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

TL;DR

eCAV is a scalable simulation platform designed for evaluating large-scale connected autonomous vehicle scenarios, enabling safer testing of V2X technologies and control algorithms with significantly higher vehicle capacity.

Contribution

The paper introduces eCAV, a modular and scalable simulation framework capable of modeling up to 256 vehicles, surpassing existing solutions by eight times in capacity for autonomous vehicle evaluation.

Findings

eCAV can simulate up to 256 vehicles simultaneously.

It outperforms existing frameworks by 8x in vehicle capacity.

Supports evaluation of V2X technologies and control algorithms.

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.