# Vehicle trajectory prediction in top-view image sequences based on deep learning method

arXiv: https://arxiv.org/abs/2102.01749 · HTML: https://mlsanity.com/paper/2102.01749/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 2 Feb 2021.

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

- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks](https://mlsanity.com/paper/1809.10732/) (2018)
- [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)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving](https://mlsanity.com/paper/1808.05819/) (2018)
- [An LSTM Network for Highway Trajectory Prediction](https://mlsanity.com/paper/1801.07962/) (2018)
- [Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs](https://mlsanity.com/paper/1805.05499/) (2018)
- [GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/1907.07792/) (2019)
- [Graph Neural Networks for Modelling Traffic Participant Interaction](https://mlsanity.com/paper/1903.01254/) (2019)
- [Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review](https://mlsanity.com/paper/1912.11676/) (2019)
- [Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling](https://mlsanity.com/paper/1705.08781/) (2017)
- [Long-Term Occupancy Grid Prediction Using Recurrent Neural Networks](https://mlsanity.com/paper/1809.03782/) (2018)
- [Stage-wise Conservative Linear Bandits](https://mlsanity.com/paper/2010.00081/) (2020)
- [Multi-Step Prediction of Occupancy Grid Maps with Recurrent Neural Networks](https://mlsanity.com/paper/1812.09395/) (2018)
- [Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks](https://mlsanity.com/paper/1906.02815/) (2019)
- [A Random Finite Set Approach for Dynamic Occupancy Grid Maps with Real-Time Application](https://mlsanity.com/paper/1605.02406/) (2016)
- [Survey on Vision-based Path Prediction](https://mlsanity.com/paper/1811.00233/) (2018)
- [Online Vehicle Trajectory Prediction using Policy Anticipation Network and Optimization-based Context Reasoning](https://mlsanity.com/paper/1903.00847/) (2019)
- [Mobility-Aware Electric Vehicle Fast Charging Load Models with Geographical Price Variations](https://mlsanity.com/paper/1811.08582/) (2018)
- [Data-Driven Vehicle Trajectory Forecasting](https://mlsanity.com/paper/1902.05400/) (2019)

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

- [Vehicle Trajectory Prediction on Highways Using Bird Eye View Representations and Deep Learning](https://mlsanity.com/paper/2207.01407/) (2022)
- [From Observation to Prediction: LSTM for Vehicle Lane Change Forecasting on Highway On/Off-Ramps](https://mlsanity.com/paper/2601.14848/) (2026)

## Abstract (arXiv)

Annually, a large number of injuries and deaths around the world are related to motor vehicle accidents. This value has recently been reduced to some extent, via the use of driver-assistance systems. Developing driver-assistance systems (i.e., automated driving systems) can play a crucial role in reducing this number. Estimating and predicting surrounding vehicles' movement is essential for an automated vehicle and advanced safety systems. Moreover, predicting the trajectory is influenced by numerous factors, such as drivers' behavior during accidents, history of the vehicle's movement and the surrounding vehicles, and their position on the traffic scene. The vehicle must move over a safe path in traffic and react to other drivers' unpredictable behaviors in the shortest time. Herein, to predict automated vehicles' path, a model with low computational complexity is proposed, which is trained by images taken from the road's aerial image. Our method is based on an encoder-decoder model that utilizes a social tensor to model the effect of the surrounding vehicles' movement on the target vehicle. The proposed model can predict the vehicle's future path in any freeway only by viewing the images related to the history of the target vehicle's movement and its neighbors. Deep learning was used as a tool for extracting the features of these images. Using the HighD database, an image dataset of the road's aerial image was created, and the model's performance was evaluated on this new database. We achieved the RMSE of 1.91 for the next 5 seconds and found that the proposed method had less error than the best path-prediction methods in previous studies.