cityuav Goto Github PK

autopilot-tensorflow

A TensorFlow implementation of this Nvidia paper: https://arxiv.org/pdf/1604.07316.pdf with some changes

awesome-autonomous-vehicle

无人驾驶的资源列表中文版

awesome-carla

👉 CARLA resources such as tutorial, blog, code and etc https://github.com/carla-simulator/carla

awesome-self-driving-car

An awesome list of self-driving cars

bdd_driving_model

cal

chauffeurnet

data_aggregation

This repository contains the code for the CVPR 2020 paper "Exploring Data Aggregation in Policy Learning for Vision-based Urban Autonomous Driving"

deep_imitative_models

Reimplementation (currently partial) of Deep Imitative Models paper, ICLR '20

explainable-deep-driving

infogail

Source code for our NIPS 2017 paper, InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations

interp-e2e-driving

Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning

lanegcn

[ECCV2020 Oral] Learning Lane Graph Representations for Motion Forecasting

learningbycheating

Driving in CARLA using model-free deep reinforcement learning

matf-architecture-details

Architecture Details for CVPR 19 paper: Multi-Agent Tensor Fusion for Contextual Trajectory Prediction

ml-multiple-futures-prediction

motionplanning

Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)

my_bibliography_for_research_on_autonomous_driving

Personal notes about scientific and research works on "Decision-Making for Autonomous Driving"

nri

Neural relational inference for interacting systems - pytorch

nvae

The Official PyTorch Implementation of "NVAE: A Deep Hierarchical Variational Autoencoder" (NeurIPS 2020 spotlight paper)

oatomobile

A research framework for autonomous driving

reinforcementlearninginautopilot

RL in AutoPilot 自动驾驶强化学习：效果展示，框架设计、算法和训练经验文档等（部分开源，update from private repo: egocar）

research

dataset and code for 2016 paper "Learning a Driving Simulator"

spectral-trajectory-and-behavior-prediction

This is the code base for Trajectory and Driver Behavior Prediction in Autonomous Vehicles using Spectral Graph Theory

trajectron-plus-plus

Code accompanying the ECCV 2020 paper "Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data" by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (* denotes equal contribution).

virtualtoreal-rl

BMVC 2017: Virtual to Real Reinforcement Learning for Autonomous Driving