Is word fragment completion a data-driven test?

23 female undergraduate students ( M = 20 yr., 10 mo., SD = 15 mo.) were given a word-fragment completion task, containing a study and nonstudy list. In the present study, the effect of orthographic familiarity (e.g., script type) of a test item on a word-fragment completion task was examined. The script types of word stimuli (Katakana and Hiragana) were manipulated between a study and test phase. Priming effect was greater when the script type was the same between a study and test phase than in the cross-script condition. Further, even if the script type of word stimulus was different between study and test phases, a significant priming effect was obtained when the test fragment was orthographically familiar. These results suggested that not only the consistency of the perceptual feature of the stimulus word between study and test phases, but also orthographic familiarity of the stimulus word in the test phase facilitated priming effect in a word-fragment completion test.

Download Full-text

Data-Driven Test Scenario Generation for Cooperative Maneuver Planning on Highways

Applied Sciences ◽

10.3390/app10228154 ◽

2020 ◽

Vol 10 (22) ◽

pp. 8154

Author(s):

Christian Knies ◽

Frank Diermeyer

Keyword(s):

Road Traffic ◽

Research Area ◽

Data Driven ◽

Highway Traffic ◽

Test Scenario ◽

Cooperative Driving ◽

Road Users ◽

Planning Algorithms ◽

Data Driven Test ◽

Test Scenarios

Future automated vehicles will have to meet the challenge of anticipating the intentions of other road users in order to plan their own behavior without compromising safety and efficiency of the surrounding road traffic. Therefore, the research area of cooperative driving deals with maneuver-planning algorithms that enable vehicles to behave cooperatively in interactive traffic scenarios. To prove the functionality of these algorithms, single test scenarios are used in the current body of literature. The use of a single, exemplary scenario bears the risk that the presented approach only works in the presented scenario and thus no general statement can be made about the performance of the algorithm. Furthermore, there is a risk that fictitious traffic scenarios may be solved which do not occur in reality. Therefore, we present a procedure for generating test scenarios based on real-world traffic datasets that require cooperation of at least one of the involved vehicles and thus are challenging from the perspective of cooperation. This procedure is applied to a large highway traffic dataset, resulting in a test scenario catalog that allows a comprehensive performance evaluation. The extracted scenarios are clustered according to the cooperative actions used to solve the respective scenario, which enables a more detailed understanding of the underlying cooperative mechanisms. In order to serve as a basis for making comparisons between different behavior planners and thus contribute to the development of future maneuver planning algorithms, a tool to extract the test scenarios from the used traffic dataset is made publicly available.

Download Full-text