mlsanityData to 14 Oct 2026

Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space

arXiv 2103.13726 ↗First posted 25 Mar 2021cs.LGcited by 21

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Abstract (arXiv)

This paper introduces the Descriptive Variational Autoencoder (DVAE), an unsupervised and end-to-end trainable neural network for predicting vehicle trajectories that provides partial interpretability. The novel approach is based on the architecture and objective of common variational autoencoders. By introducing expert knowledge within the decoder part of the autoencoder, the encoder learns to extract latent parameters that provide a graspable meaning in human terms. Such an interpretable latent space enables the validation by expert defined rule sets. The evaluation of the DVAE is performed using the publicly available highD dataset for highway traffic scenarios. In comparison to a conventional variational autoencoder with equivalent complexity, the proposed model provides a similar prediction accuracy but with the great advantage of having an interpretable latent space. For crucial decision making and assessing trustworthiness of a prediction this property is highly desirable.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 12 of its 25 references that are in the map. First posted on arXiv 25 Mar 2021.

Builds on

12 of 25 references.

References · 12

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  2. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,416
  3. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 690
  4. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  5. 2016Tutorial on Variational Autoencoderscited by 409
  6. 2017DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agentscited by 310
  7. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 277
  8. 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
  9. 2018TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactionscited by 82
  10. 2018An LSTM Network for Highway Trajectory Predictioncited by 81
  11. 2018Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecturecited by 55
  12. 2019Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Modelcited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 5; 2024: 7; 2025: 3; 2026: 3.

21 papers.

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  1. 2024Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Reviewcited by 53
  2. 2022Interpretable Self-Aware Neural Networks for Robust Trajectory Predictioncited by 22
  3. 2024Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Modelscited by 13
  4. 2022A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directionscited by 9
  5. 2023A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Predictioncited by 8
  6. 2023A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspectivecited by 6
  7. 2024Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrationscited by 5
  8. 2023Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domaincited by 4
  9. 2024A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Researchcited by 4
  10. 2023Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applicationscited by 3
  11. 2023Interpretable Goal-Based model for Vehicle Trajectory Prediction in Interactive Scenarioscited by 3
  12. 2024FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Predictioncited by 3
9 more
  1. 2025Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-based Analysiscited by 3
  2. 2024Crossfusor: A Cross-Attention Transformer Enhanced Conditional Diffusion Model for Car-Following Trajectory Predictioncited by 2
  3. 2025Dynamic Risk Assessment for Autonomous Vehicles from Spatio-Temporal Probabilistic Occupancy Heatmapscited by 1
  4. 2022Context-Aware Target Classification with Hybrid Gaussian Process prediction for Cooperative Vehicle Safety systemscited by 0
  5. 2024Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Modelscited by 0
  6. 2025Controllable Generative Trajectory Prediction via Weak Preference Alignmentcited by 0
  7. 2026Uncertainty-Aware Diffusion Model for Multimodal Highway Trajectory Prediction via DDIM Samplingcited by 0
  8. 2026Learn to Quantify Social Interaction with Constraints for Pedestrian Walkingcited by 0
  9. 2026Evaluating Physical Consistency and Plausibility in Generative Scenario Models for Autonomous Drivingcited by 0