端到端全新范式！复旦VeteranAD："感知即规划"刷新开闭环SOTA，超越DiffusionDrive~

Core Insights - The article introduces a novel "perception-in-plan" paradigm for end-to-end autonomous driving, implemented in the VeteranAD framework, which integrates perception directly into the planning process, enhancing the effectiveness of planning optimization [5][39]. - VeteranAD demonstrates superior performance on challenging benchmarks, NAVSIM and Bench2Drive, showcasing the benefits of tightly coupling perception and planning for improved accuracy and safety in autonomous driving [12][39]. Summary by Sections Introduction - The article discusses significant advancements in end-to-end autonomous driving, emphasizing the need to unify multiple tasks within a single framework to prevent information loss across stages [2][3]. Proposed Framework - VeteranAD framework is designed to embed perception into planning, allowing the perception module to operate more effectively in alignment with planning needs [5][6]. - The framework consists of two core modules: Planning-Aware Holistic Perception and Localized Autoregressive Trajectory Planning, which work together to enhance the performance of end-to-end planning tasks [12][39]. Core Modules - Planning-Aware Holistic Perception: This module interacts across three dimensions—image features, BEV features, and surrounding traffic features—to achieve a comprehensive understanding of traffic elements [6]. - Localized Autoregressive Trajectory Planning: This module generates future trajectories in an autoregressive manner, progressively refining the planned trajectory based on perception results [6][16]. Experimental Results - VeteranAD achieved a PDM Score of 90.2 on the NAVSIM navtest dataset, outperforming previous learning methods and demonstrating its effectiveness in end-to-end planning [21]. - In open-loop evaluations, VeteranAD recorded an average L2 error of 0.60, surpassing all baseline methods, while maintaining competitive performance in closed-loop evaluations [25][33]. Ablation Studies - Ablation studies indicate that the use of guiding points from anchored trajectories is crucial for accurate planning, as removing these points significantly degrades performance [26]. - The combination of both core modules results in enhanced performance, highlighting their complementary nature [26]. Conclusion - The article concludes that the "perception-in-plan" design significantly improves end-to-end planning accuracy and safety, paving the way for future research in more efficient and reliable autonomous driving systems [39].