SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving

arXiv:2404.06892v1 Announce Type: new
Abstract: End-to-End paradigms use a unified framework to implement multi-tasks in an autonomous driving system. Despite simplicity and clarity, the performance of end-to-end autonomous driving methods on sub-tasks is still far behind the single-task methods. Meanwhile, the widely used dense BEV features in previous end-to-end methods make it costly to extend to more modalities or tasks. In this paper, we propose a Sparse query-centric paradigm for end-to-end Autonomous Driving (SparseAD), where the sparse queries completely represent …

abstract arxiv autonomous autonomous driving autonomous driving system cs.cv driving features framework paradigm performance query simplicity tasks type