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
algorithms lack a distributional treatment of decision outcome.
This impedes a proper risk evaluation in ambiguous situations,
often encouraging either unsafe or conservative behavior. Thus,
we propose a two-step approach for risk-sensitive behavior
generation combining offline distribution learning with online risk assessment. Specifically, we first learn an optimal
policy in an uncertain environment with Deep Distributional
Reinforcement Learning. During execution, the optimal risk-
sensitive action is selected by applying established risk criteria,
such as the Conditional Value at Risk, to the learned state-
action return distributions. In intersection crossing scenarios,
we evaluate different risk criteria and demonstrate that our
approach increases safety, while maintaining an active driving
style. Our approach shall encourage further studies about the
benefits of risk-sensitive approaches for self-driving vehicles.
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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
algorithms...
»
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