Rethinking Cars for Sustainable Mobility – Shared-Autonomous Vehicles and Circularity
Bibliographic record
Abstract
Autonomous driving cars, underpinned by advancements in artificial intelligence, sensor technology, and the enhanced communication capability of 5G, are now poised to revolutionize transportation, promising significant improvements in safety, efficiency, and accessibility. However, the time for such a transition towards fully autonomous vehicles (AV) should coincide with the transition to Society 5.0, where cars are zero-emission vehicles and fully embedded into a circular economy (CE). This requires a radical change not only for the car industry but also for the car users, who are in the early transition from being car owners to car-users with automobiles becoming a more sustainable public mobility service. In this paper, we conduct a literature review about the state-of-the-art of autonomous driving and circular economy, as both cannot be established without substantial infrastructure enhancements for vehicle-to-everything communication and for building up more sustainable business models for the circular economy. We divide this journey into three different states where we provide a vision of new stakeholders and new business process models via the current state of its infancy, the middle state of its coexistence, and the target state of fully established Shared Autonomous Electric Vehicles (SAEV) and CE services. Given that such a radical change will undoubtedly face resistance, this transition scenario is analyzed using different aspects of power dynamics to show potential benefits and new business opportunities for the different stakeholders involved. In the final section, we show examples of different car manufacturers and how they see their current focus on new energy vehicles (NEV) and aspects of CE in their respective strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".