# Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture

arXiv: https://arxiv.org/abs/1802.06338 · HTML: https://mlsanity.com/paper/1802.06338/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2018.

## Built on by (most cited first, 30 of 55)

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- [CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning](https://mlsanity.com/paper/2106.13364/) (2021)
- [Variational Autoencoder-Based Vehicle Trajectory Prediction with an Interpretable Latent Space](https://mlsanity.com/paper/2103.13726/) (2021)
- [Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance](https://mlsanity.com/paper/2103.14231/) (2021)
- [Transferable and Adaptable Driving Behavior Prediction](https://mlsanity.com/paper/2202.05140/) (2022)
- [Bifurcations and loss jumps in RNN training](https://mlsanity.com/paper/2310.17561/) (2023)
- [Anomaly Detection in Multi-Agent Trajectories for Automated Driving](https://mlsanity.com/paper/2110.07922/) (2021)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [NetTraj: A Network-based Vehicle Trajectory Prediction Model with Directional Representation and Spatiotemporal Attention Mechanisms](https://mlsanity.com/paper/2106.11175/) (2021)
- [Uncertainty estimation for Cross-dataset performance in Trajectory prediction](https://mlsanity.com/paper/2205.07310/) (2022)
- [Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior](https://mlsanity.com/paper/2211.01294/) (2022)
- [HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling](https://mlsanity.com/paper/2110.02344/) (2021)
- [Uncertainty estimation of pedestrian future trajectory using Bayesian approximation](https://mlsanity.com/paper/2205.01887/) (2022)
- [Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks](https://mlsanity.com/paper/2309.01981/) (2023)
- [Connected and Automated Vehicles in Mixed-Traffic: Learning Human Driver Behavior for Effective On-Ramp Merging](https://mlsanity.com/paper/2304.00397/) (2023)
- [Explaining RL Decisions with Trajectories](https://mlsanity.com/paper/2305.04073/) (2023)
- [Online Adaptation of Neural Network Models by Modified Extended Kalman Filter for Customizable and Transferable Driving Behavior Prediction](https://mlsanity.com/paper/2112.06129/) (2021)
- [Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarios](https://mlsanity.com/paper/2306.05478/) (2023)
- [Goal-based Neural Physics Vehicle Trajectory Prediction Model](https://mlsanity.com/paper/2409.15182/) (2024)
- [Time Series Analysis and Modeling to Forecast: a Survey](https://mlsanity.com/paper/2104.00164/) (2021)
- [Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting](https://mlsanity.com/paper/2111.13324/) (2021)
- [Pedestrian Trajectory Prediction via Spatial Interaction Transformer Network](https://mlsanity.com/paper/2112.06624/) (2021)
- [Maneuver-Aware Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2104.14079/) (2021)
- [A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field](https://mlsanity.com/paper/2309.10948/) (2023)
- [Towards Safe Autonomy in Hybrid Traffic: Detecting Unpredictable Abnormal Behaviors of Human Drivers via Information Sharing](https://mlsanity.com/paper/2309.16716/) (2023)
- [GRANP: A Graph Recurrent Attentive Neural Process Model for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2404.08004/) (2024)
- [Multi-scale Temporal Fusion Transformer for Incomplete Vehicle Trajectory Prediction](https://mlsanity.com/paper/2409.00904/) (2024)
- [Detecting Invariant Manifolds in ReLU-Based RNNs](https://mlsanity.com/paper/2510.03814/) (2025)
- [Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue](https://mlsanity.com/paper/2207.02194/) (2022)

## Abstract (arXiv)

In this paper, we propose a deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time. We employ the encoder-decoder architecture which analyzes the pattern underlying in the past trajectory using the long short-term memory (LSTM) based encoder and generates the future trajectory sequence using the LSTM based decoder. This structure produces the K most likely trajectory candidates over occupancy grid map by employing the beam search technique which keeps the K locally best candidates from the decoder output. The experiments conducted on highway traffic scenarios show that the prediction accuracy of the proposed method is significantly higher than the conventional trajectory prediction techniques.