Skip to content

Latest commit

 

History

History
117 lines (91 loc) · 3.96 KB

README.md

File metadata and controls

117 lines (91 loc) · 3.96 KB
Raw

Multi-Modal Fusion for Sensorimotor Coordination in Steering Angle Prediction

This repository contains the code for Multi-Modal Fusion for Sensorimotor Coordination in Steering Angle Prediction. If you find our code or paper useful, please cite

@article{munir2022multi,
  title={Multi-Modal Fusion for Sensorimotor Coordination in Steering Angle Prediction},
  author={Azam, Shoaib, Munir, Farzeen, Lee, Byung-Geun and Jeon, Moongu},
  journal={arXiv preprint arXiv:2202.05500},
  year={2022}
}


 
 

Demo Videos

[DRFuser Data Demo][DDD Demo] [Carla EventScape Data Demo]

Contents

  1. Setup
  2. Dataset
  3. Dataset Generation
  4. Training
  5. Evaluation

Setup

Install anaconda

wget https://repo.anaconda.com/miniconda/Miniconda3-py38_4.10.3-Linux-x86_64.sh
bash Miniconda3-py38_4.10.3-Linux-x86_64.sh
source ~/.bashrc

Clone the repo and build the environment

git clone https://github.com/azamshoaib/DRFuser.git
cd DRFuser
conda create -n drfuser python=3.8
conda activate drfuser
pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt

Dataset

A. DRFuser Dataset
Our collected dataset include event, frame-based RGB and vehicle control data can be requested at https://forms.gle/dTHTfigxx2nNNtuQ6.

B. Davis Driving Dataset (DDD)
The Davis Driving dataset can be downloaded from https://sites.google.com/view/davis-driving-dataset-2020/home

C. Carla Eventscape Dataset

The Carla Eventscape dataset can be downloaded at http://rpg.ifi.uzh.ch/RAMNet.html

Dataset Generation

  1. DRFuser Dataset i) Pre-processing of data
    Note: We recommend to deactivate the conda environment and install the following dependencies on the system. \
    • Install ROS and its dependencies from http://wiki.ros.org/Installation/Ubuntu . In our experimentation we have used ROS melodic. Please check your Ubuntu and install the respective ROS version.
    • Create the workspace
    $ mkdir -p ~/workspace/src
$ git clone https://github.com/uzh-rpg/rpg_dvs_ros.git
$ cp msgs ~/workspace/src/
$ cd ~/workspace/
$ rosdep install --from-paths src --ignore-src -r -y
$ catkin_make
$ source devel/setup.bash
    • Install the bagpy
    pip install bagpy
    • Run the following script for the data synchronizing
    python data_synchronize.py
    • After the running the data_synchronize.py, two folders with aps_data and dvs_data will be created. The complete synchronize data in csv format will also be created in the same rosbag folder.
    • The following illustrates the folder structure after running the script
    /path/to/rosbag_file
    ├── /aps_data
    ├── /aps_data_day2-2-gist.csv
    ├── /can_info.csv
    ├── /can_out_sync.csv
    ├── /complete_data.csv
    ├── /dvs_data
    ├── /dvs_data_day2-2-gist.csv
    ├── /dvs_out_sync.csv

Training

For training the network using DRFuser dataset, run the following script

python train.py

The train.py provide a explanation about the different arguments used in the script.

Evaluation

Testing a network is done by using two scripts. First, the test.py script is used to save the predictions from the network. As a second step, the visualize.py script is used to visualize the results on the dataset.

Note: The scripts for training and evaluation are same for the DDD and Carla Eventscape datasets. The required arguments in the train.py must be changed before using the aforementioned datasets.