In this paper, we propose a real-time vision-based filtering algorithm for steering angle estimation in autonomous driving. A novel scene-based particle filtering algorithm is used to estimate and track the steering angle using images obtained from a monocular camera. Highly accurate proposal distributions and likelihood are modeled for the second order particle filter, at the scene-level, using deep learning. For every road scene, an individual proposal distribution and likelihood model is learnt for the corresponding particle filter. The proposal distribution is modeled using a novel long short term memory network-mixture-of-expert-based regression framework. To facilitate the learning of highly accurate proposal distributions, each road scene is partitioned into straight driving, left turning and right turning sub-partitions. Subsequently, each expert in the regression framework accurately model the expert driver's behavior within a specific partition of the given road scene. Owing to the accuracy of the modelled proposal distributions, the steering angle is robustly tracked, even with a limited number of sampled particles. The sampled particles are assigned importance weights using a deep learning-based likelihood. The likelihood is modeled with a convolutional neural network and extra trees-based regression framework, which predicts the steering angle for a given image. We validate our proposed algorithm using multiple sequences. We perform a detailed parameter analysis and a comparative analysis of our proposed algorithm with different baseline algorithms. Experimental results show that the proposed algorithm can robustly track the steering angles with few particles in real-time even for challenging scenes.


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    Titel :

    Automated driving by monocular camera using deep mixture of experts


    Beteiligte:
    John, V. (Autor:in) / Mita, S. (Autor:in) / Tehrani, H. (Autor:in) / Ishimaru, K. (Autor:in)


    Erscheinungsdatum :

    01.06.2017


    Format / Umfang :

    1837908 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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