Papers with Code 아카이브
Trajectory Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 32
- 보존된 평가 기록
- 132
Trajectory Prediction is the problem of predicting the short-term (1-3 seconds) and long-term (3-5 seconds) spatial coordinates of various road-agents such as cars, buses, pedestrians, rickshaws, and animals, etc. These road-agents have different dynamic behaviors that may correspond to aggressive or conservative driving styles.
Source: Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/SeongjuLee/DCENet-PyTorch
Exploring Dynamic Context for Multi-path Trajectory Prediction
https://github.com/SysCV/qd-3dt
https://github.com/ThomasDupiereux/ADLproject
https://github.com/ThomasDupiereux/EnGAN
https://github.com/ThomasDupiereux/hpgan
https://github.com/Tsinghua-MARS-Lab/M2I
M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction
https://github.com/YQRickWang/Motion_Prediction
https://github.com/YQRickWang/tf
https://github.com/YiZhou-H/Trajectory-Mamba-CVPR
Trajectory Mamba: Efficient Attention-Mamba Forecasting Model Based on Selective SSM
https://github.com/YuejiangLIU/social-nce-trajectron-plus-plus
Social NCE: Contrastive Learning of Socially-aware Motion Representations
https://github.com/ZiyiLiubird/GRIP_Plus_Plus
GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving
https://github.com/amiryanj/socialways
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/aras62/PIE
https://github.com/aras62/PIEPredict
https://github.com/argoverse/argoverse-api
https://github.com/azyner/radip
Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networks
https://github.com/bouracha/Gen_Motion
Learning Trajectory Dependencies for Human Motion Prediction
https://github.com/cmubig/SPEC
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/cmubig/socialAttention
https://github.com/code-implementation1/Code9/tree/main/sgcn
SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction
https://github.com/continental/kiwissen-bayesian-trajectory-prediction
CoverNet: Multimodal Behavior Prediction using Trajectory Sets
https://github.com/cookbook-ms/learning_on_scs
Principled Simplicial Neural Networks for Trajectory Prediction
https://github.com/daeheepark/PathPredictNusc
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
https://github.com/ebarsoum/hpgan
https://github.com/enriccorona/human-motion-prediction-pytorch
https://github.com/eth-ait/spl
https://github.com/facebookresearch/QuaterNet
https://github.com/flxai/soft-brownian-offset
Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoders
https://github.com/garroud/human-motion-prediction-pytorch
https://github.com/gauraviiita/Visualization-of-Human3.6M-Dataset
https://github.com/govvijaycal/confidence_aware_predictions
MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction
https://github.com/harshayugirase/human-path-prediction
It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
https://github.com/henry1iu/tnt-trajectory-prediction
https://github.com/henry1iu/tnt-trajectory-predition
https://github.com/huang-xx/STGAT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.