iPad: 以候选方案为中心的迭代式端到端自动驾驶
iPad: Iterative Proposal-centric End-to-End Autonomous Driving
AI总结：
针对现有端到端自动驾驶方法效率低、规划感知有限的问题，提出以候选方案为核心的迭代框架iPad，通过ProFormer编码器和两项轻量辅助任务提升性能，在基准测试中达SOTA且效率更高。
摘要：
端到端（E2E）自动驾驶系统减少了信息损失与误差累积，是传统模块化流水线的一种极具前景的替代方案，在提升出行效率与安全性方面潜力巨大。然而，现有多数端到端方法直接基于密集鸟瞰图（BEV）网格特征生成规划，存在效率低下、规划感知能力有限的问题。针对这些局限，我们提出以候选方案为中心的迭代式自动驾驶框架iPad，这一全新框架将候选方案（即一组未来候选规划）置于特征提取与辅助任务的核心位置。iPad的核心是ProFormer——一种鸟瞰图编码器，它通过以候选方案为锚点的注意力机制迭代优化候选方案及其关联特征，有效融合多视角图像数据。此外，我们还引入了两项轻量、以候选方案为中心的辅助任务——建图与预测，以极低的计算开销提升规划质量。在NAVSIM和CARLA Bench2Drive基准上的大量实验表明，iPad实现了当前最优性能，同时效率显著高于此前的领先方法。
英文摘要：
End-to-end (E2E) autonomous driving systems offer a promising alternative to traditional modular pipelines by reducing information loss and error accumulation, with significant potential to enhance both mobility and safety. However, most existing E2E approaches directly generate plans based on dense bird's-eye view (BEV) grid features, leading to inefficiency and limited planning awareness. To address these limitations, we propose iterative Proposal-centric autonomous driving (iPad), a novel framework that places proposals - a set of candidate future plans - at the center of feature extraction and auxiliary tasks. Central to iPad is ProFormer, a BEV encoder that iteratively refines proposals and their associated features through proposal-anchored attention, effectively fusing multi-view image data. Additionally, we introduce two lightweight, proposal-centric auxiliary tasks - mapping and prediction - that improve planning quality with minimal computational overhead. Extensive experiments on the NAVSIM and CARLA Bench2Drive benchmarks demonstrate that iPad achieves state-of-the-art performance while being significantly more efficient than prior leading methods.
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