https://github.com/zhumorui/m3cad
공식 코드M3CAD: Towards Generic Cooperative Autonomous Driving Benchmark
Papers with Code 아카이브
Motion Forecasting
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Motion forecasting is the task of predicting the location of a tracked object in the future
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ziqipang/lm4visualencoding
공식 코드Frozen Transformers in Language Models Are Effective Visual Encoder Layers
https://github.com/ziqipang/streamingforecasting
공식 코드https://github.com/2023-MindSpore-1/ms-code-217/tree/main/TNT
https://github.com/DerrickXuNu/OpenCOOD
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
https://github.com/Jacoo-Zhao/3D-Pose-Based-Feedback-For-Physical-Exercises
Learning Trajectory Dependencies for Human Motion Prediction
https://github.com/JunweiLiang/social-distancing-prediction
Peeking into the Future: Predicting Future Person Activities and Locations in Videos
https://github.com/Kin-Zhang/mmfn
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
https://github.com/Robotmurlock/TNT-VectorNet-and-HOME-Trajectory-Forecasting
https://github.com/Tsinghua-MARS-Lab/DenseTNT
DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets
https://github.com/YuejiangLIU/social-nce-trajectron-plus-plus
Social NCE: Contrastive Learning of Socially-aware Motion Representations
https://github.com/ZikangZhou/HiVT
https://github.com/amiryanj/socialways
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/argoverse/argoverse-api
https://github.com/autonomousvision/navsim
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
https://github.com/bouracha/Gen_Motion
Learning Trajectory Dependencies for Human Motion Prediction
https://github.com/bouracha/OoDMotion
Learning Trajectory Dependencies for Human Motion Prediction
https://github.com/chengxuduan/advhmp
Learning Trajectory Dependencies for Human Motion Prediction
https://github.com/cmubig/SPEC
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/coperception/coperception
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
https://github.com/d-eremeev/SC-GAN
https://github.com/daeheepark/t4p
trajdata: A Unified Interface to Multiple Human Trajectory Datasets
https://github.com/eth-ait/spl
https://github.com/henry1iu/tnt-trajectory-prediction
https://github.com/henry1iu/tnt-trajectory-predition
https://github.com/horizonrobotics/gump
MotionLM: Multi-Agent Motion Forecasting as Language Modeling
https://github.com/huang-xx/STGAT
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/inhwanbae/npsn
https://github.com/johnwlambert/argoverse_cbgs_kf_tracker
https://github.com/kazuki-shin/l5kit
One Thousand and One Hours: Self-driving Motion Prediction Dataset
https://github.com/kumgleb/Lyft_motion_prediction
One Thousand and One Hours: Self-driving Motion Prediction Dataset
https://github.com/m-hasan-n/pooling
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/mediabrain-sjtu/collaborative-uncertainty
https://github.com/mirkozaff/aa-sgan
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
https://github.com/motional/motional-prediction-devkit
https://github.com/motional/nuplan-devkit
NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.