Deep learning-based vehicle behavior prediction for autonomous driving applications: A review
Behaviour prediction function of an autonomous vehicle predicts the future states of the
nearby vehicles based on the current and past observations of the surrounding environment …
nearby vehicles based on the current and past observations of the surrounding environment …
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 …
AI-TP: Attention-based interaction-aware trajectory prediction for autonomous driving
K Zhang, L Zhao, C Dong, L Wu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Despite the advancements in the technologies of autonomous driving, it is still challenging to
study the safety of a self-driving vehicle. Trajectory prediction is one core function of an …
study the safety of a self-driving vehicle. Trajectory prediction is one core function of an …
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 …
Trajectory prediction for autonomous driving using spatial-temporal graph attention transformer
K Zhang, X Feng, L Wu, Z He - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
For autonomous vehicles driving on roads, future trajectories of surrounding traffic agents
(eg, vehicles, bicycles, pedestrians) are essential information. The prediction of future …
(eg, vehicles, bicycles, pedestrians) are essential information. The prediction of future …
ADCT-Net: Adaptive traffic forecasting neural network via dual-graphic cross-fused transformer
The rapid development of road traffic networks has provided a wealth of research data for
intelligent transportation systems. We are faced with vast high-dimensional traffic flow data …
intelligent transportation systems. We are faced with vast high-dimensional traffic flow data …
Hybrid deep learning models for traffic prediction in large-scale road networks
Traffic prediction is an important component in Intelligent Transportation Systems (ITSs) for
enabling advanced transportation management and services to address worsening traffic …
enabling advanced transportation management and services to address worsening traffic …
Multiple dynamic graph based traffic speed prediction method
Traffic speed prediction is a crucial and challenging task for intelligent transportation
systems. The prediction task can be accomplished via graph neural networks with structured …
systems. The prediction task can be accomplished via graph neural networks with structured …