Federated learning for connected and automated vehicles: A survey of existing approaches and challenges
Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …
(CAV), including perception, planning, and control. However, its reliance on vehicular data …
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 …
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 …
Behavioral intention prediction in driving scenes: A survey
In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …
Multimodal manoeuvre and trajectory prediction for automated driving on highways using transformer networks
S Mozaffari, MA Sormoli, K Koufos… - IEEE Robotics and …, 2023 - ieeexplore.ieee.org
Predicting the behaviour (ie, manoeuvre/trajectory) of other road users, including vehicles, is
critical for the safe and efficient operation of autonomous vehicles (AVs), aka, automated …
critical for the safe and efficient operation of autonomous vehicles (AVs), aka, automated …
Rethinking integration of prediction and planning in deep learning-based automated driving systems: a review
Automated driving has the potential to revolutionize personal, public, and freight mobility.
Besides the enormous challenge of perception, ie accurately perceiving the environment …
Besides the enormous challenge of perception, ie accurately perceiving the environment …
From prediction to planning with goal conditioned lane graph traversals
The field of motion prediction for automated driving has seen tremendous progress recently,
bearing ever-more mighty neural network architectures. Leveraging these powerful models …
bearing ever-more mighty neural network architectures. Leveraging these powerful models …
Vulnerable road user trajectory prediction for autonomous driving using a data-driven integrated approach
H Chen, Y Liu, C Hu, X Zhang - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In this paper, Vulnerable Road User (VRU) trajectory prediction for autonomous driving
based on the Intention-Attention-Gate Recurrent Unit (IA-GRU), Improved Social Force …
based on the Intention-Attention-Gate Recurrent Unit (IA-GRU), Improved Social Force …
Envclus*: Extracting common pathways for effective vessel trajectory forecasting
The task of accurately forecasting the trajectory of a vessel, and in general a moving object
operating in free space until its destination remains an open challenge. This paper …
operating in free space until its destination remains an open challenge. This paper …
A physical law constrained deep learning model for vehicle trajectory prediction
Vehicle trajectory prediction is crucial and indispensable for ensuring the safe and efficient
operation of autonomous vehicles in complex traffic environments. The application of …
operation of autonomous vehicles in complex traffic environments. The application of …