Adaptive multi-source data fusion vessel trajectory prediction model for intelligent maritime traffic
Multi-source vessel automatic identification system (AIS) data gathered in the maritime
Internet of Things (IoT) system has diverse data characteristics, such as sparse satellite data …
Internet of Things (IoT) system has diverse data characteristics, such as sparse satellite data …
Knowledge transfer in lifelong machine learning: a systematic literature review
Abstract L ifelong M achine L earning (LML) denotes a scenario involving multiple
sequential tasks, each accompanied by its respective dataset, in order to solve specific …
sequential tasks, each accompanied by its respective dataset, in order to solve specific …
Yolo-fr: A yolov5 infrared small target detection algorithm based on feature reassembly sampling method
X Mou, S Lei, X Zhou - Sensors, 2023 - mdpi.com
The loss of infrared dim-small target features in the network sampling process is a major
factor affecting its detection accuracy. In order to reduce this loss, this paper proposes YOLO …
factor affecting its detection accuracy. In order to reduce this loss, this paper proposes YOLO …
Continual Learning for Smart City: A Survey
With the digitization of modern cities, large data volumes and powerful computational
resources facilitate the rapid update of intelligent models deployed in smart cities. Continual …
resources facilitate the rapid update of intelligent models deployed in smart cities. Continual …
A multi-modal vehicle trajectory prediction framework via conditional diffusion model: A coarse-to-fine approach
Z Li, H Liang, H Wang, X Zheng, J Wang… - Knowledge-Based …, 2023 - Elsevier
Accurate prediction of the future motion of surrounding vehicles is crucial for ensuring the
safety of motion planning in autonomous vehicles. However, it is challenging to perform …
safety of motion planning in autonomous vehicles. However, it is challenging to perform …
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 …
Smemo: social memory for trajectory forecasting
Effective modeling of human interactions is of utmost importance when forecasting
behaviors such as future trajectories. Each individual, with its motion, influences surrounding …
behaviors such as future trajectories. Each individual, with its motion, influences surrounding …
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 …
Relay Hindsight Experience Replay: Self-guided continual reinforcement learning for sequential object manipulation tasks with sparse rewards
Learning with sparse rewards remains a challenging problem in reinforcement learning
(RL). In particular, for sequential object manipulation tasks, the RL agent generally only …
(RL). In particular, for sequential object manipulation tasks, the RL agent generally only …