Drivers’ Braking Behaviors in Different Motion Patterns of Vehicle-Bicycle Conflicts
Bibliographic record
Abstract
Bicycling is one of the popular modes of transportation, but bicyclists are easily involved in injuries or fatalities in vehicle-bicycle (V-B) accidents. The AEB (Autonomous Emergency Braking) systems have been developed to avoid collisions, but their adaptiveness needs to be further improved under different motion patterns of V-B conflicts. This paper analyzes drivers’ braking behaviors in different motion patterns of V-B conflicts to improve the performance of Bicyclist-AEB systems. For safety and data reliability, a driving simulator was used to reconstruct two typical conflict types, i.e., SCR (a bicycle crossing the road from right in front of a straight going car) and SSR (a bicycle cut-in from right in front of a straight going car). Either conflict contained various parameterized motion patterns, which were characterized by a combination of parameters: Vc (car velocity), TTC (time-to-collision), Vb (bicycle velocity), and Dlat (lateral distance between the car and the bicycle) or Vlat (maximum lateral velocity of the bicycle). Some 26 licensed drivers participated in an orthogonal experiment for braking behavior analysis. Results revealed that drivers brake immediately when V-B conflicts occur; hence the BRT (brake reaction time) is independent of any motion pattern parameters. This was further verified by another orthogonal experiment with 10 participants using the eye tracking device. BRT in SSR is longer than that in SCR due to the less perceptible risk and drivers’ lower expectation of a collision. The braking intensity and brake Pedal Speed are higher in short-TTC patterns in both conflict types. Therefore, TTC is not a proper activation threshold but a reasonable indicator of braking intensity and Pedal Speed for driver-adaptive AEB systems. By applying the findings in the Bicyclist-AEB, the adaptiveness and acceptability of Bicyclist-AEB systems can be improved.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".