AnchDrive：一种新端到端自动驾驶扩散策略（上大&博世）

Core Insights - The article introduces AnchDrive, an end-to-end framework for autonomous driving that effectively addresses the challenges of multimodal behavior and generalization in long-tail scenarios [1][10][38] - AnchDrive utilizes a hybrid trajectory anchor approach, combining dynamic and static anchors to enhance trajectory quality and robustness in planning [10][38] Group 1: Introduction and Background - End-to-end autonomous driving algorithms have gained significant attention due to their superior scalability and adaptability compared to traditional rule-based motion planning methods [4][12] - These methods learn control signals directly from raw sensor data, reducing the complexity of modular design and minimizing cumulative perception errors [4][12] Group 2: Methodology - AnchDrive employs a multi-head trajectory decoder that dynamically generates a set of trajectory anchors, capturing behavioral diversity under local environmental conditions [8][15] - The framework integrates a large-scale static anchor set derived from human driving data, providing cross-scenario behavioral prior knowledge [8][15] Group 3: Experimental Results - In the NAVSIM v2 simulation platform, AnchDrive achieved an Extended Predictive Driver Model Score (EPDMS) of 85.5, indicating its ability to generate robust and contextually appropriate behaviors in complex driving scenarios [9][30][34] - The performance of AnchDrive was significantly higher than existing methods, with an 8.9 point increase in EPDMS compared to VADv2, while reducing the number of trajectory anchors from 8192 to just 20 [34] Group 4: Contributions - The main contributions of the article include the introduction of the AnchDrive framework, which utilizes a truncated diffusion process initialized from a hybrid trajectory anchor set, significantly improving initial trajectory quality and planning robustness [10][38] - The design of a mixed perception model with dense and sparse branches enhances the planner's understanding of obstacles and road geometry [11][18]