X-CAR: An Experimental Vehicle Platform for Connected Autonomy Research Powered by CARMA

Goodarz Mehr, Prasenjit Ghorai, Ce Zhang, Anshul Nayak, Darshit Patel +2 more
4/6/2022
cs.RO

No official code found. Best implementation: VT-ASIM-LAB/continental_radar_driver (10 stars, GPL-3.0, last commit 2022-05-25). 1 more below.

Abstract

Autonomous vehicles promise a future with a safer, cleaner, more efficient, and more reliable transportation system. However, the current approach to autonomy has focused on building small, disparate intelligences that are closed off to the rest of the world. Vehicle connectivity has been proposed as a solution, relying on a vision of the future where a mix of connected autonomous and human-driven vehicles populate the road. Developed by the U.S. Department of Transportation Federal Highway Administration as a reusable, extensible platform for controlling connected autonomous vehicles, the CARMA Platform is one of the technologies enabling this connected future. Nevertheless, the adoption of the CARMA Platform has been slow, with a contributing factor being the limited, expensive, and relatively old vehicle configurations that are officially supported. To alleviate this problem, we propose X-CAR (eXperimental vehicle platform for Connected Autonomy Research). By implementing the CARMA Platform on more affordable, high quality hardware, X-CAR aims to increase the versatility of the CARMA Platform and facilitate its adoption for research and development of connected driving automation.

View on arXivView PDF

Code Implementations(2)

GPL-3.0

ROS radar driver for Continental ARS 408-21/404-21 for the CARMA Platform.

102C++, CMakeMay 7, 20224 years agoGPL-3.0
MIT

ROS GNSS/INS driver for Inertial Labs positioning systems for the CARMA Platform

64C++, CMakeDec 10, 20214 years agoMIT

Similar Papers

Cite this paper

@article{mehr2022xcar,
  title  = {X-CAR: An Experimental Vehicle Platform for Connected Autonomy Research Powered by CARMA},
  author = {Goodarz Mehr and Prasenjit Ghorai and Ce Zhang and Anshul Nayak and Darshit Patel and Shathushan Sivashangaran and Azim Eskandarian},
  year   = {2022},
  eprint = {2204.02559},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2204.02559}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2204.02559.svg)](https://codeofpaper.com/paper/2204.02559)

Discussion