Vehicle trajectory prediction in connected environments via heterogeneous context-aware graph convolutional networks
The accurate trajectory prediction of surrounding vehicles is crucial for the sustainability and
safety of connected and autonomous vehicles under mixed traffic streams in the real world …
safety of connected and autonomous vehicles under mixed traffic streams in the real world …
Vehicle Trajectory Prediction with Gaussian Process Regression in Connected Vehicle Environment
This paper addresses the problem of long term location prediction for collision avoidance in
Connected Vehicle (CV) environment where more information about the road and traffic data …
Connected Vehicle (CV) environment where more information about the road and traffic data …
Multi-vehicle collaborative learning for trajectory prediction with spatio-temporal tensor fusion
Accurate behavior prediction of other vehicles in the surroundings is critical for intelligent
transportation systems. Common practices to reason about the future trajectory are through …
transportation systems. Common practices to reason about the future trajectory are through …
Topological graph convolutional network-based urban traffic flow and density prediction
With the development of modern Intelligent Transportation System (ITS), reliable and
efficient transportation information sharing becomes more and more important. Although …
efficient transportation information sharing becomes more and more important. Although …
TrajGAT: A map-embedded graph attention network for real-time vehicle trajectory imputation of roadside perception
With the increasing deployment of roadside sensors, vehicle trajectories can be collected for
driving behavior analysis and vehicle-highway automation systems. However, due to …
driving behavior analysis and vehicle-highway automation systems. However, due to …
Exploring human mobility for multi-pattern passenger prediction: A graph learning framework
Traffic flow prediction is an integral part of an intelligent transportation system and thus
fundamental for various traffic-related applications. Buses are an indispensable way of …
fundamental for various traffic-related applications. Buses are an indispensable way of …
Jointly contrastive representation learning on road network and trajectory
Road network and trajectory representation learning are essential for traffic systems since
the learned representation can be directly used in various downstream tasks (eg, traffic …
the learned representation can be directly used in various downstream tasks (eg, traffic …
Internet of vehicles: Sensing-aided transportation information collection and diffusion
In view of the emergence and rapid development of the Internet of Vehicles (IoV) and cloud
computing, intelligent transport systems are beneficial in terms of enhancing the quality and …
computing, intelligent transport systems are beneficial in terms of enhancing the quality and …
Traffic flow prediction based on deep learning in internet of vehicles
In Internet of Vehicles (IoV), accurate traffic flow prediction is helpful for analyzing road
condition and then timely feedback traffic information to managers as well as travelers …
condition and then timely feedback traffic information to managers as well as travelers …
Trajectory clustering aided personalized driver intention prediction for intelligent vehicles
Early driver intention prediction plays a significant role in intelligent vehicles. Drivers exhibit
various driving characteristics impairing the performance of conventional algorithms using …
various driving characteristics impairing the performance of conventional algorithms using …