Risk-Driven Design of Perception Systems

Anthony L. Corso, Sydney M. Katz, Craig Innes, Xin Du, Subramanian Ramamoorthy, Mykel J. Kochenderfer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Modern autonomous systems rely on perception modules to process complex sensor measurements into state estimates. These estimates are then passed to a controller, which uses them to make safety-critical decisions. It is therefore important that we design perception systems to minimize errors that reduce the overall safety of the system. We develop a risk-driven approach to designing perception systems that accounts for the effect of perceptual errors on the performance of the fully-integrated, closed-loop system. We formulate a risk function to quantify the effect of a given perceptual error on overall safety, and show how we can use it to design safer perception systems by including a risk-dependent term in the loss function and generating training data in risk-sensitive regions. We evaluate our techniques on a realistic vision-based aircraft detect and avoid application and show that risk-driven design reduces collision risk by 37% over a baseline system.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 35 (NeurIPS 2022)
EditorsS. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh
PublisherCurran Associates Inc
Pages9894-9906
Number of pages12
Volume35
Publication statusPublished - 1 Apr 2023
EventThe 36th Conference on Neural Information Processing Systems, 2022 - New Orleans, United States
Duration: 28 Nov 20229 Dec 2022
Conference number: 36
https://neurips.cc/Conferences/2022

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Conference

ConferenceThe 36th Conference on Neural Information Processing Systems, 2022
Abbreviated titleNeurIPS 2022
Country/TerritoryUnited States
CityNew Orleans
Period28/11/229/12/22
Internet address

Fingerprint

Dive into the research topics of 'Risk-Driven Design of Perception Systems'. Together they form a unique fingerprint.

Cite this