一句话：系统比较软件在环、车辆在环、混合现实与真实道路四种测试模态的现实鸿沟，并定位失效无法跨模态迁移的条件。
仿真测试是自动驾驶系统（ADS）开发的重要环节，但仿真与真实行为之间的差异（现实鸿沟）会削弱测试结果的可迁移性。本文构建了配备真实相机与 LiDAR 的小型物理车辆及其数字孪生，实现软件在环（SiL）、车辆在环（ViL）、混合现实（MR）与完全真实世界四种测试模态，并在包含真实障碍物、道路拓扑与室内环境的多种驾驶场景下，评估模块化与端到端两类 ADS 架构。作者从执行、感知与行为保真度三个维度系统量化各模态的现实鸿沟，发现 SiL 与 ViL 简化了真实动力学与传感的关键方面，而 MR 能在不牺牲安全与控制的前提下提升感知真实度；同时识别出失效无法跨模态迁移的条件，并隔离出导致差异的具体鸿沟维度，为更稳健、可迁移的 ADS 验证提供可操作建议。
Simulation-based testing is a cornerstone of Autonomous Driving System (ADS) development, offering safe and scalable evaluation across diverse driving scenarios. However, discrepancies between simulated and real-world behavior, known as the reality gap, challenge the transferability of test results to deployed systems. In this paper, we present a comprehensive empirical study comparing four representative testing modalities: Software-in-the-Loop (SiL), Vehicle-in-the-Loop (ViL), Mixed-Reality (MR), and full real-world testing. Using a small-scale physical vehicle equipped with real sensors (camera and LiDAR) and its digital twin, we implement each setup and evaluate two ADS architectures (modular and end-to-end) across diverse indoor driving scenarios involving real obstacles, road topologies, and indoor environments. We systematically assess the impact of each testing modality along three dimensions of the reality gap: actuation, perception, and behavioral fidelity. Our results show that while SiL and ViL setups simplify critical aspects of real-world dynamics and sensing, MR testing improves perceptual realism without compromising safety or control. Importantly, we identify the conditions under which failures do not transfer across testing modalities and isolate the underlying dimensions of the gap responsible for these discrepancies. Our findings offer actionable insights into the respective strengths and limitations of each modality and outline a path toward more robust and transferable validation of autonomous driving systems.
DOI 原文 ·
@inproceedings{paperbot9403,
title = {A Multi-Modality Evaluation of the Reality Gap in Autonomous Driving Systems},
author = {Stefano Carlo Lambertenghi and Mirena Flores Valdez and Andrea Stocco},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00230}
}