李弘扬团队最新！SimScale：显著提升困难场景的端到端仿真框架，NavSim新SOTA

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].