mlsanityData to 7 Oct 2026

Causal-based Time Series Domain Generalization for Vehicle Intention Prediction

arXiv 2112.02093 ↗First posted 3 Dec 2021cs.LG cs.RO stat.MLcited by 17

Not on the map. Why?

Abstract (arXiv)

Accurately predicting possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically changing environments, they are expected to make accurate predictions regardless of where they are and what driving circumstances they encountered. Therefore, generalization capability to unseen domains is crucial for prediction models when autonomous vehicles are deployed in the real world. In this paper, we aim to address the domain generalization problem for vehicle intention prediction tasks and a causal-based time series domain generalization (CTSDG) model is proposed. We construct a structural causal model for vehicle intention prediction tasks to learn an invariant representation of input driving data for domain generalization. We further integrate a recurrent latent variable model into our structural causal model to better capture temporal latent dependencies from time-series input data. The effectiveness of our approach is evaluated via real-world driving data. We demonstrate that our proposed method has consistent improvement on prediction accuracy compared to other state-of-the-art domain generalization and behavior prediction methods.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 38 of its 62 references that are in the map. First posted on arXiv 3 Dec 2021.

Builds on

38 of 62 references.

References · 38

  1. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  3. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  4. 2019Invariant Risk Minimizationcited by 1,832
  5. 2016Concrete Problems in AI Safetycited by 1,582
  6. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  7. 2017Deeper, Broader and Artier Domain Generalizationcited by 983
  8. 2017Learning to Generalize: Meta-Learning for Domain Generalizationcited by 762
  9. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
  10. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 503
  11. 2018Generalizing to Unseen Domains via Adversarial Data Augmentationcited by 451
  12. 2019Domain Generalization by Solving Jigsaw Puzzlescited by 449
26 more
  1. 2015A Recurrent Latent Variable Model for Sequential Datacited by 362
  2. 2017A simple neural network module for relational reasoningcited by 345
  3. 2020TNT: Target-driveN Trajectory Predictioncited by 345
  4. 2018Neural Relational Inference for Interacting Systemscited by 339
  5. 2017Unified Deep Supervised Domain Adaptation and Generalizationcited by 322
  6. 2017DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agentscited by 310
  7. 2019CoverNet: Multimodal Behavior Prediction using Trajectory Setscited by 259
  8. 2015Domain Generalization for Object Recognition with Multi-task Autoencoderscited by 253
  9. 2019INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Mapscited by 231
  10. 2020Domain Generalization using Causal Matchingcited by 224
  11. 2019PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settingscited by 178
  12. 2021IntentNet: Learning to Predict Intention from Raw Sensor Datacited by 158
  13. 2020Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?cited by 139
  14. 2018TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agentscited by 111
  15. 2018Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMscited by 77
  16. 2020Learning Causal Semantic Representation for Out-of-Distribution Predictioncited by 60
  17. 2019Structured agents for physical constructioncited by 50
  18. 2018Probabilistic Prediction of Vehicle Semantic Intention and Motioncited by 31
  19. 2018Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networkscited by 29
  20. 2019Alleviating Privacy Attacks via Causal Learningcited by 19
  21. 2020Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Predictioncited by 17
  22. 2019Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Modelcited by 13
  23. 2018A Framework for Probabilistic Generic Traffic Scene Predictioncited by 6
  24. 2019Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviorscited by 6
  25. 2019Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systemscited by 6
  26. 2020Explicit Domain Adaptation with Loosely Coupled Samplescited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 3; 2024: 4; 2025: 4; 2026: 1.

17 papers.

Sort:

By area:

  1. 2022Behavioral Intention Prediction in Driving Scenes: A Surveycited by 30
  2. 2025Out-of-Distribution Generalization in Time Series: A Surveycited by 15
  3. 2023Deep Transfer Learning for Intelligent Vehicle Perception: a Surveycited by 10
  4. 2024TimeLDM: Latent Diffusion Model for Unconditional Time Series Generationcited by 10
  5. 2022DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shiftcited by 6
  6. 2022A Comprehensive Review of Trends, Applications and Challenges In Out-of-Distribution Detectioncited by 5
  7. 2022Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarioscited by 5
  8. 2024Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approachcited by 5
  9. 2025Learning Time-Aware Causal Representation for Model Generalization in Evolving Domainscited by 5
  10. 2022Analyzing and Enhancing Closed-loop Stability in Reactive Simulationcited by 4
  11. 2023Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representationscited by 4
  12. 2024MetaTra: Meta-Learning for Generalized Trajectory Prediction in Unseen Domaincited by 2
5 more
  1. 2025Moment Alignment: Unifying Gradient and Hessian Matching for Domain Generalizationcited by 2
  2. 2023SE-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapeletscited by 1
  3. 2025CaTFormer: Causal Temporal Transformer with Dynamic Contextual Fusion for Driving Intention Predictioncited by 1
  4. 2024Learning Latent Spaces for Domain Generalization in Time Series Forecastingcited by 0
  5. 2026Cross-Machine Anomaly Detection Leveraging Pre-trained Time-series Modelcited by 0