The detection of previously unseen, unexpected obstacles on the road is a major challenge for automated driving systems. Different from the detection of ordinary objects with pre-definable classes, detecting unexpected obstacles on the road cannot be resolved by upscaling the sensor technology alone (e.g., high resolution video imagers / radar antennas, denser LiDAR scan lines). This is due to the fact, that there is a wide variety in the types of unexpected obstacles that also do not share a common appearance (e.g., lost cargo as a suitcase or bicycle, tire fragments, a tree stem). Also adding object classes or adding "all" of these objects to a common "unexpected obstacle" class does not scale. In this contribution, we study the feasibility of using a deep learning video-based lane corridor (called "AI ego-corridor") to ease the challenge by inverting the problem: Instead of detecting a previously unseen object, the AI ego-corridor detects that the ego-lane ahead ends. A smart ground-truth definition enables an easy feature-based classification of an abrupt end of the ego-lane. We propose two neural network designs and research among other things the potential of training with synthetic data. We evaluate our approach on a test vehicle platform. It is shown that the approach is able to detect numerous previously unseen obstacles at a distance of up to 300 m with a detection rate of 95 %.


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    Titel :

    Mono Video-Based AI Corridor for Model-Free Detection of Collision-Relevant Obstacles


    Beteiligte:
    Michalke, Thomas (Autor:in) / Kaddar, Yassin (Autor:in) / Nurnberg, Thomas (Autor:in) / Kastner, Linh (Autor:in) / Lambrecht, Jens (Autor:in)


    Erscheinungsdatum :

    2023-06-04


    Format / Umfang :

    6282398 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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