端到端系列！SpareDrive：基于稀疏场景表示的端到端自动驾驶~

Core Viewpoint - The article discusses the limitations of existing end-to-end methods in autonomous driving, particularly the computational intensity of BEV paradigms and the inefficiency of sequential prediction and planning approaches. It proposes a new Sparse paradigm that allows for parallel processing of prediction and planning tasks [2][5]. Group 1: SparseDrive Methodology - SparseDrive adopts the core ideas from the previous Horizon Sparse series, focusing on sparse scene representation for autonomous driving [3]. - The proposed method modifies the similarities between motion prediction and planning, introducing a hierarchical planning selection strategy [5]. - The architecture includes features such as symmetric sparse perception and a parallel motion planner [5]. Group 2: Training and Performance - The training loss function for SparseDrive is defined as a combination of detection, mapping, motion, planning, and depth losses [9]. - Performance comparisons show that SparseDrive-S achieves a mean Average Precision (mAP) of 0.418, while SparseDrive-B reaches 0.496, outperforming other methods like UniAD [11]. - In motion prediction and planning, SparseDrive-S and SparseDrive-B demonstrate significant improvements in metrics such as minADE and minFDE compared to traditional methods [18]. Group 3: Efficiency Comparison - SparseDrive exhibits superior training and inference efficiency, requiring only 15.2 GB of GPU memory and achieving 9.0 FPS during inference, compared to UniAD's 50.0 GB and 1.8 FPS [20]. - The method's reduced computational requirements make it more accessible for real-time applications in autonomous driving [20]. Group 4: Course and Learning Opportunities - The article promotes a course focused on end-to-end autonomous driving algorithms, covering foundational knowledge, practical implementations, and various algorithmic approaches [29][41]. - The course aims to equip participants with the skills necessary to understand and implement end-to-end solutions in the autonomous driving industry [54][56].