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

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.

A Survey on Graph Neural Networks for Microservice-Based Cloud Applications

“…It is worth considering applying these GNN variants to tackle the challenges arising in microservice-based applications from different perspectives. Furthermore, attention-based mechanisms have been widely applied in other application domains with strong links to GNN-empowered designs as they have demonstrated promising results in performance improvement; see papers, such as [ 23 , 49 , 72 , 73 , 74 , 75 ]. However, limited efforts have been made regarding integrating attention mechanisms with GNNs for microservice-based applications.…”

“…Graph neural networks and their variants have been applied to address graph-based learning challenges in various domains, including but not limited to, physical system modelling [ 18 , 19 ], chemical reaction prediction [ 20 ], biological disease classification [ 21 ], traffic state prediction [ 22 , 23 , 24 , 25 ], text classification [ 26 ], machine translation [ 27 ], and object detection [ 28 , 29 ].…”

“…To illustrate a few, the authors in [ 23 ] devised an attention-based temporal graph convolutional network to predict anomalous lane-changing behaviours of drivers in highway scenarios. In addition, the authors in [ 27 ] proposed an encoder–decoder architecture coupled with a gated graph neural network for graph-to-sequence learning problems arising in natural-language processing.…”