Lists (6)
Stars
Retrieval and Retrieval-augmented LLMs
Dataset and benchmark for RAG on company internal documents.
Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption
Argoverse 2: Next generation datasets for self-driving perception and forecasting.
Official implementation of paper "Stay on Track: Novel Loss Functions for Vehicle Trajectory Prediction"
Official implementation of progressive retrospective framework published at CVPR.
Official implementation of temporal-adaptive progressive distillation submitted to arXiv.
[WACV 2026 Oral] Streaming Real-Time Trajectory Prediction Using Endpoint-Aware Modeling
[CVPR 2026] SHARP: Short-Window Streaming for Accurate and Robust Prediction in Motion Forecasting
SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving
[ICCV 2025] DONUT: A Decoder-Only Model for Trajectory Prediction
[CVPR 2024] HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention
Code for "Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals," CoRL 2021.
MTR: Motion Transformer with Global Intention Localization and Local Movement Refinement, NeurIPS 2022.
[ICCV'2023] Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders
A Unified Framework for scalable Vehicle Trajectory Prediction, ECCV 2024
[CVPR 2023] Query-Centric Trajectory Prediction
[NeurIPS 2024 Spotlight] Motion Forecasting in Continuous Driving
[NeurIPS 2024] DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States
MASAR: Motion–Appearance Synergy Refinement for Joint Detection and Trajectory Forecasting
[ICCV 2025] ForeSight: Multi-View Streaming Joint Object Detection and Trajectory Forecasting
[ECCV 2024]FipTR: A Simple yet Effective Transformer Framework for Future Instance Prediction in Autonomous Driving
[NeurIPS 2025] Future-Aware End-to-End Driving: Bidirectional Modeling of Trajectory Planning and Scene Evolution
[AAAI 2026] Perception in Plan: Coupled Perception and Planning for End-to-End Autonomous Driving