# Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks

arXiv: https://arxiv.org/abs/2308.07439 · HTML: https://mlsanity.com/paper/2308.07439/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 44 references that are in the map.
- First posted on arXiv 14 Aug 2023.

## Builds on (most cited first, 20 of 20)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2003.08111/) (2020)
- [LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting](https://mlsanity.com/paper/2101.06653/) (2021)
- [Imitating Driver Behavior with Generative Adversarial Networks](https://mlsanity.com/paper/1701.06699/) (2017)
- [TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions](https://mlsanity.com/paper/1812.04767/) (2018)
- [Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture](https://mlsanity.com/paper/1802.06338/) (2018)
- [How would surround vehicles move? A Unified Framework for Maneuver Classification and Motion Prediction](https://mlsanity.com/paper/1801.06523/) (2018)
- [Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning](https://mlsanity.com/paper/2010.03118/) (2020)
- [CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins](https://mlsanity.com/paper/2208.11036/) (2022)
- [Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks](https://mlsanity.com/paper/1906.02815/) (2019)
- [Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior](https://mlsanity.com/paper/2211.01294/) (2022)
- [Learning from Naturalistic Driving Data for Human-like Autonomous Highway Driving](https://mlsanity.com/paper/2005.11470/) (2020)
- [CEMFormer: Learning to Predict Driver Intentions from In-Cabin and External Cameras via Spatial-Temporal Transformers](https://mlsanity.com/paper/2305.07840/) (2023)
- [Towards Next Generation of Pedestrian and Connected Vehicle In-the-loop Research: A Digital Twin Co-Simulation Framework](https://mlsanity.com/paper/2212.05090/) (2022)
- [A Learning-Based Approach for Lane Departure Warning Systems with a Personalized Driver Model](https://mlsanity.com/paper/1702.01228/) (2017)
- [Trajectory Prediction for Vehicle Conflict Identification at Intersections Using Sequence-to-Sequence Recurrent Neural Networks](https://mlsanity.com/paper/2210.08009/) (2022)
- [GISNet: Graph-Based Information Sharing Network For Vehicle Trajectory Prediction](https://mlsanity.com/paper/2003.11973/) (2020)
- [Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration](https://mlsanity.com/paper/1911.09837/) (2019)

## Built on by (most cited first, 2 of 2)

- [KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced Multi-Vehicle Trajectory Forecasting at Signalized Intersections](https://mlsanity.com/paper/2404.11181/) (2024)
- [Investigating Personalized Driving Behaviors in Dilemma Zones: Analysis and Prediction of Stop-or-Go Decisions](https://mlsanity.com/paper/2405.03873/) (2024)

## Abstract (arXiv)

Accurate prediction of vehicle trajectories is vital for advanced driver assistance systems and autonomous vehicles. Existing methods mainly rely on generic trajectory predictions derived from large datasets, overlooking the personalized driving patterns of individual drivers. To address this gap, we propose an approach for interaction-aware personalized vehicle trajectory prediction that incorporates temporal graph neural networks. Our method utilizes Graph Convolution Networks (GCN) and Long Short-Term Memory (LSTM) to model the spatio-temporal interactions between target vehicles and their surrounding traffic. To personalize the predictions, we establish a pipeline that leverages transfer learning: the model is initially pre-trained on a large-scale trajectory dataset and then fine-tuned for each driver using their specific driving data. We employ human-in-the-loop simulation to collect personalized naturalistic driving trajectories and corresponding surrounding vehicle trajectories. Experimental results demonstrate the superior performance of our personalized GCN-LSTM model, particularly for longer prediction horizons, compared to its generic counterpart. Moreover, the personalized model outperforms individual models created without pre-training, emphasizing the significance of pre-training on a large dataset to avoid overfitting. By incorporating personalization, our approach enhances trajectory prediction accuracy.