# Vehicle Trajectory Prediction on Highways Using Bird Eye View Representations and Deep Learning

arXiv: https://arxiv.org/abs/2207.01407 · HTML: https://mlsanity.com/paper/2207.01407/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 4 Jul 2022.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (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)
- [INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps](https://mlsanity.com/paper/1910.03088/) (2019)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [An LSTM Network for Highway Trajectory Prediction](https://mlsanity.com/paper/1801.07962/) (2018)
- [PiP: Planning-informed Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2003.11476/) (2020)
- [Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs](https://mlsanity.com/paper/1805.05499/) (2018)
- [Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network](https://mlsanity.com/paper/1704.07049/) (2017)
- [Multi-Head Attention based Probabilistic Vehicle Trajectory Prediction](https://mlsanity.com/paper/2004.03842/) (2020)
- [A Framework for Probabilistic Generic Traffic Scene Prediction](https://mlsanity.com/paper/1810.12506/) (2018)
- [Vehicle Trajectory Prediction in Crowded Highway Scenarios Using Bird Eye View Representations and CNNs](https://mlsanity.com/paper/2008.11493/) (2020)
- [Vehicle trajectory prediction in top-view image sequences based on deep learning method](https://mlsanity.com/paper/2102.01749/) (2021)

## Built on by (most cited first, 6 of 6)

- [RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2405.00449/) (2024)
- [A POV-based Highway Vehicle Trajectory Dataset and Prediction Architecture](https://mlsanity.com/paper/2303.06202/) (2023)
- [Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2303.02880/) (2023)
- [VegaEdge: Edge AI Confluence Anomaly Detection for Real-Time Highway IoT-Applications](https://mlsanity.com/paper/2311.07880/) (2023)
- [Temporal Enhanced Floating Car Observers](https://mlsanity.com/paper/2403.03825/) (2024)
- [A Multimodal Architecture for Endpoint Position Prediction in Team-based Multiplayer Games](https://mlsanity.com/paper/2507.20670/) (2025)

## Abstract (arXiv)

This work presents a novel method for predicting vehicle trajectories in highway scenarios using efficient bird's eye view representations and convolutional neural networks. Vehicle positions, motion histories, road configuration, and vehicle interactions are easily included in the prediction model using basic visual representations. The U-net model has been selected as the prediction kernel to generate future visual representations of the scene using an image-to-image regression approach. A method has been implemented to extract vehicle positions from the generated graphical representations to achieve subpixel resolution. The method has been trained and evaluated using the PREVENTION dataset, an on-board sensor dataset. Different network configurations and scene representations have been evaluated. This study found that U-net with 6 depth levels using a linear terminal layer and a Gaussian representation of the vehicles is the best performing configuration. The use of lane markings was found to produce no improvement in prediction performance. The average prediction error is 0.47 and 0.38 meters and the final prediction error is 0.76 and 0.53 meters for longitudinal and lateral coordinates, respectively, for a predicted trajectory length of 2.0 seconds. The prediction error is up to 50% lower compared to the baseline method.