A Fast and Map-Free Model for Trajectory Prediction in Traffics
- URL: http://arxiv.org/abs/2307.09831v1
- Date: Wed, 19 Jul 2023 08:36:31 GMT
- Title: A Fast and Map-Free Model for Trajectory Prediction in Traffics
- Authors: Junhong Xiang, Jingmin Zhang and Zhixiong Nan
- Abstract summary: This paper proposes an efficient trajectory prediction model that is not dependent on traffic maps.
By comprehensively utilizing attention mechanism, LSTM, graph convolution network and temporal transformer, our model is able to learn rich dynamic and interaction information of all agents.
Our model achieves the highest performance when comparing with existing map-free methods and also exceeds most map-based state-of-the-art methods on the Argoverse dataset.
- Score: 2.435517936694533
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: To handle the two shortcomings of existing methods, (i)nearly all models rely
on high-definition (HD) maps, yet the map information is not always available
in real traffic scenes and HD map-building is expensive and time-consuming and
(ii) existing models usually focus on improving prediction accuracy at the
expense of reducing computing efficiency, yet the efficiency is crucial for
various real applications, this paper proposes an efficient trajectory
prediction model that is not dependent on traffic maps. The core idea of our
model is encoding single-agent's spatial-temporal information in the first
stage and exploring multi-agents' spatial-temporal interactions in the second
stage. By comprehensively utilizing attention mechanism, LSTM, graph
convolution network and temporal transformer in the two stages, our model is
able to learn rich dynamic and interaction information of all agents. Our model
achieves the highest performance when comparing with existing map-free methods
and also exceeds most map-based state-of-the-art methods on the Argoverse
dataset. In addition, our model also exhibits a faster inference speed than the
baseline methods.
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