Self-supervised learning for autonomous vehicles perception: A conciliation between analytical and learning methods
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This article mainly aims at motivating more investigations on self-supervised learning (SSL) perception techniques and their applications in autonomous driving. Such approaches are of broad interest as they can improve analytical methods performances, for example to perceive farther and more accurately spatially or temporally. In the meantime, they can also reduce the need of hand-labeled training data for learning methods, while offering the possibility to update the learning models into an online process. This can help an autonomous system to deal with unexpected changing conditions in the ego-vehicle environment. In all, this article firstly highlights the analytical and learning tools which may be interesting for improving or developping SSL techniques. Then, it presents the insights and correlations between existing autonomous driving perception SSL techniques, and some of their remaining limitations opening up some future research perspectives.
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