2403.09491
On using Machine Learning Algorithms for Motorcycle Collision Detection
Philipp Rodegast, Steffen Maier, Jonas Kneifl, Jörg Fehr
correcthigh confidence
- Category
- Not specified
- Journal tier
- Specialist/Solid
- Processed
- Sep 28, 2025, 12:56 AM
- arXiv Links
- Abstract ↗PDF ↗
Audit review
The candidate solution faithfully reproduces the paper’s setup and findings: fsample = 2 kHz; per-model activation thresholds tuned to yield zero false predictions on control (A.1/A.2) scenarios; 12 ms requirement; and the ISO 13232 validation grouping. Table 8’s tuned thresholds already render AdaBoost (26→13.0 ms) and Gradient Boost (25→12.5 ms) time-incapable at 12 ms, exactly as stated in the paper. The Figure 10 narrative explicitly reports that only the Neural Net meets ≤12 ms for frontal, lateral, and rear-end accidents; SVM meets lateral/rear but not frontal; and no model meets grazing—matching the candidate’s category-wise conclusions.
Referee report (LaTeX)
\textbf{Recommendation:} minor revisions
\textbf{Journal Tier:} specialist/solid
\textbf{Justification:}
A solid domain-application paper with clear constraints and interpretable results. The workflow (simulation database, ISO-based validation, zero-false-prediction threshold tuning, decision-delay assessment) is coherent and supports the conclusions. The main findings—time-incapability of AdaBoost/Gradient Boost under tuned thresholds, and category-wise timeliness led by a simple MLP—are well supported. Strengthening with worst-case delay reporting and broader control scenarios would further substantiate safety-critical claims.