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

arXiv: https://arxiv.org/abs/2103.13726 · HTML: https://mlsanity.com/paper/2103.13726/

## 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 (most cited first, 12 of 12)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents](https://mlsanity.com/paper/1704.04394/) (2017)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions](https://mlsanity.com/paper/1812.04767/) (2018)
- [An LSTM Network for Highway Trajectory Prediction](https://mlsanity.com/paper/1801.07962/) (2018)
- [Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture](https://mlsanity.com/paper/1802.06338/) (2018)
- [Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model](https://mlsanity.com/paper/1903.09381/) (2019)

## Built on by (most cited first, 21 of 21)

- [Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review](https://mlsanity.com/paper/2402.10086/) (2024)
- [Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction](https://mlsanity.com/paper/2211.08701/) (2022)
- [Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models](https://mlsanity.com/paper/2405.14384/) (2024)
- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [A Multidimensional Graph Fourier Transformation Neural Network for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2305.07416/) (2023)
- [A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective](https://mlsanity.com/paper/2312.08248/) (2023)
- [Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrations](https://mlsanity.com/paper/2412.02447/) (2024)
- [Prediction and Interpretation of Vehicle Trajectories in the Graph Spectral Domain](https://mlsanity.com/paper/2309.16702/) (2023)
- [A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research](https://mlsanity.com/paper/2410.07066/) (2024)
- [Optimization and Interpretability of Graph Attention Networks for Small Sparse Graph Structures in Automotive Applications](https://mlsanity.com/paper/2305.16196/) (2023)
- [Interpretable Goal-Based model for Vehicle Trajectory Prediction in Interactive Scenarios](https://mlsanity.com/paper/2308.04312/) (2023)
- [FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction](https://mlsanity.com/paper/2411.16747/) (2024)
- [Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-based Analysis](https://mlsanity.com/paper/2506.02599/) (2025)
- [Crossfusor: A Cross-Attention Transformer Enhanced Conditional Diffusion Model for Car-Following Trajectory Prediction](https://mlsanity.com/paper/2406.11941/) (2024)
- [Dynamic Risk Assessment for Autonomous Vehicles from Spatio-Temporal Probabilistic Occupancy Heatmaps](https://mlsanity.com/paper/2501.16480/) (2025)
- [Context-Aware Target Classification with Hybrid Gaussian Process prediction for Cooperative Vehicle Safety systems](https://mlsanity.com/paper/2212.12819/) (2022)
- [Traj-LLM: A New Exploration for Empowering Trajectory Prediction with Pre-trained Large Language Models](https://mlsanity.com/paper/2405.04909/) (2024)
- [Controllable Generative Trajectory Prediction via Weak Preference Alignment](https://mlsanity.com/paper/2510.10731/) (2025)
- [Uncertainty-Aware Diffusion Model for Multimodal Highway Trajectory Prediction via DDIM Sampling](https://mlsanity.com/paper/2602.21319/) (2026)
- [Learn to Quantify Social Interaction with Constraints for Pedestrian Walking](https://mlsanity.com/paper/2606.17897/) (2026)
- [Evaluating Physical Consistency and Plausibility in Generative Scenario Models for Autonomous Driving](https://mlsanity.com/paper/2610.01581/) (2026)

## 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.