Trajectory Forecasting
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Trajectory forecasting is a sequential prediction task, where a forecasting model predicts future trajectories of all moving agents (humans, vehicles, etc.) in a scene, based on their past trajectories and/or the scene context.
(Illustrative figure from Social NCE: Contrastive Learning of Socially-aware Motion Representations)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CIA-Oceanix/TrAISformer
공식 코드https://github.com/ChenLiu-1996/ImageFlowNet
공식 코드https://github.com/FGiuliari/Trajectory-Transformer
공식 코드https://github.com/HarshayuGirase/PECNet
공식 코드It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
https://github.com/JasonMa2016/LDS
공식 코드Likelihood-Based Diverse Sampling for Trajectory Forecasting
https://github.com/JosephGesnouin/Asymmetrical-Bi-RNNs-to-encode-pedestrian-trajectories
공식 코드https://github.com/JunweiLiang/Multiverse
공식 코드The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction
https://github.com/JunweiLiang/Multiverse/tree/master/SimAug
공식 코드https://github.com/Khrylx/AgentFormer
공식 코드AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting
https://github.com/StanfordASL/MATS
공식 코드MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control
https://github.com/StanfordASL/Trajectron
공식 코드The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs
https://github.com/StanfordASL/Trajectron-plus-plus
공식 코드Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
https://github.com/TeCSAR-UNCC/Pishgu
공식 코드Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems
https://github.com/abduallahmohamed/Social-STGCNN
공식 코드https://github.com/agrimgupta92/sgan
공식 코드Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/aimagelab/lrvq
공식 코드Trajectory Forecasting through Low-Rank Adaptation of Discrete Latent Codes
https://github.com/air-thu/dair-v2x-seq
공식 코드https://github.com/alafage/hlt-ens
공식 코드Hierarchical Light Transformer Ensembles for Multimodal Trajectory Forecasting
https://github.com/alexmonti19/dagnet
공식 코드DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting
https://github.com/alfredgu001324/MapBEVPrediction
공식 코드Accelerating Online Mapping and Behavior Prediction via Direct BEV Feature Attention
https://github.com/alfredgu001324/mapuncertaintyprediction
공식 코드Producing and Leveraging Online Map Uncertainty in Trajectory Prediction
https://github.com/argoai/argoverse-api
공식 코드https://github.com/av-lab/emt-dataset
공식 코드EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving in the Arab Gulf Region
https://github.com/borisivanovic/planningawareevaluation
공식 코드Injecting Planning-Awareness into Prediction and Detection Evaluation
https://github.com/chenfengxu714/pretram
공식 코드PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map
https://github.com/chuhengwei/ki_gan
공식 코드https://github.com/cmubig/t2fpv
공식 코드https://github.com/coolsunxu/sophie
공식 코드SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints
https://github.com/crowdbotp/OpenTraj
공식 코드OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets
https://github.com/cvml-cfu/ecam
공식 코드ECAM: A Contrastive Learning Approach to Avoid Environmental Collision in Trajectory Forecasting
https://github.com/d1024choi/hlstrajforecast
공식 코드Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting
https://github.com/dendorferpatrick/quovadis
공식 코드Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking?
https://github.com/ebekkers/ponita
공식 코드Fast, Expressive SE$(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space
https://github.com/ericaweng/joint-metrics-matter
공식 코드Joint Metrics Matter: A Better Standard for Trajectory Forecasting
https://github.com/fengzicai/awesomewmad
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.