Driver Response Analysis in Car-Following Scenarios Using Differential Global Positioning System
Bibliographic record
Abstract
Car following has become a high interest research topic over the last few years- advanced automatic vehicle control system applications in particular. Research initiatives such as the Collision Avoidance Metrics Partnership (CAMP) and Canada’s Auto21 have acquired a wealth of understanding in human car following behaviour over the last few years. However, there is a considerable shortage of fundamental research and relevant field data. Major research initiatives such as CAMP have adopted a more application oriented research approach, thus leaving a void in more generalized research. This paper investigates driver reaction times and responses with varying vehicle dynamics as applicable to a modified Action Point-based car following model. This study uses Differential Global Positioning System (DGPS) to position vehicles, a breakthrough technology that enables centimetre-level positioning and comparable speed measurement capability. Several hours of field data was collected in a 10 km loop of highways with three test drivers. The speed-headway correlation and its dependency on driver preference is assessed. In light of minimizing driver-specific influence, vehicle speed was considered as the analysis variable. Furthermore, two variables are investigated as possible driver response triggers, namely headway and headway rate. This research found that driver reaction time is speed invariant and driver dependent. With respect to driver response, both acceleration and deceleration responses were found to be highly dependent on vehicle speed. Models are presented for two drivers who participated in the field experiments. Field observations on driver sensitivity to headway changes are also presented and discussed.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".