Autonomous driving paper index
Tamed Wanderers: How AutoNavi Maps Reshapes Urban Spacetime and Individual Existence Through Algorithmic Governance
One-line summary
Taking AutoNavi Maps（Amap）as its subject, this paper explores how algorithms reshape urban mobility and individual existence under the guise of prediction and optimization.
Engineering notes
Key topics: autonomous driving, prediction. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Taking AutoNavi Maps（Amap）as its subject, this paper explores how algorithms reshape urban mobility and individual existence under the guise of prediction and optimization. The central question is: When navigation technology becomes the “default background” of daily actions, at what levels do algorithms participate in the governance of cities and bodies? This study reveals a contemporary shift in power genealogies—from territorial-centered sovereign governance to data-flow-centered generative governance. Through continuous data extraction and integration, AutoNavi transforms urban activities into raw material for algorithmic learning. Its predictive models preemptively intervene in the future, extending governance into unplayed scenarios. Simultaneously, the map's generative practices actively produce spatial order and commercial visibility through ranking and route recommendations. “Algorithmic time” further regulates individual rhythms and emotions, domesticating life within computable temporal frameworks. Research indicates that navigation algorithms not only shape the efficiency of mobility but also, in a subtle yet profound manner, restructure the perceptual framework and existential logic of urban experience.
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