X-CAR: An Experimental Vehicle Platform for Connected Autonomy Research Powered by CARMA
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Apr 2022.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 1; 2026: 2.
6 papers.
- 2023SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Drivingcited by 8
- 2024Image-Guided Outdoor LiDAR Perception Quality Assessment for Autonomous Drivingcited by 3
- 2024Why Sample Space Matters: Keyframe Sampling Optimization for LiDAR-based Place Recognitioncited by 0
- 2025AI-CDA4All: Democratizing Cooperative Autonomous Driving for All Drivers via Affordable Dash-cam Hardware and Open-source AI Softwarecited by 0
- 2026SimBEV2X: A Large-Scale Dataset and Data Generation Tool for Multi-Task Vehicle-to-Everything Cooperative Perceptioncited by 0
- 2026Real-World Perception for Autonomous Driving in Adverse Weather: Enhancing Standard Detectors via Foundation-Guided Auto-Annotationcited by 0
- 2026Real-World Perception for Autonomous Driving in Adverse Weather: Enhancing Standard Detectors via Foundation-Guided Auto-Annotation22 Sep 2026
- 2026SimBEV2X: A Large-Scale Dataset and Data Generation Tool for Multi-Task Vehicle-to-Everything Cooperative Perception27 Jul 2026
- 2025AI-CDA4All: Democratizing Cooperative Autonomous Driving for All Drivers via Affordable Dash-cam Hardware and Open-source AI Software10 May 2025
- 2024Why Sample Space Matters: Keyframe Sampling Optimization for LiDAR-based Place Recognition3 Oct 2024
- 2024Image-Guided Outdoor LiDAR Perception Quality Assessment for Autonomous Driving25 Jun 2024
- 2023SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving11 May 2023