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Code accompanying the ECCV 2020 paper "Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data" by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (…
[CVPR2022] Code for CVPR 2022 paper "Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion"
DiffusionFastForward: a free course and experimental framework for diffusion-based generative models
A collection of resources and papers on Diffusion Models
Pytorch implementation of Diffusion Models (https://arxiv.org/pdf/2006.11239.pdf)
Diffusion model papers, survey, and taxonomy
Argoverse 2: Next generation datasets for self-driving perception and forecasting.
PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend
[CVPR 2024] LMDrive: Closed-Loop End-to-End Driving with Large Language Models
AutoDriving-Planning-Control-Algorithm-Simulation-Carla
Learn how to use CARLA with basic APIs
This repository is an official implementation of ADAPT: Action-aware Driving Caption Transformer, accepted by ICRA 2023.
LAVIS - A One-stop Library for Language-Vision Intelligence
A curated list of awesome LLM for Autonomous Driving resources (continually updated)
A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
The paper list of the 86-page paper "The Rise and Potential of Large Language Model Based Agents: A Survey" by Zhiheng Xi et al.
Official implementation of our ICLR 2023 paper "Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting"
An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730
About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008
Fast and Accurate ML in 3 Lines of Code
PyTorch implementation for the paper "Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving"
Drive Like a Human: Rethinking Autonomous Driving with Large Language Models
Research Projects of the MOOC "Automated and Connected Driving Challenges"
[ECCV 2022] ST-P3, an end-to-end vision-based autonomous driving framework via spatial-temporal feature learning.
[ICCV'21] NEAT: Neural Attention Fields for End-to-End Autonomous Driving
dataset and code for 2016 paper "Learning a Driving Simulator"
[ICLR 2024] Efficient Streaming Language Models with Attention Sinks