Skip to content

Latest commit

 

History

231 Commits

Folders and files

NameName
Last commit message
Last commit date
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Cyclo-safe : a multi-sensor bike perception module (Camera, Dual 360° Lidar, GPS, Raspberry Pi, ROS2)

The aim of the CycloSafe project is to improve cyclist safety by rigorously quantifying the risks they face, with a particular focus on those occurring when being overtaken by a motor vehicle.

To do this, we have equipped a bicycle with LIDARs (Light Detection and Ranging), a camera and a GNSS antenna.

This repository contains :

  • the firmware intended to run on a Raspberry Pi to operate all the sensors and take measurements
  • Various tools to export, visualize, and analyze the collected data
  • Files required for 3D printing the case
  • File needed to manufacture the dedicated PCB.
    It was developed as part of an experimental data acquisition system for an academic research study on cyclist safety.
cyclosafe-demo.mp4

Structure

core/: Directory intended to be installed on the Raspberry Pi. Contains:

  • ROS installation script
  • CycloSafe environment setup script
  • systemd services setup script
  • source code for the different ROS nodes

design/: Contains files related to the machining of the case and the dedicated PCB.

scripts/: Contains scripts useful for retrieving and exporting data.

viewer/: Directory intended to be installed on the host machine. Contains a set of tools for visualizing and analyzing the data.

  • ROS2.md: Documentation related to ROS2 and its use in the project.

network.md: Instructions for network configuration on both the host and the Raspberry Pi, enabling interaction between the two machines.

About the Cyclo-safe project

This project is part of the broader CycloSafe research initiative conducted at the Institut national de l’information géographique et forestière (IGN), which aims to quantify the risks faced by cyclists when being overtaken by motor vehicles.

This repository is dedicated exclusively to the design and implementation of the acquisition module used to collect measurement data for the study, along with a set of tools for processing, visualizing, and analyzing the recorded data.

It does not present or discuss the results of the study itself, and should not be directly associated with its conclusions or interpretations.

For more information on the full CycloSafe study, see:
Emmanuel Cledat, Dirk Lauinger, Aymeric Dutremble, Maeve Blarel, Damien Louis Peller, Tristan Geslain, Alexandre Esteoulle, Elisabeth Giroux, Gabin Bourlon, Nicolas Pirard, Eric Ta — Cyclo-Safe: Quantitative study of the risks to which cyclists are exposed during their daily commute.

License

This project is licensed under the CeCILL-B v1 license, a free software license fully compliant with French law, based on the BSD 2-Clause license and the principles of open source.

Scope of the license — The following materials are covered by the CeCILL-B license:

  • Firmware code based on ROS2
  • All project installation scripts and data analysis scripts
  • Source code of data analysis tools, based on Qt and ROS2
  • PCB design files
  • 3D models and any corresponding f3d source files
  • Assembly and user manuals for the module

You are free to:

  • Use, modify, and redistribute this project under the terms of the CeCILL-B license
  • Use this project for both academic and commercial purposes, without the strong copyleft constraints of GPL-like licenses
  • Incorporate this code into proprietary software, as long as you respect the attribution requirements

You must:

  • Include a copy of the CeCILL-B license with any redistributed version
  • Retain notices of authorship and copyright
  • Clearly indicate any modifications made

Author: Nicolas Pirard (@Anvently)
Contact: pirard.nicolas@hotmail.fr
Additional contributors: See CONTRIBUTORS.md

📄 Full license text: CeCILL-B v1

📄 French version: CeCILL-B v1


About

Battery-powered bicycle acquisition module using camera, dual 360° Lidar, and GNSS with ROS2 on Raspberry Pi to detect overtaking vehicles and measure curb distance.

Topics

Resources

Stars

2 stars

Watchers

1 watching

Forks

Releases

Packages

Used by

Contributors

Languages