SimScale：基於大規模真實世界模擬的駕駛學習技術

摘要

實現完全自動駕駛系統需要讓系統能在各類場景中學習理性決策，包括安全關鍵場景和分佈外場景。然而，人類專家收集的真實世界數據集中此類案例的代表性不足。為彌補數據多樣性的缺失，我們提出一種新穎且可擴展的仿真框架，能夠基於現有駕駛日誌合成大量未見狀態。我們的流程採用先進的神經渲染技術與反應式環境，通過擾動自車軌跡生成高保真度的多視角觀測數據。此外，我們針對這些新仿真狀態開發了偽專家軌跡生成機制，以提供動作監督。基於合成數據，我們發現對真實世界樣本和仿真樣本進行簡單的協同訓練策略，能顯著提升各類規劃方法在挑戰性真實基準測試中的魯棒性和泛化能力——在navhard基準上最高提升6.8 EPDMS，在navtest基準上提升2.9分。更重要的是，僅通過增加仿真數據（無需額外真實數據流），這種策略改進便能實現平滑擴展。我們進一步揭示了此類仿真-真實學習系統（命名為SimScale）的關鍵發現，包括偽專家設計機制以及不同策略架構的擴展特性。我們的仿真數據與代碼將開源發布。

English

Achieving fully autonomous driving systems requires learning rational decisions in a wide span of scenarios, including safety-critical and out-of-distribution ones. However, such cases are underrepresented in real-world corpus collected by human experts. To complement for the lack of data diversity, we introduce a novel and scalable simulation framework capable of synthesizing massive unseen states upon existing driving logs. Our pipeline utilizes advanced neural rendering with a reactive environment to generate high-fidelity multi-view observations controlled by the perturbed ego trajectory. Furthermore, we develop a pseudo-expert trajectory generation mechanism for these newly simulated states to provide action supervision. Upon the synthesized data, we find that a simple co-training strategy on both real-world and simulated samples can lead to significant improvements in both robustness and generalization for various planning methods on challenging real-world benchmarks, up to +6.8 EPDMS on navhard and +2.9 on navtest. More importantly, such policy improvement scales smoothly by increasing simulation data only, even without extra real-world data streaming in. We further reveal several crucial findings of such a sim-real learning system, which we term SimScale, including the design of pseudo-experts and the scaling properties for different policy architectures. Our simulation data and code would be released.

SimScale：基於大規模真實世界模擬的駕駛學習技術

SimScale: Learning to Drive via Real-World Simulation at Scale

摘要

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