Kiroisme

Pinned Repositories

B-GAP-Behavior-Guided-Action-Prediction-and-Navigation-for-Autonomous-Driving
This is the code base for our paper "B-GAP: Behavior-Rich Simulation and Navigation for Autonomous Driving", which was published at RA-L and presented at IROS 2022.
Language:Python00
clash_for_windows_pkg
A Windows/macOS GUI based on Clash
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DIPP
[TNNLS] Differentiable Integrated Prediction and Planning Framework for Urban Autonomous Driving
Language:Python00
driving-interactions
Language:Python00
Driving-IRL-NGSIM
[T-ITS] Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning
Language:Python00
drone-dataset-tools
The goal of this repo is to make the drone datasets as easy to use as possible. For this purpose we provide source code in Python, which allows the import and visualization of the datasets.
Language:Python00
ECNUMasterThesisLatexTemplate
华东师范大学硕士学位论文 Latex 模板（Latex template for Master thesis of East China normal University）
Language:TeX00
gail-driver
Language:Python00
highD-dataset
Language:MATLAB00
HighD-NGSIM-lane-change-feature-extraction
HighD,NGSIM lane change feature extraction and save.
Language:Python00

Kiroisme's Repositories

Kiroisme/B-GAP-Behavior-Guided-Action-Prediction-and-Navigation-for-Autonomous-Driving
This is the code base for our paper "B-GAP: Behavior-Rich Simulation and Navigation for Autonomous Driving", which was published at RA-L and presented at IROS 2022.
Kiroisme/clash_for_windows_pkg
A Windows/macOS GUI based on Clash
0 0
Kiroisme/DIPP
[TNNLS] Differentiable Integrated Prediction and Planning Framework for Urban Autonomous Driving
Kiroisme/driving-interactions
Kiroisme/Driving-IRL-NGSIM
[T-ITS] Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning
Kiroisme/drone-dataset-tools
The goal of this repo is to make the drone datasets as easy to use as possible. For this purpose we provide source code in Python, which allows the import and visualization of the datasets.
Language:Python
Kiroisme/ECNUMasterThesisLatexTemplate
华东师范大学硕士学位论文 Latex 模板（Latex template for Master thesis of East China normal University）
Kiroisme/gail-driver
Kiroisme/highD-dataset
Kiroisme/HighD-NGSIM-lane-change-feature-extraction
HighD,NGSIM lane change feature extraction and save.
Kiroisme/highD.toolkit
Some plugins for highD series dataset
Kiroisme/HighwayENV
Highway driving simulator incorporating NGSIM dataset using reinforcement learning
Kiroisme/IDM_Bayesian_Calibration
Kiroisme/irl-maxent
Maximum Entropy and Maximum Causal Entropy Inverse Reinforcement Learning Implementation in Python
Kiroisme/irl_rocks
Cool Inverse Reinforcement Learning Papers
Kiroisme/irlmodelvalidation
This python package contains scripts needed to train IRL Driver models on HighD datasets. This code is accompanying the paper "Validating human driver models for interaction-aware automated vehicle controllers: A human factors approach - Siebinga, Zgonnikov & Abbink 2021"
Kiroisme/lane-change-prediction-lstm
LSTM lane-changing detection utilizing highD dataset
Kiroisme/mav_trajectory_generation
Polynomial trajectory generation and optimization, especially for rotary-wing MAVs.
Kiroisme/Naturalistic-SUMO-Gym
We provide an open source software package for AV based simulation and testing running a docker container
Kiroisme/NGSIM-data-extraction
Kiroisme/NGSIM-US-101-trajectory-dataset-smoothing
smoothing the NGSIM US-101 trajectory dataset using Savitzky-Golay Filter
Kiroisme/ngsim_env
Learning human driver models from NGSIM data with imitation learning.
Kiroisme/NGSIM_preprocess
Colab notebook to preprocess NGSIM dataset to extract leading and following vehicle information
Kiroisme/nndl.github.io
《神经网络与深度学习》邱锡鹏著 Neural Network and Deep Learning
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Kiroisme/PARL
A high-performance distributed training framework for Reinforcement Learning
Kiroisme/Personalizing-ADAS-in-CARLA
Personalization of Assistive Driving in Carla Simulator
Kiroisme/Robotics-Path-Planning-04-Quintic-Polynomial-Solver
Udacity Self-Driving Car Engineer Nanodegree: Quintic Polynomial Solver. & Paper 'Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame'
Kiroisme/Self-Driving-Cars
Coursera Open Courses from University of Toronto
Kiroisme/Self-Driving-Vehicle-Control-on-CARLA
Lane Keeping Assist function by applying Stanley method for lateral control and PID controller for longitudinal control using Python on the Carla simulator. 🚗
Kiroisme/travia
a Traffic data Visualization and Annotation tool