Bibliographic record
Abstract
This literature review describes the safety, regulatory, financial, energy, environmental, security, and privacy implications of the introduction of Autonomous Vehicles (AVs), including Connected Vehicles (CVs). Specifically, this review is intended to provide background information and context to inform municipal policy- and decision-makers at the City of Toronto, ON, Canada, of ongoing developments and speculations related to AVs and CVs. Discussion of policy and regulatory frameworks introduced or under consideration in other jurisdictions is included where considered to be substantial and relevant. While this paper is intended primarily to inform municipal policy, regulation and legislation from all levels of government across Canada and around the world have been incorporated. The scientific and technological developments facilitating the introduction of these vehicles – i.e., how AVs are being introduced – are examined only as and where necessary to provide perspective regarding what municipalities should begin to prepare for. Beyond AVs, other forms of intelligent transportation equipment and services (such as drones and wireless communication) are discussed sparingly, and only where considered relevant to municipal services and regulation. This paper considers peer-reviewed journal articles, newspaper and website articles, academic simulation studies, theses, reports from think tanks, governmental policy analyses, and other diverse sources. As of the time of writing, many of the studies, projects, and developments described are in progress; others are in various planning stages, a handful have been completed, and many are merely speculative. The umbrella term “AV” is used to refer to any and all autonomous/driverless vehicles, of which connected vehicles are considered to be a subset. In cases where a distinction needs to be drawn between a standalone autonomous vehicle (standalone AV) versus a connected vehicle (CV), that distinction is made explicit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".