Driving into the future: a cross-cutting analysis of distributed artificial intelligence, CCAM and the platform economy
DOI: 10.1007/s43684-023-00059-1
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Summary
This paper addresses the complex integration of Distributed Artificial Intelligence (DAI), Cooperative Connected and Automated Mobility (CCAM), and the Platform Economy (PE) to advance autonomous mobility solutions. The authors identify a research gap regarding the insufficient exploration of how these three domains intersect, particularly how DAI enables CCAM within the broader ecosystem of the platform economy. The primary research question seeks to define the interplay and principles derived from these intersections to facilitate the development of future urban transportation systems. The study employs a systematic literature review to analyze the core attributes and intersections of DAI, CCAM, and PE. It further grounds its findings in practical applications through two research projects: DIGINET-PS, which tested distributed intelligence approaches in a digital test track in Berlin, and the ongoing BeIntelli project, which conceptualizes a holistic AI-in-mobility framework based on platform economy principles. The authors define DAI as a threefold approach spanning Vehicle, Edge, and Cloud entities; CCAM as the technological umbrella for connectivity, cooperation, and automation; and PE as the model for value creation through network effects among platform actors. Key findings reveal that DAI provides the technical foundation for CCAM by enabling distributed computing, cooperative perception, and federated learning across vehicles, roadside infrastructure, and cloud systems. This distributed approach addresses limitations in onboard computing power and perception range. The intersection with the Platform Economy is characterized by the modeling of multi-agent systems where physical agents (vehicles, infrastructure) and market actors (users, providers) interact via platform ecosystems. The authors propose a blueprint architecture for autonomous mobility that integrates hardware, AI middleware, and advanced driving assistant systems across the Vehicle, Edge, and Cloud layers. This architecture supports data provisioning, service platforms, and development tools, enabling the emergence of scalable platform ecosystems that leverage network effects. The significance of this work lies in providing a structured framework for conceiving, designing, and implementing autonomous mobility systems. By clarifying the roles of DAI, CCAM, and PE, the paper facilitates the transition from isolated technical innovations to integrated, data-driven business models. The proposed blueprint serves as a support system for validating future solutions, emphasizing that successful autonomous mobility requires not only technical feasibility but also robust platform mechanisms to manage data exchange, service delivery, and stakeholder interaction. This contributes to the broader field by offering a comprehensive view of how distributed intelligence and platform economies converge to enable sustainable and efficient urban transportation.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-20 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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