# A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field

arXiv: https://arxiv.org/abs/2309.10948 · HTML: https://mlsanity.com/paper/2309.10948/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 14 of 14)

- [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)
- [Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net](https://mlsanity.com/paper/2012.12395/) (2020)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [IntentNet: Learning to Predict Intention from Raw Sensor Data](https://mlsanity.com/paper/2101.07907/) (2021)
- [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)
- [LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents](https://mlsanity.com/paper/2104.00249/) (2021)
- [Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture](https://mlsanity.com/paper/1802.06338/) (2018)
- [Graph Neural Networks for Modelling Traffic Participant Interaction](https://mlsanity.com/paper/1903.01254/) (2019)
- [Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks](https://mlsanity.com/paper/1906.02815/) (2019)
- [Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning](https://mlsanity.com/paper/1904.12374/) (2019)
- [Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks](https://mlsanity.com/paper/2109.10742/) (2021)
- [Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks](https://mlsanity.com/paper/2303.16109/) (2023)
- [A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories](https://mlsanity.com/paper/1609.03058/) (2016)

## Built on by (most cited first, 3 of 3)

- [Interpretable Interaction Modeling for Trajectory Prediction via Agent Selection and Physical Coefficient](https://mlsanity.com/paper/2405.13152/) (2024)
- [A Survey on Hybrid Motion Planning Methods for Automated Driving Systems](https://mlsanity.com/paper/2406.05575/) (2024)
- [Towards A General-Purpose Motion Planning for Autonomous Vehicles Using Fluid Dynamics](https://mlsanity.com/paper/2406.05708/) (2024)

## Abstract (arXiv)

Anticipating the motion of other road users is crucial for automated driving systems (ADS), as it enables safe and informed downstream decision-making and motion planning. Unfortunately, contemporary learning-based approaches for motion prediction exhibit significant performance degradation as the prediction horizon increases or the observation window decreases. This paper proposes a novel technique for trajectory prediction that combines a data-driven learning-based method with a velocity vector field (VVF) generated from a nature-inspired concept, i.e., fluid flow dynamics. In this work, the vector field is incorporated as an additional input to a convolutional-recurrent deep neural network to help predict the most likely future trajectories given a sequence of bird's eye view scene representations. The performance of the proposed model is compared with state-of-the-art methods on the HighD dataset demonstrating that the VVF inclusion improves the prediction accuracy for both short and long-term (5~sec) time horizons. It is also shown that the accuracy remains consistent with decreasing observation windows which alleviates the requirement of a long history of past observations for accurate trajectory prediction. Source codes are available at: https://github.com/Amir-Samadi/VVF-TP.