Review of pedestrian trajectory prediction methods: Comparing deep learning and knowledge-based approaches
In crowd scenarios, predicting trajectories of pedestrians is a complex and challenging task
depending on many external factors. The topology of the scene and the interactions …
depending on many external factors. The topology of the scene and the interactions …
Pedestrian intention prediction for autonomous vehicles: A comprehensive survey
Lately, Autonomous vehicles (AV) have been gaining traction globally owing to their huge
social, economic and environmental benefits. However, the rising safety apprehensions for …
social, economic and environmental benefits. However, the rising safety apprehensions for …
A decomposition dynamic graph convolutional recurrent network for traffic forecasting
Our daily lives are greatly impacted by traffic conditions, making it essential to have accurate
predictions of traffic flow within a road network. Traffic signals used for forecasting are …
predictions of traffic flow within a road network. Traffic signals used for forecasting are …
Socialvae: Human trajectory prediction using timewise latents
Predicting pedestrian movement is critical for human behavior analysis and also for safe and
efficient human-agent interactions. However, despite significant advancements, it is still …
efficient human-agent interactions. However, despite significant advancements, it is still …
CSCNet: Contextual semantic consistency network for trajectory prediction in crowded spaces
Trajectory prediction aims to predict the movement trend of the agents like pedestrians,
bikers, vehicles. It is helpful to analyze and understand human activities in crowded spaces …
bikers, vehicles. It is helpful to analyze and understand human activities in crowded spaces …
Ptp-stgcn: pedestrian trajectory prediction based on a spatio-temporal graph convolutional neural network
J Lian, W Ren, L Li, Y Zhou, B Zhou - Applied Intelligence, 2023 - Springer
It is the prerequisite to ensure the safety of road users in traffic scenes for the application of
autonomous vehicles. Pedestrians are the main participants in traffic scenes, and …
autonomous vehicles. Pedestrians are the main participants in traffic scenes, and …
[HTML][HTML] Dual-branch spatio-temporal graph neural networks for pedestrian trajectory prediction
Pedestrian trajectory prediction is an important area in computer vision, with wide
applications in autonomous driving, robot path planning, and surveillance systems. The core …
applications in autonomous driving, robot path planning, and surveillance systems. The core …
[HTML][HTML] Under the hood of transformer networks for trajectory forecasting
Transformer Networks have established themselves as the de-facto state-of-the-art for
trajectory forecasting but there is currently no systematic study on their capability to model …
trajectory forecasting but there is currently no systematic study on their capability to model …
Tri-HGNN: Learning triple policies fused hierarchical graph neural networks for pedestrian trajectory prediction
W Zhu, Y Liu, P Wang, M Zhang, T Wang, Y Yi - Pattern Recognition, 2023 - Elsevier
In complex and dynamic urban traffic scenarios, the accurate trajectory prediction of
surrounding pedestrians with interactive behaviors plays a vital role in the self-driving …
surrounding pedestrians with interactive behaviors plays a vital role in the self-driving …
Spatio-temporal interaction aware and trajectory distribution aware graph convolution network for pedestrian multimodal trajectory prediction
Pedestrian trajectory prediction is a critical research area with numerous domains, eg, blind
navigation, autonomous driving systems, and service robots. There exist two challenges in …
navigation, autonomous driving systems, and service robots. There exist two challenges in …