A Fast and Map-Free Model for Trajectory Prediction in Traffics
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 26 of its 30 references that are in the map. First posted on arXiv 19 Jul 2023.
Builds on
26 of 30 references.
References · 26
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14 more
- 2021Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Drivingcited by 73
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- 2020Traffic Agent Trajectory Prediction Using Social Convolution and Attention Mechanismcited by 4
- 2021A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNNcited by 2
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.