无图端到端智驾到底用什么样的图

Core Viewpoint - The article discusses the various types of maps used in autonomous driving, highlighting the evolution from traditional navigation maps to more advanced SD maps and their implications for driving technology [1][6]. Group 1: Types of Maps - Cockpit Navigation Map: Provides static information such as navigation trajectory link points and dynamic information like lane actions, originally designed for human drivers [1]. - SD Map: An upgraded version of cockpit navigation maps that includes road junction information, allowing for better navigation through complex intersections [1][3]. - SDPro Map: Incorporates lane topology, detailing connections between lanes, which aids in efficient lane changes and navigation [3]. - Light Map: A simplified version of HD maps, relying on visual crowdsourcing for updates, retaining only essential road features [4][5]. - Crowdsourced Maps: Developed by OEMs to keep pace with autonomous driving advancements, allowing for tailored map data that meets specific internal needs [5]. Group 2: Map Evolution and Usage - The evolution of maps shows an increase in features but a decrease in applicability and freshness, indicating a shift in focus towards more specialized maps for autonomous driving [6]. - For end-to-end autonomous driving, SD maps are generally sufficient, but having SDPro or crowdsourced maps is beneficial for accurate prior information [6][7]. - The architecture of end-to-end autonomous driving systems relies heavily on maps as essential inputs for models and rule strategies [7].