https://github.com/vita-epfl/semdisc
Are socially-aware trajectory prediction models really socially-aware?
Papers with Code 아카이브
Trajectory Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 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/vita-epfl/social-nce-crowdnav
Social NCE: Contrastive Learning of Socially-aware Motion Representations
https://github.com/vita-epfl/trajnetplusplusbaselines
Human Trajectory Forecasting in Crowds: A Deep Learning Perspective
https://github.com/whubaichuan/Vessel_Trajectory_Prediction
Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks
https://github.com/xincoder/GRIP_Trajectory
GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving
https://github.com/yadrimz/DESIRE
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
https://github.com/yulinliu101/DeepTP
Predicting Aircraft Trajectories: A Deep Generative Convolutional Recurrent Neural Networks Approach
https://github.com/zhangpur/SR-LSTM
SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction
https://github.com/zqzqz/AdvTrajectoryPrediction
GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.