How to Cite
Abstract
Deep learning is a non-parametric approach suitable for solving prediction problems. It can be used in different areas employing the autoencoders and the recurrent architecture, and its capability of learning features without dimensionality reduction, and assessing the temporal patterns of the outputs, respectively. Deep recurrent networks use sequences and feedback loops to model the temporality of the outputs, so they are suitable for processing time series data with long dependencies and modeling a highly complex input-output mapping. Deep learning has primarily been used as a black-box model due to its high flexibility and performance, such that complex input-to-output mappings can be created without in-depth knowledge of the data. However, in practice, a purely black-box prediction often leads the data to be exploited in a faulty fashion. This usually results in the quality of the paths generated and thus the performance of the autonomous vehicle regarding safety and efficiency being hindered.
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