一句话：提出E-CoDrive联合仿真框架，集成能耗模型、微观交通与高保真驾驶模拟器，支持自动驾驶电动车能源关键场景的可复现闭环测试。
自动驾驶研究多聚焦安全，对能耗与可持续性等非功能属性关注不足；随着自动驾驶电动车在城市场景普及，需要测试复杂交通动态如何影响能耗，以及车辆能否在电量耗尽前完成行程。E-CoDrive 是一个可复现的闭环驾驶联合仿真框架，将能耗消耗模型、微观交通模拟器与高保真驾驶模拟器集成，用于城市场景下自动驾驶电动车软件栈的能源感知场景测试。该工具论文介绍其架构，并通过测试基于 Autoware 的 AEV 软件栈展示适用性。评估表明，不同交通条件会导致车辆能耗出现显著差异，且工件已公开。
Autonomous driving research has largely focused on safety while giving limited attention to non-functional aspects such as energy consumption and sustainability. As Autonomous Electric Vehicles (AEVs) become increasingly common in urban traffic, understanding how complex traffic dynamics influence their energy consumption is paramount to test whether AEVs can complete trips before battery depletion. To support energy-aware scenario-based testing of AEVs, we present E-CoDrive, a framework for reproducible closed-loop driving co-simulations that integrates an energy consumption model, a micro-traffic simulator, and a high-fidelity driving simulator to test AEV software stacks in urban scenarios. This tool paper describes the architecture of E-CoDrive and demonstrates its applicability by testing an Autoware-based AEV stack. Our evaluation shows that varying traffic conditions produce substantial differences in vehicle energy consumption. The artifact is publicly available at https://doi.org/10.6084/m9.figshare.32244783, and a screencast showing the tool is available at https://youtu.be/yX9fWHqCvgc.
@inproceedings{abs-2607-04803,
title = {E-CoDrive: A Co-Simulation Framework for Testing Energy-Critical Driving Scenarios},
author = {Manfredi Napolitano and Alessandra Somma and Alessio Gambi and Andrea Stocco and Nicola Mazzocca},
booktitle = {ASE},
year = {2026},
doi = {10.1145/3832783.3834648}
}