RECENT STUDIES OF COMPARATIVE PHILOSOPHY IN THE BALKANS: INTERVIEW WITH JANA S. ROŠKER

ADAS&M

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving

Images in mind – Design metaphor and method to classify driver distraction in critical situations

The paper presents a driver model which classifies visual distraction based on the detection of atypical driving behavior. The model forwards the information to a driver adaptive collision mitigation system (CMS) and activates the acoustic warning earlier in case of distraction. Therefore the model requires the knowledge of the normal driving behavior. For that reason we introduce a design metaphor. We use the human memory and its ability to build up mental representations. Based on the idea to interpret multivariate time series as gray level images we adapted the concept of mental images to learn a situation based normal driving behavior. The model transfers the property of the long term memory to store, to interfere and to forget prototypes of mental images. We compare the stored prototypical image with the current image to obtain a distraction index. If the index reaches a certain threshold value the acoustic warning is presented

CONFORM – A visualization tool and method to classify driving styles in context of highly automated driving

The paper introduces the method and tool CONFORM (Conflict regocnition by image processing methods). CONFORM will be integrated as a driver model into the ibeo test vehicle during the project phase of EU–project HoliDes. The aim of CONFORM is to support the system designer to properly parameterize the default behavior of a highly automated vehicle to guarantee a high system acceptance. Thereby CONFORM addresses intra and inter individual differences in the driving behavior. CONFORM measures the difference between the default system behavior and the natural driving behavior of a human driver situation-dependent to determine the necessity of an adaptation. Based on a driving simulator study the paper describes how CONFORM is able to visualize and to cluster certain driving patterns/styles in a vehicle following/vehicle approaching scenario. We use the study results to derive recommendations for the design of the system behavior of highly automated vehicles

Should my vehicle drive as I do? A methodology to determine drivers‘ preference for automated driving styles.

With automated driving being on the cusp of wide spread market introduction (Rivera & van der Meulen, 2014), Human Factors aspect of design and evaluation of the exact behaviour of automated vehicles gains crucial importance. Safety margins determine an envelope of possible trajectories for the automated vehicle, but as of today, the parameterisation of the vehicle’s behaviour within that envelope to create attractive driving styles has has not received wide spread attention yet (Scherer et al., 2015). However, for acceptance of automated driving functions, deemed critical to deliver the promised reduction in accident numbers, it is essential to design automated driving styles that win end users over to activate them on public roads. In two consecutive studies we investigated how to measure preferences for automated driving styles, whether or not drivers prefer being driven similar to their own driving, or if at least default styles can be created that a majority of the users enjoy. In a first study 43 subjects drove in three scenarios on a two-lane motorway in a motion-based driving simulator. Users were instructed to drive at 120 km/h, forcing them to overtake slower vehicles on the right lane. The scenarios were varied regarding the presence and behaviour of faster cars on the left lane, compelling subjects to decide on timing and execution of overtaking manoeuvres. Based on the data from this study, four prototypical driving styles were extracted, using a multivariate time-series clustering algorithm (Griesche et al., 2014). In a second study, 35 subjects from study 1 rated the attractiveness of the prototypical driving styles gained from that study, in addition to their own style. Using a Best-Worst-Scaling technique, preferences for either one of the prototypical driving styles or their own style could be measured. The results indicate that i) many but not all subjects do like their own styles, but not in every situation, and ii) certain styles exist which are preferred by the great majority of the users

Soll mein Auto so fahren wie ich? – im Kontext des automatisierten Fahrens

Die Anpassung des Fahrstiles einer Automation an Fahrerpräferenzen gewinnt im Rahmen des hoch- und vollautomatisierten Fahrens immense Bedeutung. Wir stellen eine generische Methodik vor, wie solche Fahrstile und Präferenzen von Fahrern dafür empirisch ermittelt werden können