Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
V Bharilya, N Kumar - Vehicular Communications, 2024 - Elsevier
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
A survey on trajectory-prediction methods for autonomous driving
In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network
Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential
for safe and efficient operation of connected automated vehicles under complex driving …
for safe and efficient operation of connected automated vehicles under complex driving …
A survey on graph neural networks in intelligent transportation systems
Intelligent Transportation System (ITS) is vital in improving traffic congestion, reducing traffic
accidents, optimizing urban planning, etc. However, due to the complexity of the traffic …
accidents, optimizing urban planning, etc. However, due to the complexity of the traffic …
Graph neural networks for intelligent transportation systems: A survey
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …
recent years. Owing to their power in analyzing graph-structured data, they have become …
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 …
Environment-attention network for vehicle trajectory prediction
Y Cai, Z Wang, H Wang, L Chen, Y Li… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
In vehicle trajectory prediction, the difficulty in modeling the interaction relationship between
vehicles lies in constructing the interaction structure between the vehicles in the traffic …
vehicles lies in constructing the interaction structure between the vehicles in the traffic …
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 …
Human observation-inspired trajectory prediction for autonomous driving in mixed-autonomy traffic environments
In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a
formidable challenge, especially in mixed autonomy environments. Traditional approaches …
formidable challenge, especially in mixed autonomy environments. Traditional approaches …
Toward safe and smart mobility: Energy-aware deep learning for driving behavior analysis and prediction of connected vehicles
Connected automated driving technologies have shown tremendous improvement in recent
years. However, it is still not clear how driving behaviors and energy consumption correlate …
years. However, it is still not clear how driving behaviors and energy consumption correlate …