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

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arxiv 2506.16535 v2 pith:GASSV5RE submitted 2025-06-19 cs.RO cs.MAcs.NIcs.SYeess.SY

Tyler Landle , Jordan Rapp , Dean Blank , Chandramouli Amarnath , Abhijit Chatterjee , Alexandros Daglis , Umakishore Ramachandran This is my paper · ORCID
classification cs.ROcs.MAcs.NIcs.SYeess.SY
keywords vehiclesautonomouscontrolevaluationalgorithmsecavsafetyconnected
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abstract

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.

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