819 research outputs found
Transferable Pedestrian Motion Prediction Models at Intersections
One desirable capability of autonomous cars is to accurately predict the
pedestrian motion near intersections for safe and efficient trajectory
planning. We are interested in developing transfer learning algorithms that can
be trained on the pedestrian trajectories collected at one intersection and yet
still provide accurate predictions of the trajectories at another, previously
unseen intersection. We first discussed the feature selection for transferable
pedestrian motion models in general. Following this discussion, we developed
one transferable pedestrian motion prediction algorithm based on Inverse
Reinforcement Learning (IRL) that infers pedestrian intentions and predicts
future trajectories based on observed trajectory. We evaluated our algorithm on
a dataset collected at two intersections, trained at one intersection and
tested at the other intersection. We used the accuracy of augmented
semi-nonnegative sparse coding (ASNSC), trained and tested at the same
intersection as a baseline. The result shows that the proposed algorithm
improves the baseline accuracy by 40% in the non-transfer task, and 16% in the
transfer task
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
Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting
Deep motion forecasting models have achieved great success when trained on a
massive amount of data. Yet, they often perform poorly when training data is
limited. To address this challenge, we propose a transfer learning approach for
efficiently adapting pre-trained forecasting models to new domains, such as
unseen agent types and scene contexts. Unlike the conventional fine-tuning
approach that updates the whole encoder, our main idea is to reduce the amount
of tunable parameters that can precisely account for the target domain-specific
motion style. To this end, we introduce two components that exploit our prior
knowledge of motion style shifts: (i) a low-rank motion style adapter that
projects and adjusts the style features at a low-dimensional bottleneck; and
(ii) a modular adapter strategy that disentangles the features of scene context
and motion history to facilitate a fine-grained choice of adaptation layers.
Through extensive experimentation, we show that our proposed adapter design,
coined MoSA, outperforms prior methods on several forecasting benchmarks.Comment: CoRL 202
Knowledge transfer for scene-specific motion prediction
When given a single frame of the video, humans can not only interpret the content of the scene, but also they are able to forecast the near future. This ability is mostly driven by their rich prior knowledge about the visual world, both in terms of (i) the dynamics of moving agents, as well as (ii) the semantic of the scene. In this work we exploit the interplay between these two key elements to predict scene-specific motion patterns. First, we extract patch descriptors encoding the probability of moving to the adjacent patches, and the probability of being in that particular patch or changing behavior. Then, we introduce a Dynamic Bayesian Network which exploits this scene specific knowledge for trajectory prediction. Experimental results demonstrate that our method is able to accurately predict trajectories and transfer predictions to a novel scene characterized by similar elements
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