轻舟智航最新GuideFlow：端到端轨迹规划新方案

Core Insights - The article discusses the development of a new planning framework called GuideFlow, which addresses the challenges of trajectory generation in end-to-end autonomous driving by incorporating explicit constraints and enhancing model optimization capabilities [3][11][49] - GuideFlow integrates various conditional signals to guide the generation process, improving the robustness and safety of autonomous driving systems [11][49] Summary by Sections Background Review - End-to-end autonomous driving (E2E-AD) has emerged as an attractive alternative to traditional modular approaches, allowing for unified training through data [9] - Recent advancements have shifted from single-modal to multi-modal trajectory generation to better reflect inherent uncertainties in real driving scenarios [9][10] GuideFlow Framework - GuideFlow explicitly models the flow matching process to alleviate mode collapse issues and flexibly integrates multiple guiding signals [3][11] - The framework combines flow matching with Energy-Based Model (EBM) training to enhance the model's ability to meet physical constraints [3][11] Experimental Results - GuideFlow demonstrated superior performance on various benchmark datasets, achieving state-of-the-art (SOTA) results, particularly on the challenging NavSim dataset with an Extended PMD Score (EPDMS) of 43.0 [3][34][37] - The framework's collision rate was notably low, with an average of 0.07% on the NuScenes dataset, showcasing its safety capabilities [40][41] Contributions and Innovations - The article highlights three core strategies within GuideFlow: speed field constraints, flow state constraints, and EBM flow optimization, which collectively enhance trajectory feasibility and safety [11][28][31] - The integration of driving aggressiveness scoring allows for dynamic adjustments in trajectory styles during inference, further refining the model's adaptability [33][49] Conclusion - GuideFlow represents a significant advancement in trajectory planning for autonomous driving, effectively embedding safety constraints into the generation process and demonstrating robust performance across various datasets [49]