# Optimizing Ego Vehicle Trajectory Prediction: The Graph Enhancement Approach

arXiv: https://arxiv.org/abs/2312.13104 · HTML: https://mlsanity.com/paper/2312.13104/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)](https://mlsanity.com/paper/2004.04523/) (2020)
- [Adaptive Trajectory Prediction via Transferable GNN](https://mlsanity.com/paper/2203.05046/) (2022)
- [Graph Neural Networks: Taxonomy, Advances and Trends](https://mlsanity.com/paper/2012.08752/) (2020)
- [Graph Positional and Structural Encoder](https://mlsanity.com/paper/2307.07107/) (2023)
- [SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization](https://mlsanity.com/paper/2209.02109/) (2022)
- [Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Model](https://mlsanity.com/paper/1910.04803/) (2019)
- [An Explainer for Temporal Graph Neural Networks](https://mlsanity.com/paper/2209.00807/) (2022)
- [Long Short-term Memory RNN](https://mlsanity.com/paper/2105.06756/) (2021)
- [Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification](https://mlsanity.com/paper/2002.00514/) (2020)
- [Long-term Prediction of Vehicle Behavior using Short-term Uncertainty-aware Trajectories and High-definition Maps](https://mlsanity.com/paper/2003.06143/) (2020)
- [Transformer based trajectory prediction](https://mlsanity.com/paper/2112.04350/) (2021)
- [Navigating Uncertainty: The Role of Short-Term Trajectory Prediction in Autonomous Vehicle Safety](https://mlsanity.com/paper/2307.05288/) (2023)
- [Energy Disaggregation via Deep Temporal Dictionary Learning](https://mlsanity.com/paper/1809.03534/) (2018)
- [Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process](https://mlsanity.com/paper/1910.08102/) (2019)

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

- [MapsTP: HD Map Images Based Multimodal Trajectory Prediction for Automated Vehicles](https://mlsanity.com/paper/2407.05811/) (2024)

## Abstract (arXiv)

Predicting the trajectory of an ego vehicle is a critical component of autonomous driving systems. Current state-of-the-art methods typically rely on Deep Neural Networks (DNNs) and sequential models to process front-view images for future trajectory prediction. However, these approaches often struggle with perspective issues affecting object features in the scene. To address this, we advocate for the use of Bird's Eye View (BEV) perspectives, which offer unique advantages in capturing spatial relationships and object homogeneity. In our work, we leverage Graph Neural Networks (GNNs) and positional encoding to represent objects in a BEV, achieving competitive performance compared to traditional DNN-based methods. While the BEV-based approach loses some detailed information inherent to front-view images, we balance this by enriching the BEV data by representing it as a graph where relationships between the objects in a scene are captured effectively.