ICRA 2025 bib

_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},