Autonomous Vehicle-Cyclist Interaction: Peril and Promise
Bibliographic record
Abstract
Autonomous vehicles (AVs) will redefine interactions between road users. Presently, cyclists and drivers communicate through implicit cues (vehicle motion) and explicit but imprecise signals (hand gestures, horns). Future AVs could consistently communicate awareness and intent and other feedback to cyclists based on their sensor data. We present an exploration of AV-cyclist interaction, starting with preliminary design studies which informed the implementation of an immersive VR AV-cyclist simulator, and the design and evaluation of a number of AV-cyclist interfaces. Our findings suggest that AV-cyclist interfaces can improve rider confidence in lane merging scenarios. We contribute an AV-cyclist immersive simulator, insights on trade-offs of various aspects of AV-cyclist interaction design including modalities, location, and complexity, and positive results suggesting improved rider confidence due to AV-cyclist interaction. While we are encouraged by the potential positive impact AV-cyclist interfaces can have on cyclist culture, we also emphasize the risks over-reliance can pose to cyclists.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".