Energy-Efficient Adaptive System Reconfiguration for Dynamic Deadlines in Autonomous Driving
This addresses energy efficiency for autonomous vehicles, offering significant gains but is incremental over existing optimization approaches.
The paper tackled the problem of energy optimization in autonomous driving systems with dynamic deadlines, achieving up to 46.4% average energy reduction compared to static methods.
The increasing computing demands of autonomous driving applications make energy optimizations critical for reducing battery capacity and vehicle weight. Current energy optimization methods typically target traditional real-time systems with static deadlines, resulting in conservative energy savings that are unable to exploit additional energy optimizations due to dynamic deadlines arising from the vehicle's change in velocity and driving context. We present an adaptive system optimization and reconfiguration approach that dynamically adapts the scheduling parameters and processor speeds to satisfy dynamic deadlines while consuming as little energy as possible. Our experimental results with an autonomous driving task set from Bosch and real-world driving data show energy reductions up to 46.4% on average in typical dynamic driving scenarios compared with traditional static energy optimization methods, demonstrating great potential for dynamic energy optimization gains by exploiting dynamic deadlines.