Robust Perception Architecture Design for Automotive Cyber-Physical Systems
This addresses the need for efficient and dependable perception in autonomous vehicles, though it appears incremental by integrating existing methods into a unified framework.
The paper tackles the problem of designing robust perception architectures for automotive cyber-physical systems by proposing PASTA, a framework for global co-optimization of deep learning and sensing, which finds vehicle-specific solutions as demonstrated with Audi-TT and BMW-Minicooper vehicles.
In emerging automotive cyber-physical systems (CPS), accurate environmental perception is critical to achieving safety and performance goals. Enabling robust perception for vehicles requires solving multiple complex problems related to sensor selection/ placement, object detection, and sensor fusion. Current methods address these problems in isolation, which leads to inefficient solutions. We present PASTA, a novel framework for global co-optimization of deep learning and sensing for dependable vehicle perception. Experimental results with the Audi-TT and BMW-Minicooper vehicles show how PASTA can find robust, vehicle-specific perception architecture solutions.