36 research outputs found
Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning
Learning-based behavior prediction methods are increasingly being deployed in
real-world autonomous systems, e.g., in fleets of self-driving vehicles, which
are beginning to commercially operate in major cities across the world. Despite
their advancements, however, the vast majority of prediction systems are
specialized to a set of well-explored geographic regions or operational design
domains, complicating deployment to additional cities, countries, or
continents. Towards this end, we present a novel method for efficiently
adapting behavior prediction models to new environments. Our approach leverages
recent advances in meta-learning, specifically Bayesian regression, to augment
existing behavior prediction models with an adaptive layer that enables
efficient domain transfer via offline fine-tuning, online adaptation, or both.
Experiments across multiple real-world datasets demonstrate that our method can
efficiently adapt to a variety of unseen environments.Comment: 12 pages, 13 figures, 2 tables. ICRA 202
DiffStack: A Differentiable and Modular Control Stack for Autonomous Vehicles
Autonomous vehicle (AV) stacks are typically built in a modular fashion, with
explicit components performing detection, tracking, prediction, planning,
control, etc. While modularity improves reusability, interpretability, and
generalizability, it also suffers from compounding errors, information
bottlenecks, and integration challenges. To overcome these challenges, a
prominent approach is to convert the AV stack into an end-to-end neural network
and train it with data. While such approaches have achieved impressive results,
they typically lack interpretability and reusability, and they eschew
principled analytical components, such as planning and control, in favor of
deep neural networks. To enable the joint optimization of AV stacks while
retaining modularity, we present DiffStack, a differentiable and modular stack
for prediction, planning, and control. Crucially, our model-based planning and
control algorithms leverage recent advancements in differentiable optimization
to produce gradients, enabling optimization of upstream components, such as
prediction, via backpropagation through planning and control. Our results on
the nuScenes dataset indicate that end-to-end training with DiffStack yields
substantial improvements in open-loop and closed-loop planning metrics by,
e.g., learning to make fewer prediction errors that would affect planning.
Beyond these immediate benefits, DiffStack opens up new opportunities for fully
data-driven yet modular and interpretable AV architectures. Project website:
https://sites.google.com/view/diffstackComment: CoRL 2022 camera read
Reinforcement Learning with Human Feedback for Realistic Traffic Simulation
In light of the challenges and costs of real-world testing, autonomous
vehicle developers often rely on testing in simulation for the creation of
reliable systems. A key element of effective simulation is the incorporation of
realistic traffic models that align with human knowledge, an aspect that has
proven challenging due to the need to balance realism and diversity. This works
aims to address this by developing a framework that employs reinforcement
learning with human preference (RLHF) to enhance the realism of existing
traffic models. This study also identifies two main challenges: capturing the
nuances of human preferences on realism and the unification of diverse traffic
simulation models. To tackle these issues, we propose using human feedback for
alignment and employ RLHF due to its sample efficiency. We also introduce the
first dataset for realism alignment in traffic modeling to support such
research. Our framework, named TrafficRLHF, demonstrates its proficiency in
generating realistic traffic scenarios that are well-aligned with human
preferences, as corroborated by comprehensive evaluations on the nuScenes
dataset.Comment: 9 pages, 4 figure