Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration

TL;DR

This paper introduces a graph convolutional network framework for probabilistic acceleration prediction of vehicles, integrating RNNs to improve temporal modeling, and demonstrates superior performance over existing methods in simulation studies.

Contribution

The paper presents a novel graph-based acceleration prediction framework that combines GCNs and RNNs for improved traffic trajectory forecasting.

Findings

Proposed networks outperform state-of-the-art methods.

Integration of RNNs enhances temporal prediction accuracy.

Framework effectively models vehicle acceleration distributions.

Abstract

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.