Perception Without Vision for Trajectory Prediction: Ego Vehicle Dynamics as Scene Representation for Efficient Active Learning in Autonomous Driving
CoRR(2024)
Abstract
This study investigates the use of trajectory and dynamic state information
for efficient data curation in autonomous driving machine learning tasks. We
propose methods for clustering trajectory-states and sampling strategies in an
active learning framework, aiming to reduce annotation and data costs while
maintaining model performance. Our approach leverages trajectory information to
guide data selection, promoting diversity in the training data. We demonstrate
the effectiveness of our methods on the trajectory prediction task using the
nuScenes dataset, showing consistent performance gains over random sampling
across different data pool sizes, and even reaching sub-baseline displacement
errors at just 50
data initially helps overcome the ”cold start problem,” while introducing
novelty becomes more beneficial as the training pool size increases. By
integrating trajectory-state-informed active learning, we demonstrate that more
efficient and robust autonomous driving systems are possible and practical
using low-cost data curation strategies.
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