This is like a massive safety report card for modern car safety features (like automatic braking and lane-keeping). It uses real crash data to figure out which features actually reduce injuries, by how much, and in what situations.
Organizations investing billions in ADAS (e.g., automatic emergency braking, lane-keeping assist) lack clear, data-driven evidence on how effective each feature is at reducing injuries in real-world crashes. This work helps quantify safety impact and prioritize which technologies, vehicle platforms, or policy measures deliver the largest injury reduction for the least cost.
Access to large-scale, detailed crash and injury datasets combined with rigorous analytical methodology and domain expertise in traffic safety and automotive engineering.
Classical-ML (Scikit/XGBoost)
Structured SQL
High (Custom Models/Infra)
Data access and integration from heterogeneous crash databases and vehicle/ADAS configuration records; regulatory and privacy constraints on detailed crash and injury data.
Early Majority
Focuses specifically on quantifying real-world injury reduction effectiveness of ADAS using empirical crash data and rigorous analytical methods, rather than only simulation, controlled testing, or marketing claims.