[Invited Talk 1] Detection and Resolution of Control Decision Anomalies
Kang Geun Shin
Professor, University of Michigan
Detection and Resolution of Control Decision Anomalies
Component faults, bugs, and malicious attacks can all degrade in, or even prevent semi-autonomous systems (SASs) from, correctly capturing their operation context, which is essential to support critical safety features like emergency braking in an autonomous car. While safety features in modern SASs usually rely on static assignment of control priority, such a design may lead to catastrophic accidents when accompanied with erroneous/compromised control and context estimation.
To mitigate the grave danger of SASs' use of incorrect data for making control decisions and learn from the incidents/crashes of Boeing 737 MAX, we propose CADCA, a novel control decision-maker for SASs, that is designed to operate under sensor/data errors or falsifications as well as malicious/erroneous control inputs with the ultimate goal of resolving conflicting control inputs to ensure safety. Our extensive evaluation (of more than 15,700 test-cases) has shown CADCA to achieve a 98% success rate in preventing the execution of incorrect control decisions caused by component failures and/or malicious attacks in the most common scenarios.
This talk will detail the motivation, design and evaluation of CADCA with semi-autonomous vehicles as a representative SAS.
This is joint work with Daniel Chen and Noah Curran.
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