Projects per year
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.
|Title of host publication||Proceedings of the NeurIPS 2022|
|Number of pages||17|
|Publication status||Accepted/In press - 14 Sep 2022|
|Event||The 36th Conference on Neural Information Processing Systems, 2022 - New Orleans, United States|
Duration: 28 Nov 2022 → 9 Dec 2022
Conference number: 36
|Conference||The 36th Conference on Neural Information Processing Systems, 2022|
|Abbreviated title||NeurIPS 2022|
|Period||28/11/22 → 9/12/22|
FingerprintDive into the research topics of 'Risk-Driven Design of Perception Systems'. Together they form a unique fingerprint.
- 1 Active
UKRI Trustworthy Autonomous Systems Node in Governance and Regulation
Ramamoorthy, R., Belle, V., Bundy, A., Jackson, P., Lascarides, A. & Rajan, A.
1/11/20 → 30/04/24