mlsanityData to 14 Oct 2026

Spatial-Channel Transformer Network for Trajectory Prediction on the Traffic Scenes

arXiv 2101.11472 ↗First posted 27 Jan 2021cs.CVcited by 5

Not on the map. Why?

Abstract (arXiv)

Predicting motion of surrounding agents is critical to real-world applications of tactical path planning for autonomous driving. Due to the complex temporal dependencies and social interactions of agents, on-line trajectory prediction is a challenging task. With the development of attention mechanism in recent years, transformer model has been applied in natural language sequence processing first and then image processing. In this paper, we present a Spatial-Channel Transformer Network for trajectory prediction with attention functions. Instead of RNN models, we employ transformer model to capture the spatial-temporal features of agents. A channel-wise module is inserted to measure the social interaction between agents. We find that the Spatial-Channel Transformer Network achieves promising results on real-world trajectory prediction datasets on the traffic scenes.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 15 of its 20 references that are in the map. First posted on arXiv 27 Jan 2021.

Builds on

15 of 20 references.

References · 15

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  7. 2020Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Predictioncited by 325
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  12. 2019SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Predictioncited by 111
3 more
  1. 2019Particle Filter Recurrent Neural Networkscited by 36
  2. 2019Differentiable Algorithm Networks for Composable Robot Learningcited by 24
  3. 2018An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmarkcited by 16

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 1.

5 papers.

  1. 2023Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directionscited by 23
  2. 2022Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarioscited by 13
  3. 2023SST: A Simplified Swin Transformer-based Model for Taxi Destination Prediction based on Existing Trajectorycited by 4
  4. 2023Target-point Attention Transformer: A novel trajectory predict network for end-to-end autonomous drivingcited by 2
  5. 2024C2INet: Realizing Incremental Trajectory Prediction with Prior-Aware Continual Causal Interventioncited by 0