2,106 research outputs found
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving
Self-driving vehicles need to anticipate a diverse set of future traffic
scenarios in order to safely share the road with other traffic participants
that may exhibit rare but dangerous driving. In this paper, we present LookOut,
an approach to jointly perceive the environment and predict a diverse set of
futures from sensor data, estimate their probability, and optimize a
contingency plan over these diverse future realizations. In particular, we
learn a diverse joint distribution over multi-agent future trajectories in a
traffic scene that allows us to cover a wide range of future modes with high
sample efficiency while leveraging the expressive power of generative models.
Unlike previous work in diverse motion forecasting, our diversity objective
explicitly rewards sampling future scenarios that require distinct reactions
from the self-driving vehicle for improved safety. Our contingency planner then
finds comfortable trajectories that ensure safe reactions to a wide range of
future scenarios. Through extensive evaluations, we show that our model
demonstrates significantly more diverse and sample-efficient motion forecasting
in a large-scale self-driving dataset as well as safer and more comfortable
motion plans in long-term closed-loop simulations than current state-of-the-art
models
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Bridging the Gap Between Multi-Step and One-Shot Trajectory Prediction via Self-Supervision
Accurate vehicle trajectory prediction is an unsolved problem in autonomous
driving with various open research questions. State-of-the-art approaches
regress trajectories either in a one-shot or step-wise manner. Although
one-shot approaches are usually preferred for their simplicity, they relinquish
powerful self-supervision schemes that can be constructed by chaining multiple
time-steps. We address this issue by proposing a middle-ground where multiple
trajectory segments are chained together. Our proposed Multi-Branch
Self-Supervised Predictor receives additional training on new predictions
starting at intermediate future segments. In addition, the model 'imagines' the
latent context and 'predicts the past' while combining multi-modal trajectories
in a tree-like manner. We deliberately keep aspects such as interaction and
environment modeling simplistic and nevertheless achieve competitive results on
the INTERACTION dataset. Furthermore, we investigate the sparsely explored
uncertainty estimation of deterministic predictors. We find positive
correlations between the prediction error and two proposed metrics, which might
pave way for determining prediction confidence.Comment: 8 pages, 6 figures, to be published in 34th IEEE Intelligent Vehicles
Symposium (IV
From Prediction to Planning With Goal Conditioned Lane Graph Traversals
The field of motion prediction for automated driving has seen tremendous
progress recently, bearing ever-more mighty neural network architectures.
Leveraging these powerful models bears great potential for the closely related
planning task. In this letter we propose a novel goal-conditioning method and
show its potential to transform a state-of-the-art prediction model into a
goal-directed planner. Our key insight is that conditioning prediction on a
navigation goal at the behaviour level outperforms other widely adopted
methods, with the additional benefit of increased model interpretability. We
train our model on a large open-source dataset and show promising performance
in a comprehensive benchmark
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