Finding a safety feature algorithm for electric-scooter by detecting the closest obstacles
Bibliographic record
Abstract
Recently, the e-scooter is more popular and trendy, therefore improvement of the safety feature of e-scooter, a moving vehicle that has a maximum speed about 40 km/hour is very important. Especially, Toronto has just approved these kind ofvehicles that can ride on the bike lanes under the by-law. In this research, an algorithm is proposed to use a single camera for detecting the obstacles using the latest deep learning-based models. This new approach omits the complex alignment and expensive equipment. The deep learning models are employed to judge whether the safe distance exists between the e-scooter and the obstacle. If it detects the obstacles, it reports the closest obstacle by comparing depth information of all obstacles and a warning will be issued based on the obstacle detection. If no obstacle is detected, a message informing about the safe riding situation is announced. The proposed algorithm has the accuracy about 70%. In the future work, by including additional advanced deep learning models for depth estimation and distance calculation, a better performance is expected.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".