3,928 research outputs found
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
CAR-Net: Clairvoyant Attentive Recurrent Network
We present an interpretable framework for path prediction that leverages
dependencies between agents' behaviors and their spatial navigation
environment. We exploit two sources of information: the past motion trajectory
of the agent of interest and a wide top-view image of the navigation scene. We
propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where
to look in a large image of the scene when solving the path prediction task.
Our method can attend to any area, or combination of areas, within the raw
image (e.g., road intersections) when predicting the trajectory of the agent.
This allows us to visualize fine-grained semantic elements of navigation scenes
that influence the prediction of trajectories. To study the impact of space on
agents' trajectories, we build a new dataset made of top-view images of
hundreds of scenes (Formula One racing tracks) where agents' behaviors are
heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net
successfully attends to these salient regions. Additionally, CAR-Net reaches
state-of-the-art accuracy on the standard trajectory forecasting benchmark,
Stanford Drone Dataset (SDD). Finally, we show CAR-Net's ability to generalize
to unseen scenes.Comment: The 2nd and 3rd authors contributed equall
A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction
Accurate and robust trajectory prediction of neighboring agents is critical
for autonomous vehicles traversing in complex scenes. Most methods proposed in
recent years are deep learning-based due to their strength in encoding complex
interactions. However, unplausible predictions are often generated since they
rely heavily on past observations and cannot effectively capture the transient
and contingency interactions from sparse samples. In this paper, we propose a
hierarchical hybrid framework of deep learning (DL) and reinforcement learning
(RL) for multi-agent trajectory prediction, to cope with the challenge of
predicting motions shaped by multi-scale interactions. In the DL stage, the
traffic scene is divided into multiple intermediate-scale heterogenous graphs
based on which Transformer-style GNNs are adopted to encode heterogenous
interactions at intermediate and global levels. In the RL stage, we divide the
traffic scene into local sub-scenes utilizing the key future points predicted
in the DL stage. To emulate the motion planning procedure so as to produce
trajectory predictions, a Transformer-based Proximal Policy Optimization (PPO)
incorporated with a vehicle kinematics model is devised to plan motions under
the dominant influence of microscopic interactions. A multi-objective reward is
designed to balance between agent-centric accuracy and scene-wise
compatibility. Experimental results show that our proposal matches the
state-of-the-arts on the Argoverse forecasting benchmark. It's also revealed by
the visualized results that the hierarchical learning framework captures the
multi-scale interactions and improves the feasibility and compliance of the
predicted trajectories
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