A Pursuit Evasion Approach for Avoiding an Inattentive Human in the Presence of a Static Obstacle
Bibliographic record
Abstract
We consider a mobile robot, modelled as a Dubins’ car, following a path. In the vicinity of the path is a human, modelled as an agile point mass, and a static obstacle. The robot’s objective is to follow the path unless it is absolutely necessary to deviate so as to avoid collision with the static obstacle or human. We seek to guarantee robot’s safety, even if the human is distracted or inattentive, and thus we consider worst-case motions for the human. The resulting problem takes the form of a reversed homicidal chauffeur game, but with the addition of a static obstacle. The static obstacle can interfere with the escape route of the robot, and thus fundamentally changes the form of the game. We propose a navigation algorithm for the robot that provably guarantees safety and that attempts to delay its reaction for as long as possible. We validate the proposed approach in simulation and provide a comparison to existing collision avoidance methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".