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

Jul 16, 2026

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Autonomous driving research emphasizes safety-critical behaviors while often neglecting non-functional aspects such as energy consumption and sustainability. As autonomous electric vehicles increasingly operate in urban traffic, the effect of complex traffic dynamics on energy usage becomes essential to ensure trips can finish before battery depletion. E-CoDrive provides reproducible closed-loop driving co-simulations that integrate an energy model, a micro-traffic simulator, and a high-fidelity driving simulator for testing AEV software stacks. Evaluation shows traffic variations produce substantial energy consumption differences, and the artifact is publicly available.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages5

Reading Time13 min

Introduction

Motivation and problem scope

Limitations of existing simulation tools

E-CoDrive framework approach

E-CoDrive: A Co-Simulation Framework for Testing Energy-Critical Driving Scenarios Manfredi Napolitano Università degli Studi di Napoli Federico II Naples, Italy Alessandra Somma Università degli Studi di Napoli Federico II Naples, Italy Alessio Gambi Austrian Institute of Technology Vienna, Austria The Italian Institute of AI for Industry Turin, Italy arXiv :2607 .04803v 1 [ cs . SE] 6 Jul 2026 Andrea Stocco Technical University of Munich fortiss GmbH Munich, Germany Nicola Mazzocca Università degli Studi di Napoli Federico II Naples, Italy 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](https://doi.org/10.6084/m9.figshare.32244783), and a screencast showing the tool is available at [https://youtu.be/](https://youtu.be/)[ ](https://youtu.be/)[yX9fWHqCvgc.](yX9fWHqCvgc.) ACM Reference Format: Manfredi Napolitano, Alessandra Somma, Alessio Gambi, Andrea Stocco, and Nicola Mazzocca. 2026. E-CoDrive: A Co-Simulation Framework for Testing Energy-Critical Driving Scenarios. In Proceedings of Proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE ’26). ACM, New York, NY, USA, 5 pages. [https://doi.org/XXXXXXX](https://doi.org/XXXXXXX). XXXXXXX 1 Introduction Research on autonomous driving has made substantial progress in the validation of safety-critical behaviors, perception pipelines, motion-planning logic, and the reliability of learning-enabled components [7, 10, 15, 16, 20] . However, investigating how complex Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission [and/or a fee. Request permissions from permissions@acm.org](and/or a fee. Request permissions from permissions@acm.org). ASE’26, Munich, Germany © 2026 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 978-1-4503-XXXX-X/2026/06 [https://doi.org/XXXXXXX.XXXXXXX](https://doi.org/XXXXXXX.XXXXXXX) environments and dynamic traffic affect energy consumption remains underexplored, despite its central role in the design of Autonomous Electric Vehicles (AEVs) . Consequently, validating AEVs using existing tools that focus on safety but are oblivious to energy consumption might fail to expose misbehaviors such as idling, repeated acceleration, or detours that lead to premature battery exhaustion. Studying the interplay among traffic, energy consumption, and recovery requires coordinating traffic generation, autonomous decision-making, and energy-aware measurements in a consistent and reproducible setting. Unfortunat

FAQs

Why is energy consumption important for autonomous electric vehicle testing?

Urban traffic dynamics can significantly change energy consumption, and batteries may deplete before a trip ends if energy-critical behaviors are not exposed during validation.

What does E-CoDrive integrate to enable energy-aware co-simulation?

E-CoDrive combines an energy consumption model, a micro-traffic simulator, and a high-fidelity driving simulator to run synchronized closed-loop simulations.

What limitation do current tools have that E-CoDrive addresses?

Existing systems often focus on data exchange and do not reliably provide coordinated execution with synchronized timelines across traffic, vehicle dynamics, and autonomy modules.

Jul 16, 2026

Upload by Aria