Self-Supervised Learning for Autonomous Vehicles Perception: A Conciliation Between Analytical and Learning Methods

@article{Chiaroni2021SelfSupervisedLF,
  title={Self-Supervised Learning for Autonomous Vehicles Perception: A Conciliation Between Analytical and Learning Methods},
  author={Florent Chiaroni and Mohamed-Cherif Rahal and Nicolas Hueber and Fr{\'e}d{\'e}ric Dufaux},
  journal={IEEE Signal Processing Magazine},
  year={2021},
  volume={38},
  pages={31-41}
}
The interest in autonomous driving has continuously increased in the last two decades. However, to be adopted, such critical systems need to be safe. Concerning the perception of the ego-vehicle environment, the literature has investigated two different types of methods. On the one hand, traditional analytical methods generally rely on handcrafted designs and features while on the other hand, learning methods aim at designing their own appropriate representation of the observed scene. 

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