2 research outputs found
Transfer Importance Sampling \unicode{x2013} How Testing Automated Vehicles in Multiple Test Setups Helps With the Bias-Variance Tradeoff
The promise of increased road safety is a key motivator for the development
of automated vehicles (AV). Yet, demonstrating that an AV is as safe as, or
even safer than, a human-driven vehicle has proven to be challenging. Should an
AV be examined purely virtually, allowing large numbers of fully controllable
tests? Or should it be tested under real environmental conditions on a proving
ground? Since different test setups have different strengths and weaknesses, it
is still an open question how virtual and real tests should be combined. On the
way to answer this question, this paper proposes transfer importance sampling
(TIS), a risk estimation method linking different test setups. Fusing the
concepts of transfer learning and importance sampling, TIS uses a scalable,
cost-effective test setup to comprehensively explore an AV's behavior. The
insights gained then allow parameterizing tests in a more trustworthy test
setup accurately reflecting risks. We show that when using a trustworthy test
setup alone is prohibitively expensive, linking it to a scalable test setup can
increase efficiency \unicode{x2013} without sacrificing the result's
validity. Thus, the test setups' individual deficiencies are compensated for by
their systematic linkage.Comment: 6 pages, 5 figures, 1 table, submitted to IEEE ITSC 202
Vectorized Scenario Description and Motion Prediction for Scenario-Based Testing
Automated vehicles (AVs) are tested in diverse scenarios, typically specified
by parameters such as velocities, distances, or curve radii. To describe
scenarios uniformly independent of such parameters, this paper proposes a
vectorized scenario description defined by the road geometry and vehicles'
trajectories. Data of this form are generated for three scenarios, merged, and
used to train the motion prediction model VectorNet, allowing to predict an
AV's trajectory for unseen scenarios. Predicting scenario evaluation metrics,
VectorNet partially achieves lower errors than regression models that
separately process the three scenarios' data. However, for comprehensive
generalization, sufficient variance in the training data must be ensured. Thus,
contrary to existing methods, our proposed method can merge diverse scenarios'
data and exploit spatial and temporal nuances in the vectorized scenario
description. As a result, data from specified test scenarios and real-world
scenarios can be compared and combined for (predictive) analyses and scenario
selection.Comment: 6 pages, 7 figures, 3 table