  Autonomous driving vehicles provide a vast potential for realizing use cases
in the on-road and off-road domains. Consequently, remarkable solutions exist
to autonomous systems' environmental perception and control. Nevertheless,
proof of safety remains an open challenge preventing such machinery from being
introduced to markets and deployed in real world. Traditional approaches for
safety assurance of autonomously driving vehicles often lead to
underperformance due to conservative safety assumptions that cannot handle the
overall complexity. Besides, the more sophisticated safety systems rely on the
vehicle's perception systems. However, perception is often unreliable due to
uncertainties resulting from disturbances or the lack of context incorporation
for data interpretation. Accordingly, this paper illustrates the potential of a
modular, self-adaptive autonomy framework with integrated dynamic risk
management to overcome the abovementioned drawbacks.
