Spatial Identification and Network Vulnerability Analysis of Autonomous Vehicle Pick-Up Locations: A Data-Driven Complex Network Approach

Engineering notes

Key topics: autonomous driving, autonomous vehicle, waymo. 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

With the accelerating commercialization of autonomous driving technology, robotaxis have emerged as a significant force in reshaping urban transportation systems. However, their service efficiency and system resilience depend heavily on the spatial layout and network structure of pick-up points. Utilizing the Waymo Open Motion Dataset comprising 2,316,135 motion trajectories, this study proposes a multi-stage analytical framework to systematically identify autonomous vehicle pick-up points and evaluate the vulnerability of the constructed network. First, trajectories are stratified using kinematic criteria, and K-Means clustering is applied to 12 kinematic and geometric features to distinguish genuine pick-up and drop-off (PUDO) events from traffic-related stops. The identified pick-up points are then aggregated into spatial grid nodes to construct an undirected, unweighted network. Finally, network vulnerability is assessed by simulating random failures and three types of targeted attacks. The findings reveal that: (1) identifies 21,503 candidate pick-up points exhibiting pronounced curbside-departure characteristics from 111,321 stop-to-go trajectories. (2) The network exhibits global sparsity and high local clustering; the largest connected component (LCC) encompasses 66.1% of nodes, forming a primary service area covering the urban core, while the remaining 33.9% are scattered across 377 isolated fragments. (3) The network demonstrates strong robustness against random failures but is highly vulnerable to targeted attacks on high-betweenness centrality nodes. Removing merely the top 5% of such nodes reduces the LCC to 36.7%, and at 20% removal the LCC drops to 3.5% with near-complete loss of global efficiency. This study contributes a reproducible, machine learning-based methodology for extracting pick-up points from trajectory data and reveals structural vulnerabilities in autonomous driving service networks from a complex network perspective, providing quantitative evidence for enhancing the resilience of future urban intelligent transportation systems.

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