Robust Perception Architecture Design for Automotive Cyber-Physical Systems

TL;DR

This paper introduces PASTA, a framework that globally co-optimizes sensing and deep learning to enhance environmental perception in automotive cyber-physical systems, improving safety and performance.

Contribution

The paper presents PASTA, a novel framework that jointly optimizes sensor placement and perception algorithms for more robust vehicle perception systems.

Findings

PASTA finds vehicle-specific perception architectures.

Experimental results demonstrate improved perception robustness.

Framework applicable to different vehicle models.

Abstract

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.