To ensure safety and functional correctness of automated and autonomous driving systems, virtual scenariobased testing is used. Experts derive traffic scenario types and generate instances of these types with the support of test generation tools. Since driving systems operate in a real-world environment, it is always possible to find a new scenario type as well as new instances of scenario types that are different from all other scenario types and instances. Thus, the testing process to find faulty behavior may continue forever. There is a practical need for test ending criteria for both of the following problems: Did we test all scenario types? Did we sufficiently test each type with specific instances? We address the first question and present a suitable test ending criterion and methodology. Whether the system is tested in each scenario type is reduced to the question whether all test scenarios are known. We analyze driving data to provide a statistical guarantee that all scenario types are covered. We model this as a Coupon Collector's Problem. We present experimental results for the application of this model to different driving tasks of automated and autonomous driving systems.
Test scenario generation for testing automated and autonomous driving systems requires knowledge about the recurring traffic cases, known as scenario types. The most common approach in industry is to have experts create lists of scenario types. This poses the risk both that certain types are overlooked; and that the mental model that underlies the manual process is inadequate. We propose to extract scenario types from real driving data by clustering recorded scenario instances, which are composed of timeseries. Existing works in the domain of traffic data either cannot cope with multivariate timeseries; are limited to one or two vehicles per scenario instance; or they use handcrafted features that are based on the mental model of the data scientist. The latter suffers from similar shortcomings as manual scenario type derivation. Our approach clusters scenario instances relying as little as possible on a mental model. As such, we consider the approach an important complement to manual scenario type derivation. It may yield scenario types overlooked by the experts, and it may provide a different segmentation of a whole set of scenarios instances into scenario types, thus overall increasing confidence in the handcrafted scenario types. We present the application of the approach to a real driving dataset.
Combining mid-air gestures with pen input for bi-manual input on tablets has been reported as an alternative and attractive input technique in drawing applications. Previous work has also argued that mid-air gestural input can cause discomfort and arm fatigue over time, which can be addressed in a desktop setting by allowing users to gesture in alternative restful arm positions (e.g., elbow rests on desk). However, it is unclear if and how gesture preferences and gesture designs would be different for alternative arm positions. In order to inquire these research question we report on a user and choice based gesture elicitation study in which 10 participants designed gestures for different arm positions. We provide an indepth qualitative analysis and detailed categorization of gestures, discussing commonalities and differences in the gesture sets based on a "think aloud" protocol, video recordings, and self-reports on user preferences.
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