Motion stereo-based collision avoidance for an intelligent smart car door system
Bibliographic record
Abstract
Driver assistance systems in today's cars assist drivers when the car is in motion. However, there is relatively less, to almost no work on assistance systems when the car is stationary. This paper focuses on such a system, and describes the design and realization of a collision avoidance system for a novel car door assistance system, the smart car door. An omnidirectional camera is attached to each side-view mirror of a car, and the fold-in fold-out movement of the mirror is used to generate 3D information about static obstacles next to the car based on a motion stereo approach. The car door controller uses the sensor data to compute collision-free door opening paths, and the two-hinge kinematic system of the smart car door enables to move the door around static obstacles. The latter increases the door entrance area in tight parking lots, which drastically improves the ingress/egress to/from a vehicle. These are enhancements to currently available systems that only indicate the presence of obstacles next to the car. Experiments on a fully realized prototype demonstrated a robust generation of 3D information about obstacles next to the car. A study with 20 test users showed that our system strongly supports the users to avoid collisions when operating the door.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".