Design of Safer Control for Semi-Autonomous Vehicles

Component faults, bugs, and malicious attacks can all degrade in, or even prevent semi-autonomous vehicles (SAVs) from, correctly capturing their operation context, which is essential to support critical safety features like emergency braking or wheel-steering. While safety features in contemporary SAVs 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 consequence of using incorrect data, we propose CADCA , a novel control decision-maker for SAVs, 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 evaluation of \(>\) 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 ( i.e. , rear-end) collisions.