AI-Powered Technology to Keep Cyclists Safe
Bibliographic record
Abstract
Cycling is ubiquitous as a means of transportation and a common hobby to get active outdoors. However, the recent uprise in cycling accidents has led cyclists to feel unsafe on the roads. Copilot by Velo.ai is an AI-based bicycle technology that addresses this problem by collecting spatial and audio-visual data and using AI, preventing accidents before they occur by predicting oncoming vehicles or pedestrians. Copilot is novel in adapting sensory, photography, and AI technology from autonomous vehicles to the cycling industry. Although Copilot is cutting-edge and ground-breaking for cycling safety, it is not accessible to everyone. Barriers to Copilot include disability, unemployment/job security, and employment/working conditions. These social determinants of health (SDoH) interact with each other, as well as intersectional factors, to influence individual access to Copilot, which notably differs depending on the status of one’s country of residence (i.e., developed versus developing). Recent peer-reviewed research confirms the efficacy of the technology used in Copilot and Copilot itself to prevent cycling accidents resulting in injury/death. As technology evolves and machine learning is used to address current limitations, the future of Copilot is promising.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".