UST: Unifying Spatio-Temporal Context for Trajectory Prediction in
Autonomous Driving
- URL: http://arxiv.org/abs/2005.02790v1
- Date: Wed, 6 May 2020 13:02:57 GMT
- Title: UST: Unifying Spatio-Temporal Context for Trajectory Prediction in
Autonomous Driving
- Authors: Hao He, Hengchen Dai, Naiyan Wang
- Abstract summary: We propose a unified approach to treat time and space dimensions equally for modeling-temporal context.
We show that the proposed method substantially outperforms the previous state-of-the-art methods while maintaining its simplicity.
- Score: 20.017491739890588
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Trajectory prediction has always been a challenging problem for autonomous
driving, since it needs to infer the latent intention from the behaviors and
interactions from traffic participants. This problem is intrinsically hard,
because each participant may behave differently under different environments
and interactions. This key is to effectively model the interlaced influence
from both spatial context and temporal context. Existing work usually encodes
these two types of context separately, which would lead to inferior modeling of
the scenarios. In this paper, we first propose a unified approach to treat time
and space dimensions equally for modeling spatio-temporal context. The proposed
module is simple and easy to implement within several lines of codes. In
contrast to existing methods which heavily rely on recurrent neural network for
temporal context and hand-crafted structure for spatial context, our method
could automatically partition the spatio-temporal space to adapt the data.
Lastly, we test our proposed framework on two recently proposed trajectory
prediction dataset ApolloScape and Argoverse. We show that the proposed method
substantially outperforms the previous state-of-the-art methods while
maintaining its simplicity. These encouraging results further validate the
superiority of our approach.
Related papers
- Linear Attention is Enough in Spatial-Temporal Forecasting [0.0]
We propose treating nodes in road networks at different time steps as independent spatial-temporal tokens.
We then feed them into a vanilla Transformer to learn complex spatial-temporal patterns.
Our code achieves state-of-the-art performance at an affordable computational cost.
arXiv Detail & Related papers (2024-08-17T10:06:50Z) - AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving [59.94343412438211]
We introduce the GPT style next token motion prediction into motion prediction.
Different from language data which is composed of homogeneous units -words, the elements in the driving scene could have complex spatial-temporal and semantic relations.
We propose to adopt three factorized attention modules with different neighbors for information aggregation and different position encoding styles to capture their relations.
arXiv Detail & Related papers (2024-03-20T06:22:37Z) - Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations [53.797896854533384]
Class-agnostic motion prediction methods directly predict the motion of the entire point cloud.
While most existing methods rely on fully-supervised learning, the manual labeling of point cloud data is laborious and time-consuming.
We introduce three simple spatial and temporal regularization losses, which facilitate the self-supervised training process effectively.
arXiv Detail & Related papers (2024-03-20T02:58:45Z) - Space and Time Continuous Physics Simulation From Partial Observations [0.0]
Data-driven methods based on large-scale machine learning promise high adaptivity by integrating long-range dependencies more directly and efficiently.
We focus on fluid dynamics and address the shortcomings of a large part of the literature, which are based on fixed support for computations and predictions in the form of regular or irregular grids.
We propose a novel setup to perform predictions in a continuous spatial and temporal domain while being trained on sparse observations.
arXiv Detail & Related papers (2024-01-17T13:24:04Z) - Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned
Interactive Trajectory Prediction [29.701029725302586]
We study the joint trajectory prediction problem with the goal-conditioned framework.
We introduce a conditional-variational-autoencoder-based (CVAE) model to explicitly encode different interaction modes into the latent space.
We propose a novel approach to avoid KL vanishing and induce an interpretable interactive latent space with pseudo labels.
arXiv Detail & Related papers (2022-03-28T21:41:21Z) - Averaging Spatio-temporal Signals using Optimal Transport and Soft
Alignments [110.79706180350507]
We show that our proposed loss can be used to define temporal-temporal baryechecenters as Fr'teche means duality.
Experiments on handwritten letters and brain imaging data confirm our theoretical findings.
arXiv Detail & Related papers (2022-03-11T09:46:22Z) - Congestion-aware Multi-agent Trajectory Prediction for Collision
Avoidance [110.63037190641414]
We propose to learn congestion patterns explicitly and devise a novel "Sense--Learn--Reason--Predict" framework.
By decomposing the learning phases into two stages, a "student" can learn contextual cues from a "teacher" while generating collision-free trajectories.
In experiments, we demonstrate that the proposed model is able to generate collision-free trajectory predictions in a synthetic dataset.
arXiv Detail & Related papers (2021-03-26T02:42:33Z) - Temporal Context Aggregation Network for Temporal Action Proposal
Refinement [93.03730692520999]
Temporal action proposal generation is a challenging yet important task in the video understanding field.
Current methods still suffer from inaccurate temporal boundaries and inferior confidence used for retrieval.
We propose TCANet to generate high-quality action proposals through "local and global" temporal context aggregation.
arXiv Detail & Related papers (2021-03-24T12:34:49Z) - Exploring Dynamic Context for Multi-path Trajectory Prediction [33.66335553588001]
We propose a novel framework, named Dynamic Context Network (DCENet)
In our framework, the spatial context between agents is explored by using self-attention architectures.
A set of future trajectories for each agent is predicted conditioned on the learned spatial-temporal context.
arXiv Detail & Related papers (2020-10-30T13:39:20Z) - SSP: Single Shot Future Trajectory Prediction [26.18589883075203]
We propose a robust solution to future trajectory forecast, which can be practically applicable to autonomous agents in highly crowded environments.
First, we use composite fields to predict future locations of all road agents in a single-shot, which results in a constant time.
Second, interactions between agents are modeled as non-local, response enabling spatial relationships between different locations to be captured temporally.
Third, the semantic context of the scene are modeled and take into account the environmental constraints that potentially influence the future motion.
arXiv Detail & Related papers (2020-04-13T09:56:38Z) - A Spatial-Temporal Attentive Network with Spatial Continuity for
Trajectory Prediction [74.00750936752418]
We propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC)
First, spatial-temporal attention mechanism is presented to explore the most useful and important information.
Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity.
arXiv Detail & Related papers (2020-03-13T04:35:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.