Looking through the Perceptions of Blinds: Potential Impacts of Connected Autonomous Vehicles on Pedestrians with Visual Impairment
Bibliographic record
Abstract
The paper investigates the impacts and barriers posed by connected autonomous vehicles (CAVs) for pedestrians with visual impairment. This study uses a customized web-based survey of visually impaired people from Canada and abroad. Collected data are used to estimate econometric models to identify the critical factors that affect the level of trust in CAVs and the preference for using CAVs from the visually impaired individuals’ perspective. Separate models are estimated for Canadian and non-Canadian samples, as Canadian and non-Canadian participants show some differences in perception and positive attitude towards CAVs. The models reveal that the majority of the respondents prefer to get feedback and alerts from CAVs. Congenitally blind Canadians are less likely to trust CAVs, but non-Canadian congenital blinds tend to trust CAVs. The models also indicate that the respondents who experienced being near an accident with an electric vehicle (EV) are less likely to choose CAVs. Respondents who rely on mobile applications and technology-based devices for navigating purposes tend to trust CAVs. Blind people who rely on conventional navigation tools (e.g., white cane, guide dog, etc.) are less likely to be the users of CAVs. Gender effect is visible, as the female participants tend not to trust CAVs. In relation to policy recommendations, subsidies should be provided to various advocacy groups to offer orientation and mobility (O&M) training services, which are pivotal to educate how to use technology-based navigational services. Also, automobile manufacturers should be enforced to add acoustic vehicle alert systems (AVAS) to both EVs and CAVs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".