# Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration

arXiv: https://arxiv.org/abs/1911.09837 · HTML: https://mlsanity.com/paper/1911.09837/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

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

- [Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks](https://mlsanity.com/paper/2308.07439/) (2023)
- [A Knowledge-Informed Deep Learning Paradigm for Generalizable and Stability-Optimized Car-Following Models](https://mlsanity.com/paper/2504.14241/) (2025)
- [Vehicle Acceleration Prediction Considering Environmental Influence and Individual Driving Behavior](https://mlsanity.com/paper/2504.04159/) (2025)

## Abstract (arXiv)

The ability to model and predict ego-vehicle's surrounding traffic is crucial for autonomous pilots and intelligent driver-assistance systems. Acceleration prediction is important as one of the major components of traffic prediction. This paper proposes novel approaches to the acceleration prediction problem. By representing spatial relationships between vehicles with a graph model, we build a generalized acceleration prediction framework. This paper studies the effectiveness of proposed Graph Convolution Networks, which operate on graphs predicting the acceleration distribution for vehicles driving on highways. We further investigate prediction improvement through integrating of Recurrent Neural Networks to disentangle the temporal complexity inherent in the traffic data. Results from simulation studies using comprehensive performance metrics support the conclusion that our proposed networks outperform state-of-the-art methods in generating realistic trajectories over a prediction horizon.