Papers Trajectory Clustering
“Trajectory Clustering” 태그가 달린 논문 37편 · 필터 해제
Policy-Based Trajectory Clustering in Offline Reinforcement Learning
We introduce a novel task of clustering trajectories from offline reinforcement learning (RL) datasets, where each cluster center represents the policy that generated its trajectories. By leveraging the connection betwee…
ClusteringD4RLOffline RLreinforcement-learning+4Stable Trajectory Clustering: An Efficient Split and Merge Algorithm
Clustering algorithms group data points by characteristics to identify patterns. Over the past two decades, researchers have extended these methods to analyze trajectories of humans, animals, and vehicles, studying their…
ClusteringTrajectory ClusteringTrajectory-Class-Aware Multi-Agent Reinforcement Learning
In the context of multi-agent reinforcement learning, generalization is a challenge to solve various tasks that may require different joint policies or coordination without relying on policies specialized for each task. …
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningStarcraft+2Deep multi-intentional inverse reinforcement learning for cognitive multi-function radar inverse cognition
In recent years, radar systems have advanced significantly, offering environmental adaptation and multi-task capabilities. These developments pose new challenges for electronic intelligence (Elint) and electronic support…
reinforcement-learningReinforcement LearningTrajectory ClusteringA Survey of Distance-Based Vessel Trajectory Clustering: Data Pre-processing, Methodologies, Applications, and Experimental Evaluation
Vessel trajectory clustering, a crucial component of the maritime intelligent transportation systems, provides valuable insights for applications such as anomaly detection and trajectory prediction. This paper presents a…
Anomaly DetectionClusteringSurveyTrajectory Clustering+1Dance of the ADS: Orchestrating Failures through Historically-Informed Scenario Fuzzing
As autonomous driving systems (ADS) advance towards higher levels of autonomy, orchestrating their safety verification becomes increasingly intricate. This paper unveils ScenarioFuzz, a pioneering scenario-based fuzz tes…
Autonomous DrivingGraph Neural NetworkTrajectory ClusteringFast maneuver recovery from aerial observation: trajectory clustering and outliers rejection
The implementation of road user models that realistically reproduce a credible behavior in a multi-agentsimulation is still an open problem. A data-driven approach consists on to deduce behaviors that may exist in real s…
ClusteringTrajectory ClusteringUn análisis bibliométrico de la producción científica acerca del agrupamiento de trayectorias GPS
Clustering algorithms or methods for GPS trajectories are in constant evolution due to the interest aroused in part of the scientific community. With the development of clustering algorithms considered traditional, impro…
ArticlesClusteringTrajectory ClusteringPath Integral Control with Rollout Clustering and Dynamic Obstacles
Model Predictive Path Integral (MPPI) control has proven to be a powerful tool for the control of uncertain systems (such as systems subject to disturbances and systems with unmodeled dynamics). One important limitation …
ClusteringTrajectory ClusteringPredictive Clustering of Vessel Behavior Based on Hierarchical Trajectory Representation
Vessel trajectory clustering, which aims to find similar trajectory patterns, has been widely leveraged in overwater applications. Most traditional methods use predefined rules and thresholds to identify discrete vessel …
ClusteringTrajectory ClusteringDiscovering Behavioral Modes in Deep Reinforcement Learning Policies Using Trajectory Clustering in Latent Space
Understanding the behavior of deep reinforcement learning (DRL) agents is crucial for improving their performance and reliability. However, the complexity of their policies often makes them challenging to understand. In …
ClusteringDeep Reinforcement LearningDimensionality ReductionTrajectory ClusteringChoose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering
Passenger clustering based on trajectory records is essential for transportation operators. However, existing methods cannot easily cluster the passengers due to the hierarchical structure of the passenger trip informati…
ClusteringCommunity DetectionTrajectory ClusteringMulti‑camera trajectory matching based on hierarchical clustering and constraints
The fast improvement of deep learning methods resulted in breakthroughs in image classification, object detection, and object tracking. Autonomous driving and traffic monitoring systems, especially the on-premise install…
AttributeAutonomous DrivingConstrained Clusteringimage-classification+8Distribution-Based Trajectory Clustering
Trajectory clustering enables the discovery of common patterns in trajectory data. Current methods of trajectory clustering rely on a distance measure between two points in order to measure the dissimilarity between two …
ClusteringTrajectory ClusteringA Trajectory K-Anonymity Model Based on Point Density and Partition
As people's daily life becomes increasingly inseparable from various mobile electronic devices, relevant service application platforms and network operators can collect numerous individual information easily. When releas…
Trajectory ClusteringTensor Dirichlet Process Multinomial Mixture Model for Passenger Trajectory Clustering
Passenger clustering based on travel records is essential for transportation operators. However, existing methods cannot easily cluster the passengers due to the hierarchical structure of the passenger trip information, …
ClusteringTrajectory ClusteringClustering Human Mobility with Multiple Spaces
Human mobility clustering is an important problem for understanding human mobility behaviors (e.g., work and school commutes). Existing methods typically contain two steps: choosing or learning a mobility representation …
ClusteringDeep ClusteringTrajectory ClusteringTrajectory Clustering Performance Evaluation: If we know the answer, it's not clustering
Advancements in Intelligent Traffic Systems (ITS) have made huge amounts of traffic data available through automatic data collection. A big part of this data is stored as trajectories of moving vehicles and road users. A…
ClusteringTrajectory ClusteringIdentifying the module structure of swarms using a new framework of network-based time series clustering
Swarm is a collective motion phenomenon whose dynamic mechanism and cooperation structure could be identified based on observations. Unmanned Aerial Vehicles (UAV) is a special artificial swarm with unique rules and stru…
ClusteringTime SeriesTime Series AnalysisTime Series Clustering+1An Unsupervised Learning Method with Convolutional Auto-Encoder for Vessel Trajectory Similarity Computation
To achieve reliable mining results for massive vessel trajectories, one of the most important challenges is how to efficiently compute the similarities between different vessel trajectories. The computation of vessel tra…
ClusteringTrajectory Clustering