End-to-end Autonomous Driving: Challenges and Frontiers

arXiv:2306.16927v2 Announce Type: replace-cross
Abstract: The autonomous driving community has witnessed a rapid growth in approaches that embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle motion plans, instead of concentrating on individual tasks such as detection and motion prediction. End-to-end systems, in comparison to modular pipelines, benefit from joint feature optimization for perception and planning. This field has flourished due to the availability of large-scale datasets, closed-loop evaluation, and the increasing need for autonomous driving algorithms …

arxiv autonomous autonomous driving challenges cs.ai cs.cv cs.lg cs.ro driving frontiers type