iPad: Iterative Proposal-centric End-to-End Autonomous Driving
Journal:
arXiv
Published Date:
May 21, 2025
Abstract
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.
