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Self-Supervised Learning for Autonomous Vehicles Perception: A Conciliation Between Analytical and Learning Methods
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DOI：10.1109/MSP.2020.2977269.png)
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Abstract
En 中文
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.
Keywords:
Learning systems
Autonomous vehicles
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