Quantitative Analysis of Situation Awareness During Autonomous Vehicle Handover on the Da Vinci Research Kit
Bibliographic record
Abstract
The current trends in the research and development of self-driving technology aim for Level 3+ autonomy, where the vehicle controls both lateral and longitudinal motions of the dynamic driving task, while the driver is permitted to divert their attention, as long as they are able to react properly to a handover request initiated by the vehicle. At this level of autonomy, situation awareness of the human driver has become one of the most important metrics of safety. This paper presents the results of a user study to evaluate handover performance at Level 3 autonomy. The study investigates whether the level of situation awareness during critical handover situations has a direct impact on task performance, with higher situation awareness expected to lead to better outcomes during emergency interventions. The study is performed in a simulated environment, using the CARLA driving simulator and the master console of the da Vinci Surgical System. The test subjects were asked to answer the questions of a questionnaire during the experiment; the answers for those questions and the measured control signals were analyzed to gain further knowledge on the safety of the handover process.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".