Robust Perception Architecture Design for Automotive Cyber-Physical Systems

• URL: http://arxiv.org/abs/2205.08067v1
• Date: Tue, 17 May 2022 03:02:07 GMT
• Title: Robust Perception Architecture Design for Automotive Cyber-Physical Systems
• Authors: Joydeep Dey, Sudeep Pasricha
• Abstract summary: PASTA is a framework for global co-optimization of deep learning and sensing for dependable vehicle perception. We show how PASTA can find robust, vehicle-specific perception architecture solutions.
• Score: 4.226118870861363
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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