Provably-Safe Offloading of Neural Network Controllers for Energy-Efficiency in Autonomous Driving Systems
DATE/TIME: Wednesday, January 23rd, 12:00pm
SPEAKER: Mohanad Odema
To mitigate the high energy demand of Neural Network (NN) based Autonomous Driving Systems (ADSs), we consider the problem of offloading NN controllers from the ADS to nearby edge-computing infrastructure, but in such a way that formal vehicle safety properties are guaranteed. In particular, we propose the EnergyShield framework, which repurposes a controller "shield" as a low-power runtime safety monitor for the ADS vehicle.