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 …
Automatic fabric defect detection based on an improved YOLOv5
R Jin, Q Niu - Mathematical Problems in Engineering, 2021 - Wiley Online Library
Fabric defect detection is particularly remarkable because of the large textile production
demand in China. Traditional manual detection method is inefficient, time‐consuming …
demand in China. Traditional manual detection method is inefficient, time‐consuming …
Continual learning-based trajectory prediction with memory augmented networks
Forecasting pedestrian trajectories is widely used in mobile agents such as self-driving
vehicles and social robots. Deep neural network-based trajectory prediction models …
vehicles and social robots. Deep neural network-based trajectory prediction models …
Long-short term spatio-temporal aggregation for trajectory prediction
C Yang, Z Pei - IEEE Transactions on Intelligent Transportation …, 2023 - ieeexplore.ieee.org
Pedestrian trajectory prediction in crowd scenes plays a significant role in intelligent
transportation systems. The main challenges are manifested in learning motion patterns and …
transportation systems. The main challenges are manifested in learning motion patterns and …
SSAGCN: social soft attention graph convolution network for pedestrian trajectory prediction
P Lv, W Wang, Y Wang, Y Zhang… - IEEE transactions on …, 2023 - ieeexplore.ieee.org
Pedestrian trajectory prediction is an important technique of autonomous driving. In order to
accurately predict the reasonable future trajectory of pedestrians, it is inevitable to consider …
accurately predict the reasonable future trajectory of pedestrians, it is inevitable to consider …
STGlow: A flow-based generative framework with dual-graphormer for pedestrian trajectory prediction
The pedestrian trajectory prediction task is an essential component of intelligent systems. Its
applications include but are not limited to autonomous driving, robot navigation, and …
applications include but are not limited to autonomous driving, robot navigation, and …
Meta-IRLSOT++: A meta-inverse reinforcement learning method for fast adaptation of trajectory prediction networks
Recent research on pedestrian trajectory prediction based on deep learning has made
significant progress. However, the previous methods do not deeply explore the relationship …
significant progress. However, the previous methods do not deeply explore the relationship …
A multi-task learning network with a collision-aware graph transformer for traffic-agents trajectory prediction
It is critical for autonomous vehicles to accurately forecast the future trajectories of
surrounding agents to avoid collisions. However, capturing the complex interactions …
surrounding agents to avoid collisions. However, capturing the complex interactions …
DPCIAN: A novel dual-channel pedestrian crossing intention anticipation network
The increase in car ownership has improved the convenience of people's travel, but it has
also increased the potential risk of pedestrian-vehicle conflicts. In complex traffic scenarios …
also increased the potential risk of pedestrian-vehicle conflicts. In complex traffic scenarios …
Dynamic attention-based CVAE-GAN for pedestrian trajectory prediction
Z Zhou, G Huang, Z Su, Y Li… - IEEE Robotics and …, 2022 - ieeexplore.ieee.org
Predicting pedestrian trajectories is crucial for human-interactive systems. This task is
compounded by the inherently multimodal nature of human motions and complex external …
compounded by the inherently multimodal nature of human motions and complex external …