# Multi-Head Attention based Probabilistic Vehicle Trajectory Prediction

arXiv: https://arxiv.org/abs/2004.03842 · HTML: https://mlsanity.com/paper/2004.03842/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2020.

## Built on by (most cited first, 12 of 12)

- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks](https://mlsanity.com/paper/2309.01981/) (2023)
- [Vehicle Trajectory Prediction on Highways Using Bird Eye View Representations and Deep Learning](https://mlsanity.com/paper/2207.01407/) (2022)
- [Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning](https://mlsanity.com/paper/2409.11676/) (2024)
- [Towards Safe Autonomy in Hybrid Traffic: Detecting Unpredictable Abnormal Behaviors of Human Drivers via Information Sharing](https://mlsanity.com/paper/2309.16716/) (2023)
- [Building Real-time Awareness of Out-of-distribution in Trajectory Prediction for Autonomous Vehicles](https://mlsanity.com/paper/2409.17277/) (2024)
- [Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2509.10570/) (2025)
- [End-to-End Visual Autonomous Parking via Control-Aided Attention](https://mlsanity.com/paper/2509.11090/) (2025)
- [Emotion recognition based on multi-modal electrophysiology multi-head attention Contrastive Learning](https://mlsanity.com/paper/2308.01919/) (2023)
- [C2INet: Realizing Incremental Trajectory Prediction with Prior-Aware Continual Causal Intervention](https://mlsanity.com/paper/2411.12313/) (2024)
- [Characterized Diffusion Networks for Enhanced Autonomous Driving Trajectory Prediction](https://mlsanity.com/paper/2411.16457/) (2024)
- [Adaptive Multi-Mode Out-of-Distribution Detection for Trajectory Prediction in Autonomous Vehicles](https://mlsanity.com/paper/2509.13577/) (2025)

## Abstract (arXiv)

This paper presents online-capable deep learning model for probabilistic vehicle trajectory prediction. We propose a simple encoder-decoder architecture based on multi-head attention. The proposed model generates the distribution of the predicted trajectories for multiple vehicles in parallel. Our approach to model the interactions can learn to attend to a few influential vehicles in an unsupervised manner, which can improve the interpretability of the network. The experiments using naturalistic trajectories at highway show the clear improvement in terms of positional error on both longitudinal and lateral direction.