E-CoDrive: A Co-Simulation Framework for Testing Energy-Critical Driving Scenarios

2026-07-06

AI Summary

This study addresses the lack of systematic evaluation of sustainability metrics—particularly energy consumption—in autonomous driving research, which hinders reliable assessment of whether electric autonomous vehicles can complete urban trips before depleting their battery. To bridge this gap, we propose the first closed-loop co-simulation framework that deeply integrates high-fidelity driving simulation, microscopic traffic simulation, and vehicle energy consumption models, enabling energy-aware scenario-based testing. Our framework couples the Autoware autonomous driving stack with multi-level simulation modules to support reproducible evaluation of energy usage in complex urban environments. Experimental results demonstrate that traffic conditions significantly influence energy consumption, validating the effectiveness of our approach. The associated tools and demonstrations have been open-sourced, filling a critical void in sustainability validation for autonomous driving systems.

Abstract

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.

Problem

• Autonomous Electric Vehicles
• energy consumption
• traffic dynamics
• scenario-based testing
• sustainability

Innovation

• co-simulation
• energy consumption
• autonomous electric vehicles
• scenario-based testing
• micro-traffic simulation