数据来自:https://github.com/colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers, 2024轨迹预测相关论文和源代码汇总:

ECCV 2024

Learning Semantic Latent Directions for Accurate and Controllable Human Motion Prediction.

论文:https://arxiv.org/abs/2407.11494

代码:https://github.com/GuoweiXu368/SLD-HMP

MART: MultiscAle Relational Transformer Networks for Multi-agent Trajectory Prediction.

论文:https://arxiv.org/abs/2407.21635

代码:https://github.com/gist-ailab/MART

Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation.

论文:https://arxiv.org/abs/2408.00374

Project:https://yixiaowang7.github.io/OptTrajDiff_Page/

代码:https://github.com/YixiaoWang7/OptTrajDiff

Progressive Pretext Task Learning for Human Trajectory Prediction.

论文:https://arxiv.org/abs/2407.11588

代码:https://github.com/iSEE-Laboratory/PPT

PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving.

论文:https://arxiv.org/abs/2311.08100

代码:https://github.com/zlichen/PPAD

Scene-aware Human Motion Forecasting via Mutual Distance Prediction.

论文:https://arxiv.org/abs/2310.00615

代码:https://github.com/xccyue/MutualDistance

UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction.

论文:https://arxiv.org/abs/2403.15098

代码:https://github.com/vita-epfl/UniTraj

CVPR 2024

Adapting to Length Shift: FlexiLength Network for Trajectory Prediction.

论文:https://arxiv.org/abs/2404.00742

Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving.

论文:https://arxiv.org/abs/2306.15755

CaDeT: a Causal Disentanglement Approach for Robust Trajectory Prediction in Autonomous Driving.

Paper:https://openaccess.thecvf.com/content/CVPR2024/papers/Pourkeshavarz_CaDeT_a_Causal_Disentanglement_Approach_for_Robust_Trajectory_Prediction_in_CVPR_2024_paper.pdf

Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction.

论文:https://arxiv.org/abs/2403.18447

代码:https://github.com/InhwanBae/LMTrajectory

Website:https://ihbae.com/publication/lmtrajectory/

Density-Adaptive Model Based on Motif Matrix for Multi-Agent Trajectory Prediction.

论文: https://openaccess.thecvf.com/content/CVPR2024/papers/Wen_Density-Adaptive_Model_Based_on_Motif_Matrix_for_Multi-Agent_Trajectory_Prediction_CVPR_2024_paper.pdf

Higher-order Relational Reasoning for Pedestrian Trajectory Prediction.

论文: https://openaccess.thecvf.com/content/CVPR2024/papers/Kim_Higher-order_Relational_Reasoning_for_Pedestrian_Trajectory_Prediction_CVPR_2024_paper.pdf

HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention.

论文:https://arxiv.org/abs/2404.06351

代码:https://github.com/XiaolongTang23/HPNet

Human Motion Prediction under Unexpected Perturbation.

代码:https://github.com/realcrane/Human-Motion-Prediction-under-Unexpected-Perturbation

MoST: Multi-modality Scene Tokenization for Motion Prediction.

论文:https://arxiv.org/abs/2404.19531

Producing and Leveraging Online Map Uncertainty in Trajectory Prediction.

论文:https://arxiv.org/abs/2403.16439

代码:https://github.com/alfredgu001324/MapUncertaintyPrediction

Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations.

论文:https://arxiv.org/abs/2403.13261

代码:https://github.com/kwwcv/SelfMotion

SingularTrajectory: Universal Trajectory Predictor using Diffusion Model.

论文:https://arxiv.org/abs/2403.18452

Website:https://ihbae.com/publication/singulartrajectory/

代码:https://github.com/InhwanBae/SingularTrajectory

SmartRefine: An Scenario-Adaptive Refinement Framework for Efficient Motion Prediction.

论文:https://arxiv.org/abs/2403.11492

代码:https://github.com/opendilab/SmartRefine

SocialCircle: Learning the Angle-based Social Interaction Representation for Pedestrian Trajectory Prediction.

论文:https://arxiv.org/abs/2310.05370

代码: https://github.com/cocoon2wong/SocialCircle/tree/TorchVersion(beta)

T4P: Test-Time Training of Trajectory Prediction via Masked Autoenarxivr and Actor-specific Token Memory.

论文:https://arxiv.org/abs/2403.10052

Multi-agent Long-term 3D Human Pose Forecasting via Interaction-aware Trajectory Conditioning.

论文:https://arxiv.org/abs/2404.05218

ICLR 2024

SEPT: Towards Efficient Scene Representation Learning for Motion Prediction.

论文:https://arxiv.org/abs/2309.15289

Social-Transmotion: Promptable Human Trajectory Prediction.

论文:https://arxiv.org/abs/2312.16168

代码:https://github.com/vita-epfl/social-transmotion

RAL 2024

SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving.

论文:https://arxiv.org/abs/2402.02519

代码:https://github.com/HKUST-Aerial-Robotics/SIMPL

ICRA 2024

CRITERIA: A New Benchmarking Paradigm for Evaluating Trajectory Prediction Models for Autonomous Driving.

论文:https://arxiv.org/abs/2310.07794

A Novel Benchmarking Paradigm and a Scale## and Motion-Aware Model for Egocentric Pedestrian Trajectory Prediction.

论文:https://arxiv.org/abs/2310.10424

Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction.

论文:https://arxiv.org/abs/2302.10873

DESTINE: Dynamic Goal Queries with Temporal Transductive Alignment for Trajectory Prediction.

论文:https://arxiv.org/abs/2310.07438

Disentangled Neural Relational Inference for Interpretable Motion Prediction.

论文:https://arxiv.org/abs/2401.03599

FIMP: Future Interaction Modeling for Multi-Agent Motion Prediction.

论文:https://arxiv.org/abs/2401.16189

LaCE-LHMP: Airflow Modelling-Inspired Long-Term Human Motion Prediction by Enhancing Laminar Characteristics in Human Flow.

论文:https://arxiv.org/abs/2403.13640

代码:https://github.com/test-bai-cpu/LaCE-LHMP

MacFormer: Map-Agent Coupled Transformer for Real-time and Robust Trajectory Prediction.

论文:https://arxiv.org/abs/2308.10280

MGTR: Multi-Granular Transformer for Motion Prediction with LiDAR.

论文:https://arxiv.org/abs/2312.02409

Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments.

arXix:https://arxiv.org/abs/2402.04318

代码:https://github.com/Petrichor625/Gava

Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments.

论文:http://arxiv.org/abs/2402.04318

代码:https://github.com/Petrichor625/Gava

Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions.

Project:https://chantsss.github.io/POP/

论文:https://arxiv.org/abs/2309.15685

代码:https://github.com/chantsss/POP-arxiv

Pedestrian Trajectory Prediction Using Dynamics-based Deep Learning.

论文:https://arxiv.org/abs/2309.09021

PBP: Path-based Trajectory Prediction for Autonomous Driving.

论文:https://arxiv.org/abs/2309.03750

Scene Informer: Anchor-based Occlusion Inference and Trajectory Prediction in Partially Observable Environments.

论文:https://arxiv.org/abs/2309.13893

代码:https://github.com/sisl/SceneInformer

AAAI 2024

Improving Transferability for Cross-domain Trajectory Prediction via Neural Stochastic Differential Equation.

论文:https://arxiv.org/abs/2312.15906

代码:https://github.com/daeheepark/TrajSDE

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