# Smooth-Trajectron++: Augmenting the Trajectron++ behaviour prediction model with smooth attention

arXiv: https://arxiv.org/abs/2305.19678 · HTML: https://mlsanity.com/paper/2305.19678/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 15 of 15)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting](https://mlsanity.com/paper/2103.14023/) (2021)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [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)
- [Structural-RNN: Deep Learning on Spatio-Temporal Graphs](https://mlsanity.com/paper/1511.05298/) (2015)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [Social Attention: Modeling Attention in Human Crowds](https://mlsanity.com/paper/1710.04689/) (2017)
- [Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2003.08111/) (2020)
- [Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data](https://mlsanity.com/paper/2001.03093/) (2020)
- [Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior](https://mlsanity.com/paper/2003.11959/) (2020)
- [A context-aware pedestrian trajectory prediction framework for automated vehicles](https://mlsanity.com/paper/2104.08123/) (2021)
- [Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction](https://mlsanity.com/paper/2203.04421/) (2022)
- [Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios](https://mlsanity.com/paper/2211.05455/) (2022)
- [Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study](https://mlsanity.com/paper/2305.15187/) (2023)

## Built on by (most cited first, 1 of 1)

- [Controllable Generative Trajectory Prediction via Weak Preference Alignment](https://mlsanity.com/paper/2510.10731/) (2025)

## Abstract (arXiv)

Understanding traffic participants' behaviour is crucial for predicting their future trajectories, aiding in developing safe and reliable planning systems for autonomous vehicles. Integrating cognitive processes and machine learning models has shown promise in other domains but is lacking in the trajectory forecasting of multiple traffic agents in large-scale autonomous driving datasets. This work investigates the state-of-the-art trajectory forecasting model Trajectron++ which we enhance by incorporating a smoothing term in its attention module. This attention mechanism mimics human attention inspired by cognitive science research indicating limits to attention switching. We evaluate the performance of the resulting Smooth-Trajectron++ model and compare it to the original model on various benchmarks, revealing the potential of incorporating insights from human cognition into trajectory prediction models.