Social interactions for autonomous driving: A review and perspectives
No human drives a car in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can interact with other road users …
their goals in social traffic scenes. A rational human driver can interact with other road users …
Deep learning-based vehicle behavior prediction for autonomous driving applications: A review
S Mozaffari, OY Al-Jarrah, M Dianati… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
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 …
Graph-based spatial-temporal convolutional network for vehicle trajectory prediction in autonomous driving
Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and
motion planning of autonomous vehicles. This paper proposes a graph-based spatial …
motion planning of autonomous vehicles. This paper proposes a graph-based spatial …
Personalized vehicle trajectory prediction based on joint time-series modeling for connected vehicles
Motion prediction for the leading vehicle is a critical task for connected autonomous
vehicles. It provides a method to model the leading-following vehicle behavior and analysis …
vehicles. It provides a method to model the leading-following vehicle behavior and analysis …
Intensive review of drones detection and tracking: linear kalman filter versus nonlinear regression, an analysis case
RA Zitar, A Mohsen, AEF Seghrouchni… - … Methods in Engineering, 2023 - Springer
In this paper, an extensive review for objects and drones (AUVs) detection and tracking is
presented. The article presents state of the art methods used in detection and tracking of …
presented. The article presents state of the art methods used in detection and tracking of …
Online anomalous trajectory detection with deep generative sequence modeling
Detecting anomalous trajectory has become an important and fundamental concern in many
real-world applications. However, most of the existing studies 1) cannot handle the …
real-world applications. However, most of the existing studies 1) cannot handle the …
5G-enabled V2X communications for vulnerable road users safety applications: a review
Abstract Intelligent Transportation System (ITS) is continuously evolving alongside
communication technologies and autonomous driving, giving way to new applications and …
communication technologies and autonomous driving, giving way to new applications and …
SCALE-Net: Scalable vehicle trajectory prediction network under random number of interacting vehicles via edge-enhanced graph convolutional neural network
Predicting the future trajectory of surrounding vehicles in a randomly varying traffic level is
one of the most challenging problems in developing an autonomous vehicle. Since there is …
one of the most challenging problems in developing an autonomous vehicle. Since there is …
Interaction-aware trajectory prediction of connected vehicles using CNN-LSTM networks
Predicting the future trajectory of a surrounding vehicle in congested traffic is one of the
necessary abilities of an autonomous vehicle. In congestion, a vehicle's future movement is …
necessary abilities of an autonomous vehicle. In congestion, a vehicle's future movement is …
Deeptrack: Lightweight deep learning for vehicle trajectory prediction in highways
Vehicle trajectory prediction is essential for enabling safety-critical intelligent transportation
systems (ITS) applications used in management and operations. While there have been …
systems (ITS) applications used in management and operations. While there have been …