Injecting Hallucinations in Autonomous Vehicles: A Component-Agnostic Safety Evaluation Framework
Injecting Hallucinations in Autonomous Vehicles: A Component-Agnostic Safety Evaluation Framework
This research presents a framework for evaluating the safety of autonomous vehicles by simulating perception failures as hallucinations. The results provide statistical validation of the framework and highlight how specific hallucination types affect vehicle safety.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Environment
- SimulationOpen source S4
Reported numbers
hallucination types induced
6
View original evidence
, we propose a configurable, component-agnostic hallucination injection framework that induces six plausible hallucination types in an open-source simulation environment. A total of 18,356 simulations (8,695 baseline and 9,661 with hallucinations injected) were executed while AVs crossed an unsignalized intersection with transversal traffic. The results statistically validate the framework and quantify the impact of
Open source S4
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
, we propose a configurable, component-agnostic hallucination injection framework that induces six plausible hallucination types in an open-source simulation environment. A total of 18,356 simulations (8,695 baseline and 9,661 with hallucinations injected) were executed while AVs crossed an unsignalized intersection with transversal traffic. The results statistically validate the framework and quantify the impact of
Open source S4
each hallucination type on collisions and on the minimum distance between the AV and the nearest vehicle, used as a continuous proxy for the safety margin. Certain hallucinations, especially missed detection, blind region, angular drift, phantom perception, and latency, significantly increase the odds of collision, while linear drift significantly reduces minimum distances without producing a statistically significan
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org