SafeCam: Analyzing intersection-related driver behaviors using multi-sensor smartphones
Bibliographic record
Abstract
A large number of car accidents occur at intersections every year mainly due to drivers' "illegal maneuver" or "unsafe behavior". To promote traffic safety, we present SafeCam, a smartphone-based system that jointly leverages vehicle dynamics and the real-time traffic control information (e.g., traffic signals) to detect and study driver dangerous behaviors at intersections. In particular, SafeCam uses embedded sensors (i.e., inertial sensors) on the phone to generate soft hints tracking different driving conditions while at the same time adopts vision-based algorithms to recognize intersection-related critical driving events including unsafe turns, running stop signs and running red lights. In order to improve the system efficiency, we utilize adaptive color filtering under two lighting conditions (e.g., sunny and cloudy) and deploy the subsampling methods to make a trade off between the detection rate and the processing latency. In the evaluation, we conduct real-road driving experiments involving 15 drivers and 6 vehicles. The experiment results demonstrate that SafeCam is robust and effective in real-road driving environments, and has great potential to alert drivers for their dangerous behaviors at intersections and at the same time help them shape safe driving habits. Our experiments also reveal several interesting findings. 1) On average a driver failed to fully stop at stop signs 3 times in a trip of 3.5 km. 2) 11 out of 15 participants have lane drifting problems when they are making turns in the test. 3) Drivers took longer braking time when they approached a stop sign than a red light.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".