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Document type:
Konferenzbeitrag 
Author(s):
Bernhard, Julian; Pollok, Stefan; Knoll, Alois 
Title:
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning 
Abstract:
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 variety of human driving styles. Such uncertainties can generate ambiguous decisions, requiring the algorithm to appropriately balance low-probability hazardous events, e.g. collisions, and high-probability beneficial events, e.g. quickly crossing the intersection. State-of-the-art behavior generation al...    »
 
Editor:
IEEE 
Book / Congress title:
2019 IEEE Intelligent Vehicles Symposium (IV) 
Year:
2019