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Found 4 papers, 2 papers with code
Attacking Motion Planners Using Adversarial Perception Errors
Autonomous driving (AD) systems are often built and tested in a modular fashion, where the performance of different modules is measured using task-specific metrics.
An Active Learning Reliability Method for Systems with Partially Defined Performance Functions
This enables active learning Gaussian process methods to be applied to problems where the performance of the system is sometimes undefined, and we demonstrate the effectiveness of our approach by testing our methodology on synthetic numerical examples for the autonomous driving domain.
Query-based Hard-Image Retrieval for Object Detection at Test Time
There is a longstanding interest in capturing the error behaviour of object detectors by finding images where their performance is likely to be unsatisfactory.
A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation
There has been increasing interest in characterising the error behaviour of systems which contain deep learning models before deploying them into any safety-critical scenario.
