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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