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Risk-Driven Design of Perception Systems
Anthony Corso · Sydney Katz · Craig Innes · Xin Du · Subramanian Ramamoorthy · Mykel J Kochenderfer

Wed Nov 30 09:00 AM -- 11:00 AM (PST) @ Hall J #928

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.

Author Information

Anthony Corso (Stanford University)
Sydney Katz (Stanford University)
Craig Innes (Edinburgh University, University of Edinburgh)
Xin Du (University of Edinburgh)
Subramanian Ramamoorthy (The University of Edinburgh)
Mykel J Kochenderfer (Stanford University)

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