@inproceedings{1109, author = {Charles Hartsell and Shreyas Ramakrishna and Abhishek Dubey and Daniel Stojcsics and Nagabhushan Mahadevan and Gabor Karsai}, title = {ReSonAte: A Runtime Risk Assessment Framework for Autonomous Systems}, abstract = {

Autonomous Cyber Physical Systems (CPSs) are
often required to handle uncertainties and self-manage the system
operation in response to problems and increasing risk in the
operating paradigm. This risk may arise due to distribution
shifts, environmental context, or failure of software or hardware
components. Traditional techniques for risk assessment focus on
design-time techniques such as hazard analysis, risk reduction,
and assurance cases among others. However, these static, design-
time techniques do not consider the dynamic contexts and failures
the systems face at runtime. We hypothesize that this requires
a dynamic assurance approach that computes the likelihood
of unsafe conditions or system failures considering the safety
requirements, assumptions made at design time, past failures in a
given operating context, and the likelihood of system component
failures. We introduce the ReSonAte dynamic risk estimation
framework for autonomous systems. ReSonAte reasons over Bow-
Tie Diagrams (BTDs) which capture information about hazard
propagation paths and control strategies. Our innovation is the
extension of the BTD formalism with attributes for modeling
the conditional relationships with the state of the system and
environment. We also describe a technique for estimating these
conditional relationships and equations for estimating risk based
on the state of the system and environment. To help with this
process, we provide a scenario modeling procedure that can
use the prior distributions of the scenes and threat conditions
to generate the data required for estimating the conditional
relationships. To improve scalability and reduce the amount of
data required, this process considers each control strategy in
isolation and composes several single-variate distributions into
one complete multi-variate distribution for the control strategy
in question. Lastly, we describe the effectiveness of our approach
using two separate autonomous system simulations: CARLA and
an unmanned underwater vehicle.

}, year = {2021}, journal = {16th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS}, pages = {118-129}, month = {05}, publisher = {IEEE}, address = {Madrid, Spain}, isbn = {978-1-6654-0289-7}, url = {https://ieeexplore.ieee.org/document/9462019}, doi = {10.1109/SEAMS51251.2021.00025}, }