Detecting other agents and forecasting their behavior is an integral part of the modern robotic autonomy stack, especially in safety-critical scenarios entailing human-robot interaction such as autonomous driving. Due to the importance of these components, there has been a significant amount of interest and research in perception and trajectory forecasting, resulting in a wide variety of approaches. Common to most works, however, is the use of the same few accuracy-based evaluation metrics, e.g., intersection-over-union, displacement error, log-likelihood, etc. While these metrics are informative, they are task-agnostic and outputs that are evaluated as equal can lead to vastly different outcomes in downstream planning and decision making. In this work, we take a step back and critically assess current evaluation metrics, proposing task-aware metrics as a better measure of performance in systems where they are deployed. Experiments on an illustrative simulation as well as real-world autonomous driving data validate that our proposed task-aware metrics are able to account for outcome asymmetry and provide a better estimate of a model’s closed-loop performance.
Injecting Planning-Awareness into Prediction and Detection Evaluation
2022-06-05
3354496 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Injecting Into The Commerical Vehicle Market
Online Contents | 2011
OIL & GAS - Injecting New Life Into Assets
Online Contents | 2012
PRODUCTS - Injecting Precision Into A Small Package
Online Contents | 2006
Advanced long products - injecting efficiency into modern engines
Tema Archiv | 2007
|