来自香港大学OpenDriveLab、中科院自动化所、小米汽车的联合团队提出了一种解决方案—— SimScale 。 自动驾驶数据荒怎么破？ OpenDriveLab 投稿 量子位 | 公众号 QbitAI 该方案 通过真实世界仿真生成关键场景，以及真实仿真协同训练策略，首次揭示了自动驾驶仿真数据的规模效应 。 现实世界难以提供足够的关键与长尾场景，采集到的大多是价值有限的常态片段，导致 数据越多、提升越难 。 因此，自动驾驶的瓶颈不在规模，而在缺乏能系统生成关键场景并支撑大规模训练的新路径。 无需更多真实数据， 只靠扩大仿真数量，一样能持续突破任何端到端驾驶模型的性能上限 。 为什么要有SimScale？ 因为让大模型屡创新高的Data Scaling，在自动驾驶场景中失灵了—— 为此，SimScale应运而生。 什么是SimScale？ SimScale是一个能"无限扩张世界"的仿真生成框架，通过高保真神经渲染，自动制造多样化反应式交通场景与伪专家示范。 它也是一套让仿真与真实"相互增益"的训练策略，使各种端到端模型都能越训越强，鲁棒性与泛化性全面提升。 它还是一份首次系统揭示自动驾驶仿真规模效益的"实践 ...

Core Viewpoint - The article discusses the limitations of current data scaling methods in autonomous driving and introduces SimScale, a framework designed to generate critical driving scenarios through scalable 3D simulation, enhancing the performance of end-to-end driving models without the need for more real-world data [2][5][44]. Background Review - Data scaling has been a fundamental principle in modern deep learning across various fields, including language and vision. In autonomous driving, end-to-end planning leverages large-scale driving data to create fully autonomous systems [5][44]. SimScale Framework - SimScale is a simulation generation framework that utilizes high-fidelity neural rendering to create diverse reactive traffic scenarios and pseudo-expert demonstrations. It integrates simulation and real-world data to enhance the robustness and generalization of various end-to-end models [6][12][44]. Simulation Data Generation - The framework employs a 3D Gaussian Splatting (3DGS) simulation data engine to control the states of the vehicle and other agents over time, rendering multi-view videos from the vehicle's perspective. This process involves perturbing vehicle trajectories to maximize state space coverage and generating corresponding expert trajectories for comparison [13][15][19]. Experimental Results - The results from the navhard and navtest benchmark tests show significant performance improvements across all models, with GTRS-Dense achieving a score of 47.2 on navhard, marking a new state-of-the-art performance. The integration of simulation data enhances model robustness in challenging and unseen scenarios [30][31][32][44]. Data Scaling Analysis - The study analyzes the scaling behavior of different planners under fixed real-world data conditions, revealing that the performance of planners improves predictably with increased simulation data. The exploration of pseudo-expert behaviors and interactive environments significantly enhances the effectiveness of simulation data [33][38][39][44]. Conclusion - SimScale demonstrates how large-scale simulation can amplify the value of real-world datasets in end-to-end autonomous driving. The framework's ability to generate pseudo-expert data and its collaborative training approach lead to notable improvements in model performance, emphasizing the importance of simulation in the development of autonomous driving technologies [44].