SHITIANYU-hue

Efficient-motion-planning

To guarantee safe and efficient driving for automated vehicles in complicated traffic conditions, the motion planning module of automated vehicles are expected to generate collision-free driving policies as soon as possible in varying traffic environment. However, there always exist a tradeoff between efficiency and accuracy for the motion planning algorithms. Besides, most motion planning methods cannot find the desired trajectory under extreme scenarios (e.g., lane change in crowded traffic scenarios). This study proposed an efficient motion planning strategy for automated lane change based on Mixed-Integer Quadratic Optimization (MIQP) and Neural Networks. We modeled the lane change task as a mixed-integer quadratic optimization problem with logical constraints, which allows the planning module to generate feasible, safe and comfortable driving actions for lane changing process. Then, a hierarchical machine learning structure that consists of SVM-based classification layer and NN-based action learning layer is established to generate desired driving policies that can make online, fast and generalized motion planning. Our model is validated in crowded lane change scenarios through numerical simulations and results indicate that our model can provide optimal and efficient motion planning for automated vehicles

kaggle-bio

https://www.kaggle.com/competitions/open-problems-multimodal/

LaneGCN

[ECCV2020 Oral] Learning Lane Graph Representations for Motion Forecasting

macad-gym

Multi-Agent Connected Autonomous Driving (MACAD) Gym environments for Deep RL. Code for the paper presented in the Machine Learning for Autonomous Driving Workshop at NeurIPS 2019:

argoverse-api

Official GitHub repository for Argoverse dataset

atari-agents

Code and links for trained Atari agents

challenge-aido_RL-IL

Reinforcement Learning + Imitation Learning based approach to AI Driving Olympics

DenseTNT

DICG

Deep Implicit Coordination Graphs

guandan_mcc

mcc_second_guandan

HEBO

Bayesian optimisation library developped by Huawei Noah's Ark Lab

Home-Credit-Default-Risk-analysis

Home Credit Default Risk

IG-RL-1

Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control

its_simulator

macad-agents

Agents code for Multi-Agent Connected Autonomous Driving (MACAD) described in the paper presented in the Machine Learning for Autonomous Driving Workshop at NeurIPS 2019:

mixed-autonomy-paper-reading

Modeling_Simulation

Modeling, Simulation, and Decision Making: Cellular Automata, Networks, and Monte Carlo simulations.

multi-bcq

nerf

Code release for NeRF (Neural Radiance Fields)

pytorch_DGN

The pytorch implementation of DGN

reinforcement_learning_Interview_Notes-Chinese

强化学习面试（未完待续）

RGLight

Improving the generalizability and robustness of large-scale traffic signal control

rl-paper-reading

rl_algorithms

Structural implementation of RL key algorithms

RLCN

safety-gym

StarCraft

Implementations of QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II

twitter-sentiment-analysis

wastland

wilderness-scavenger

A platform for intelligent agent learning based on a 3D open-world FPS game developed by Inspir.AI.