From 66ca4b7415a90cefab5b246a5ff71ff02152a3f8 Mon Sep 17 00:00:00 2001 From: mjf-su <114531941+mjf-su@users.noreply.github.com> Date: Sun, 15 Sep 2024 20:38:26 -0700 Subject: [PATCH] ICRA 2025 bib --- _bibliography/ASL_Bib.bib | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/_bibliography/ASL_Bib.bib b/_bibliography/ASL_Bib.bib index 91b94d3b..321a2434 100755 --- a/_bibliography/ASL_Bib.bib +++ b/_bibliography/ASL_Bib.bib @@ -4244,6 +4244,18 @@ @inproceedings{FoutterBohjEtAl2024 timestamp = {2024-08-12} } +@inproceedings{DyroFoutterEtAl2024, + author = {Dyro, R. and Foutter, M. and Li, R. and Schmerling, E. and Zhou, X. and Di Lillo, L. and Pavone, M.}, + title = {Realistic Extreme Behavior Generation for Improved AV Testing}, + booktitle = proc_IEEE_ICRA, + year = {2025}, + abstract = {This work introduces a framework to diagnose the strengths and shortcomings of Autonomous Vehicle (AV) collision avoidance technology with synthetic yet realistic potential collision scenarios adapted from real-world, collision-free data. Our framework generates counterfactual collisions with diverse crash properties, e.g., crash angle and velocity, between an adversary and a target vehicle by adding perturbations to the adversary's predicted trajectory from a learned AV behavior model. Our main contribution is to ground these adversarial perturbations in realistic behavior as defined through the lens of data-alignment in the behavior model's parameter space. Then, we cluster these synthetic counterfactuals to identify plausible and representative collision scenarios to form the basis of a test suite for downstream AV system evaluation. We demonstrate our framework using two state-of-the-art behavior prediction models as sources of realistic adversarial perturbations, and show that our scenario clustering evokes interpretable failure modes from a baseline AV policy under evaluation.}, + url = {/wp-content/papercite-data/pdf/Dyro.Foutter.Li.ea.ICRA2025.pdf}, + owner = {foutter}, + keywords = {sub}, + timestamp = {2024-09-15} +} + @inproceedings{FladerAhnEtAl2016, author = {Flader, I. B. and Ahn, C. H. and Gerrard, D. D. and Ng, E. J. and Yang, Y. and Hong, V. A. and Pavone, M. and Kenny, T. W.}, title = {Autonomous calibration of {MEMS} disk resonating gyroscope for improved sensor performance},