Safety-Aware System Optimization for Autonomous Machines

Abstract

Autonomous machines such as vehicles, drones, and robotic manipulators promise to transform the world by unleashing humans from repetitive, dangerous, and labor-intensive tasks. However, their widespread deployment requires advances in safety, real-time performance, and resilience. This thesis tackles these key challenges through end-to-end system optimization that maintains safety while improving performance and fault tolerance. We optimize the entire Perception-Planning-Control (PPC) computing pipeline that takes in sensor readings and output control commands. First, this thesis develops a model to quantify the perception processing rate requirements for safe autonomous driving in complex scenarios, connecting the varying real-time latency requirements with the operating scenarios. Second, we accelerate the time-consuming 3D mapping in perception. A specialized accelerator is designed that achieves substantially higher throughput and energy efficiency over a CPU, enabling real-time perception for 3D mapping. Third, we accelerate the computationally expensive optimization-based motion planning algorithms with a variable precision search method that reduces memory bandwidth pressure without sacrificing positional and orientational precision. Lastly, we assess autonomous machines' fault tolerance characteristics against real-world noises and errors to generate reliable control commands. We propose a fault characterization framework that evaluates the impact of silent data corruptions (SDCs) on application-level metrics. To mitigate SDCs, we propose lightweight anomaly detection techniques to recover failures in the computing pipeline with insignificant overhead. This dissertation enables the development of safe, real-time, and resilient autonomous machines. The contributions chart a path toward robust deployment of autonomous machines that can transform society.