Opportunities and Challenges of GIS in the Era of Artificial Intelligence

-- Taking the driverless car as an example

Abstract

 In the era of artificial intelligence, the deep integration of Geographic Information Systems (GIS) and autonomous vehicles is driving a paradigm shift in spatial decision-making from human experience to algorithmic autonomy. Through interdisciplinary analysis, this paper reveals three key opportunities and structural challenges for GIS-enabled autonomous driving. Technologically, the intelligent evolution of high-precision maps provides vehicles with centimeter-level spatial awareness, while multi-source perception fusion significantly enhances positioning robustness in complex scenarios. Vehicle-road coordination mechanisms have restructured traffic resource allocation models. However, technological progress faces fundamental contradictions: dynamic updates of high-precision maps are constrained by 40% positioning failure in satellite-obscured areas and the exorbitant cost of LiDAR, sparking data ownership disputes; multi-source heterogeneous data fusion encounters 20cm-level coordinate system conversion errors and semantic gaps in traffic signage; fragmented standards and institutional vacuums lead to algorithmic spatial discrimination and national security risks. The study demonstrates that GIS opportunities essentially involve transferring spatial decision-making authority to algorithms, while challenges stem from structural imbalances between technological efficacy and social governance. Future efforts should establish a tripartite governance framework integrating "federated learning updates, algorithm audits, and national security desensitization" to achieve synergistic evolution between technological innovation and spatial justice.