The governance of AI-enabled transport: bridging Kuwait’s tech-policy gap vis-à-vis the UAE and Singapore

Engineering notes

Using a mixed-methods design (39 documents, 20 key-informant interviews; 2025 cutoff), Kuwait serves as the primary case study, contextualized against documentary benchmarks from the United Arab Emirates (UAE) and Singapore. The benchmarks demonstrate near parity for analytics, while autonomy remains in a pacing gap pending liability and insurance frameworks.

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

Artificial intelligence (AI) for urban mobility, including adaptive signal control, incident management, predictive maintenance, Mobility as a Service, and Level 4 shuttles, is technically mature, yet scale depends on institutional readiness. We propose a Technology-Policy Readiness Matrix (TPRM) coupling Technology Readiness Levels (TRL, one to nine) with a custom nine-rung Policy Readiness Level (PRL). To evaluate deployment risk, the matrix plots initiatives into qualitative alignment zones based on their Ordinal Divergence ( D ordinal ): Synchronized (Green), Pacing Gap (Amber), and Institutional Void (Red). Using a mixed-methods design (39 documents, 20 key-informant interviews; 2025 cutoff), Kuwait serves as the primary case study, contextualized against documentary benchmarks from the United Arab Emirates (UAE) and Singapore. Findings reveal a structural “hardware-first” bias rooted in Rentier State incentives: while analytics sit at TRL six to nine, policy readiness lags at PRL two to four. This divergence creates an Institutional Void driven by institutional decoupling, where the state prioritizes allocative procurement over the regulatory enforcement required to routinize data sharing and application programming interfaces (APIs). The benchmarks demonstrate near parity for analytics, while autonomy remains in a pacing gap pending liability and insurance frameworks. A targeted sequence of governance mechanisms lifts PRL by one to two rungs: transport data and API circulars, single-window permitting, KPI-linked public–private partnerships, and standardized assurance with safety cases for autonomous vehicle pilots. A 2026 to 2035 roadmap models three pathways (Managed Transition, Accelerated Innovation, and Policy Lag) setting triggers for API enforcement, KPI disclosure, and sandbox licenses. Closing the readiness gap requires a shift from allocative capacity to regulatory autonomy; doing so yields earlier gains in travel time, ensures compliance with national decarbonization targets, and provides a portable diagnostic for derisking autonomy.

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