Lane-GNN: Integrating GNN for Predicting Drivers' Lane Change Intention

TL;DR

This paper introduces Lane-GNN, a graph neural network model that accurately predicts drivers' lane change intentions within 90 seconds, enhancing traffic flow anomaly detection in VSL systems.

Contribution

The paper proposes Lane-GNN, an attention temporal graph convolutional neural network, for detecting lane change intentions, outperforming baseline models in accuracy and speed.

Findings

Lane-GNN achieves 99.42% accuracy in lane change detection.

Lane-GNN detects lane change intentions within 90 seconds.

Model interpretation methods help understand prediction mechanisms.

Abstract

Nowadays, intelligent highway traffic network is playing an important role in modern transportation infrastructures. A variable speed limit (VSL) system can be facilitated in the highway traffic network to provide useful and dynamic speed limit information for drivers to travel with enhanced safety. Such system is usually designed with a steady advisory speed in mind so that traffic can move smoothly when drivers follow the speed, rather than speeding up whenever there is a gap and slowing down at congestion. However, little attention has been given to the research of vehicles' behaviours when drivers left the road network governed by a VSL system, which may largely involve unexpected acceleration, deceleration and frequent lane changes, resulting in chaos for the subsequent highway road users. In this paper, we focus on the detection of traffic flow anomaly due to drivers' lane change intention on the highway traffic networks after a VSL system. More specifically, we apply graph modelling on the traffic flow data generated by a popular mobility simulator, SUMO, at road segment levels. We then evaluate the performance of lane changing detection using the proposed Lane-GNN scheme, an attention temporal graph convolutional neural network, and compare its performance with a temporal convolutional neural network (TCNN) as our baseline. Our experimental results show that the proposed Lane-GNN can detect drivers' lane change intention within 90 seconds with an accuracy of 99.42% under certain assumptions. Finally, some interpretation methods are applied to the trained models with a view to further illustrate our findings.