most citedAddressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning

33 citations · 33 across the 1 of their papers we have counts for

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cs.AI2021★ 33 cited

Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning

Julian Bernhard, Stefan Pollok, Alois Knoll

For highly automated driving above SAE level~3, behavior generation algorithms must reliably consider the inherent uncertainties of the traffic environment, e.g. arising from the v…