Survey on artificial intelligence (AI) techniques for vehicular ad-hoc networks (VANETs)
A Mchergui, T Moulahi, S Zeadally - Vehicular Communications, 2022 - Elsevier
Advances in communications, smart transportation systems, and computer systems have
recently opened up vast possibilities of intelligent solutions for traffic safety, convenience …
recently opened up vast possibilities of intelligent solutions for traffic safety, convenience …
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 review of vehicle lane change research
C Ma, D Li - Physica A: Statistical Mechanics and its Applications, 2023 - Elsevier
Vehicle lane change behavior, which is an important part of traffic flow theory, can have a
fundamental impact on the macro and micro characteristics of traffic flow. At the same time, it …
fundamental impact on the macro and micro characteristics of traffic flow. At the same time, it …
Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning
Driving behavior modeling is of great importance for designing safe, smart, and
personalized autonomous driving systems. In this paper, an internal reward function-based …
personalized autonomous driving systems. In this paper, an internal reward function-based …
An interacting multiple model for trajectory prediction of intelligent vehicles in typical road traffic scenario
This article presents an interacting multiple model (IMM) for short-term prediction and long-
term trajectory prediction of an intelligent vehicle. This model is based on vehicle's physics …
term trajectory prediction of an intelligent vehicle. This model is based on vehicle's physics …
AMGB: Trajectory prediction using attention-based mechanism GCN-BiLSTM in IOV
R Li, Y Qin, J Wang, H Wang - Pattern Recognition Letters, 2023 - Elsevier
Accurate and reliable prediction of vehicle trajectories is closely related to the path planning
of intelligent vehicles and contributes to intelligent transportation safety, especially in …
of intelligent vehicles and contributes to intelligent transportation safety, especially in …
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 …
Graph and recurrent neural network-based vehicle trajectory prediction for highway driving
Integrating trajectory prediction to the decision-making and planning modules of modular
autonomous driving systems is expected to improve the safety and efficiency of self-driving …
autonomous driving systems is expected to improve the safety and efficiency of self-driving …
MALS-Net: A multi-head attention-based LSTM sequence-to-sequence network for socio-temporal interaction modelling and trajectory prediction
Predicting the trajectories of surrounding vehicles is an essential task in autonomous
driving, especially in a highway setting, where minor deviations in motion can cause serious …
driving, especially in a highway setting, where minor deviations in motion can cause serious …
Group vehicle trajectory prediction with global spatio-temporal graph
D Xu, X Shang, Y Liu, H Peng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Vehicle trajectory prediction is a challenging problem in the field of autonomous driving,
which is of great significance to the safety of autonomous driving and traffic roads. In view of …
which is of great significance to the safety of autonomous driving and traffic roads. In view of …