Estimating Automobile Crash Characteristics from Images using Deep Learning
Bibliographic record
Abstract
Crash characteristics such as crash velocity (Delta-V) and location of collision (LOC) are important determinants of the severity of the injury sustained by an occupant of an accident vehicle. Based on the predicted severity levels of injury, insurance companies can estimate the claim’s cost and better plan their financial reserves. We present a promising approach for accurately predicting Delta-V and LOC using deep learning methods, without the need for a forensic crash reconstruction. We constrain the study to small passenger vehicles and to front and rear collisions with crash velocities under 96 kph. We first develop and refine our image processing and deep CNN architectures using images created by using vehicle crash simulation software. Using a k-fold cross-validation approach, our methods are able to predict the crash velocity of simulated collisions (108 images) with a MAE of 3.41 kph (MAPE of 8.2%). Similarly, a multiple task learning CNN is able to predict Delta-V of real-world collisions (310 images) with a MAE of 4.19 kph (MAPE of 16.2%) and classify the LOC with 92% accuracy.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".