🚗 Predict multi-agent trajectories for autonomous vehicles using advanced models on the Argoverse 2 dataset, enhancing motion forecasting capabilities.

Argoverse 2: Next generation datasets for self-driving perception and forecasting.

"Official code for TCRAT-Pred: Multi-Agents Trajectory Prediction for Autonomous Vehicles with Multi-Modal Predictions (IEEE CogInfoCom 2024)"

[ICCV 2025] DONUT: A Decoder-Only Model for Trajectory Prediction

Trajectory Forecasting Competition for CSE 251 B, Spring 2025

[RAL 2025]Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network

This is useful to debug the Argoverse V1.1 dataset.

Argoverse User Guide.

[RAL 2025]Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network-xinguipeng

Using PyTorch to predict where cars, cyclists, and pedestrians will go.

The official implementation of "Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting" presented in ECCV2022.

[CVPRW 2024]Official PyTorch Implementation of "LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints"

This repository contains our work on a comprehensive investigation on motion prediction for Autonomous Vehicles using the PowerBEV framework and a Multi-Camera setup. Validated trajectory forecasting capabilities on the NuScenes, Woven and Argoverse datasets and identified challenges in model generalization across these datasets.

Official github for Delay-adaptive Detector

[CVPR 2023] Query-Centric Trajectory Prediction

TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction. ICRA 2023. You may also want to check out the updated version: https://github.com/zhejz/TrafficBotsV1.5

VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking (CVPR 2023)

Motion Forecasting for Autonomous Vehicles using the Argoverse Motion Forecasting Dataset

Official repository for Efficient Baselines for Motion Prediction in Autonomous Driving. Presented at CVPR and ICRA Workshops 2022, and ITSC conference 2022.

[CoRL-2022] SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving