eCAV: An Edge-Assisted Evaluation Platform for Connected Autonomous Vehicles

As autonomous vehicles edge closer to widespread adoption, enhancing road safety through collision avoidance and minimization of collateral damage becomes imperative. Vehicle-to-everything (V2X) technologies, which include vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-cloud (V2C), are being proposed as mechanisms to achieve this safety improvement.Simulation-based testing is crucial for early-stage evaluation of Connected Autonomous Vehicle (CAV) control systems, offering a safer and more cost-effective alternative to real-world tests. However, simulating large 3D environments with many complex single- and multi-vehicle sensors and controllers is computationally intensive. There is currently no evaluation framework that can effectively evaluate realistic scenarios involving large numbers of autonomous vehicles.We propose eCAV -- an efficient, modular, and scalable evaluation platform to facilitate both functional validation of algorithmic approaches to increasing road safety, as well as performance prediction of algorithms of various V2X technologies, including a futuristic Vehicle-to-Edge control plane and correspondingly designed control algorithms. eCAV can model up to 256 vehicles running individual control algorithms without perception enabled, which is $8\times$ more vehicles than what is possible with state-of-the-art alternatives.

@article{landle2025_2506.16535,
  title={ eCAV: An Edge-Assisted Evaluation Platform for Connected Autonomous Vehicles },
  author={ Tyler Landle and Jordan Rapp and Dean Blank and Chandramouli Amarnath and Abhijit Chatterjee and Alexandros Daglis and Umakishore Ramachandran },
  journal={arXiv preprint arXiv:2506.16535},
  year={ 2025 }
}