The development of Advanced Driver Assistance Systems (ADAS) is proceeding rapidy, leading to fastly growing pools of recorded driving data for testing and validation. To make use of this data pool efficiently, a low barrier access for development engineers is of high importance, facilitating reuse of the data for function assessment or simulation. The main contribution of this work is the adoption of ideas from Natural Language Processing (NLP) to enable the semantic interpretation of driving data in their respective contexts, facilitating search and comparison in the data pool. The suggested process consists of a binning to convert the multivariate time series into discrete drive states and a subsequent transformation into a lower-dimensional space, a so-called embedding. The transformation is performed by the adaptation of Word2Vec, a model originally developed for the embedding of words in language processing tasks. The resulting drive state embedding can be used to compare driving sequences on a semantic level with the inclusion of their contexts, permitting applications like the extraction of similar sequences or the detection of anomalous events.


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    Title :

    Semantic Comparison of Driving Sequences by Adaptation of Word Embeddings


    Contributors:


    Publication date :

    2020-09-20


    Size :

    1030559 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English