mlsanityData to 7 Oct 2026

Transfer Learning Study of Motion Transformer-based Trajectory Predictions

arXiv 2404.08271 ↗First posted 12 Apr 2024cs.LG cs.ROcited by 5

Not on the map. Why?

Abstract (arXiv)

Trajectory planning in autonomous driving is highly dependent on predicting the emergent behavior of other road users. Learning-based methods are currently showing impressive results in simulation-based challenges, with transformer-based architectures technologically leading the way. Ultimately, however, predictions are needed in the real world. In addition to the shifts from simulation to the real world, many vehicle- and country-specific shifts, i.e. differences in sensor systems, fusion and perception algorithms as well as traffic rules and laws, are on the agenda. Since models that can cover all system setups and design domains at once are not yet foreseeable, model adaptation plays a central role. Therefore, a simulation-based study on transfer learning techniques is conducted on basis of a transformer-based model. Furthermore, the study aims to provide insights into possible trade-offs between computational time and performance to support effective transfers into the real world.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 37 of its 55 references that are in the map. First posted on arXiv 12 Apr 2024.

Builds on

37 of 55 references.

References · 37

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2020Language Models are Few-Shot Learnerscited by 32,199
  5. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  6. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  7. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  8. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  9. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  10. 2021Domain Generalization: A Surveycited by 760
  11. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 728
  12. 2018Deep Visual Domain Adaptation: A Surveycited by 634
25 more
  1. 2018End-to-End Multi-Task Learning with Attentioncited by 533
  2. 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 532
  3. 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 528
  4. 2021NuPlan: A closed-loop ML-based planning benchmark for autonomous vehiclescited by 485
  5. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  6. 2018A Survey on Deep Transfer Learningcited by 417
  7. 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 417
  8. 2019Transfusion: Understanding Transfer Learning for Medical Imagingcited by 366
  9. 2020TNT: Target-driveN Trajectory Predictioncited by 345
  10. 2019Exploring the Limitations of Behavior Cloning for Autonomous Drivingcited by 343
  11. 2021DenseTNT: End-to-end Trajectory Prediction from Dense Goal Setscited by 292
  12. 2022Motion Transformer with Global Intention Localization and Local Movement Refinementcited by 267
  13. 2014One weird trick for parallelizing convolutional neural networkscited by 264
  14. 2020One Thousand and One Hours: Self-driving Motion Prediction Datasetcited by 239
  15. 2021Scene Transformer: A unified architecture for predicting multiple agent trajectoriescited by 165
  16. 2020Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?cited by 139
  17. 2023MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Queryingcited by 139
  18. 2015Instance-aware Semantic Segmentation via Multi-task Network Cascadescited by 120
  19. 2021Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Taskscited by 100
  20. 2021Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learningcited by 72
  21. 2020On the Value of Target Data in Transfer Learningcited by 62
  22. 2020Multi-task learning for natural language processing in the 2020s: where are we going?cited by 27
  23. 2022Shifts 2.0: Extending The Dataset of Real Distributional Shiftscited by 22
  24. 2023EDA: Evolving and Distinct Anchors for Multimodal Motion Predictioncited by 17
  25. 2023MGTR: Multi-Granular Transformer for Motion Prediction with LiDARcited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2024LoRD: Adapting Differentiable Driving Policies to Distribution Shiftscited by 4
  2. 2025Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedbackcited by 3
  3. 2026A Generalizable Physics-guided Causal Model for Trajectory Prediction in Autonomous Drivingcited by 1
  4. 2025A Concept for Efficient Scalability of Automated Driving Allowing for Technical, Legal, Cultural, and Ethical Differencescited by 0
  5. 2026Unveiling Transferability in Trajectory Prediction via Latent Scene Embeddingscited by 0