Co-Optimizing Sensing and Deep Machine Learning in Automotive Cyber-Physical Systems

Abstract

Accurate perception of the environment is critical to achieving safety and performance goals in emerging semi-autonomous vehicles. Building a perception architecture to support autonomy goals in vehicles requires solving many complex problems related to sensor selection and placement, sensor fusion, and machine leaning driven object detection. In this paper, we present a framework for co-optimizing sensing and machine learning to meet autonomy goals in emerging automotive cyber-physical systems. Experimental results that target level 2 autonomy goals for the Audi-TT and BMW-Minicooper vehicles demonstrate how our framework can intelligently traverse the massive design space to find robust, vehicle-specific perception architecture solutions.