Assessing the Complexity of Potential Bicycle Interference on Vehicles on Urban Road Segments
Bibliographic record
Abstract
Interacting with bicycles on urban road segments is complex for vehicles, due to the diverse and flexible cycling behavior that can cause interferences. Studies show that driving mixed with bicycles is also a great challenge for autonomous vehicles (AVs), and it is necessary to consider the interference of bicycles when selecting public roads for testing. However, existing road evaluation methods mostly focus on autonomous driving functions and accident analysis, although bicycles have been considered, often with insufficient consideration of their interference. This study analyzes two types of cycling behavior that could interfere with vehicles, including lateral (turning handlebars) and longitudinal (braking or accelerating) behavior, with each occurrence of such behavior considered as one potential lateral or longitudinal interference. From the perspective of cycling behavior, a framework is proposed to assess the complexity of potential bicycle interference on vehicles on road segments. A higher frequency of both potential lateral and longitudinal interference represents a higher complexity of potential interference. A naturalistic field experiment was conducted to collect the potential lateral and longitudinal interference frequency and the environmental parameters of road segments. The quantile regression model was applied to analyze the environmental factors influencing different interference frequencies separately and further establish the assessing model of the potential bicycle interference complexity, and the usability of the model has been demonstrated with a case study. Results show that the potential interference complexity varies across road segments, with some factors leading to more frequent potential lateral and longitudinal interference but with varying degrees of impact (such as the separation between bicycles and vehicles), while some only affect the lateral interference frequency (such as the on‐street parking condition). The proposed framework can help autonomous driving companies or evaluation agencies to select appropriate testing roads, thus promoting the development of autonomous driving.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".