enough to take back control over the vehicle in complex situations. All these improvements represent major steps towards the paradigm of crash free driving. In order to develop and improve the ATTENTION ASSIST, the central goal of the present work was the development of pattern detection and classiﬁcation algorithms to detect fatigue from driving sensors. One major approach to achieve a sufﬁciently high detection rate while maintaining the false alarm rate at a minimum was the incorporation of further patterns with sleepiness-associative ability. Features reported in literature were assessed as well as improved extraction techniques. Various new features were proposed for their applicability under real-road conditions. The mentioned steering pattern detection is the most important feature and was further optimized. Essential series sensor signals, available in most today’s vehicles were considered, such as steering wheel angle, lateral and longitudinal acceleration, yaw rate, wheel rotation rate, acceleration pedal, wheel suspension level, and vehicle operation. Another focus was on the lateral control using camera-based lane data. Under real driving conditions, the effects of sleepiness on the driving performance are very small and severely obscured by external inﬂuences such as road condition, curvature, cross-wind, vehicle speed, trafﬁc, steering parameters etc. Furthermore, drivers also have very different individual driving styles. Short-term distraction from vehicle operation also has a big impact on the driving behavior. Proposals are given on how to incorporate such factors. Since lane features require an optional tracking camera, a proposal is made on how to estimate some lane deviation features from only inertial sensory by means of an extended Kalman ﬁlter. Every feature is related to a number of parameters and implementation details. A highly accelerated method for parameter optimization of the large amount of data is presented and applied to the most promising features. The alpha-spindle rate from the Electroencephalogram (EEG) and Electrooculogram (EOG) were assessed for their performance under real driving conditions. In contrast to the majority of results in literature, EEG was not observed to contribute any useful information to the fatigue reference (except for two drives with microsleeps). Generally, the subjective self-assessments according to the Karolinska Sleepiness Scale and a three level warning acceptance question were consequently used. Various correlation measures and statistical test were used to assess the correlation of features with the reference. This thesis is based on a database with over 27,000 drives that accumulate to over 1.5 mio km of real-road drives. In addition, various supervised real-road driving studies were conducted that involve advanced fatigue levels. The fusion of features is performed by different classiﬁers like Artiﬁcial Neural Networks (ANN) and Support Vector Machines (SVM). Fair classiﬁcation results are achieved with ANN and SVM using cross-validation. A selection of the most potential and independent features is given based on automatic SFFS feature selection. Classical machine learning methods are used in order to yield maximal system transparency and since the algorithms are targeted to run in present control units. The potential of using end-to-end deep learning algorithms is discussed. Whereas its application to CAN-signals is problematic, there is a high potential for driver-camera based approaches. Finally, features were implemented in a real-time demonstrator using an own CAN-interface framework. While various ﬁndings are already rolled out in ATTENTION ASSIST 1.0, 2.0 and ATTENTION LEVEL, it was shown that further improvements are possible by incorporating a selection of steering- and lane-based features and sophisticated classiﬁers. The problem can only be solved on a system level considering all topics discussed in this thesis. After decades of research, it must be recognized that the limitations of indirect methods have been reached. Especially in view of emerging automated driving, direct methods like eye-tracking must be considered and have shown the greatest potential.