Direction-Aware Telematics Crash Detection and Injury Plausibility Scoring for False Injury Claim Mitigation

Modern connected-vehicle programs enable insurers and fleet operators to receive high-frequency inertial signals during collisions, supporting both rapid crash detection (for emergency response and First Notice of Loss) and post-loss analytic validation of claimed injuries. However, many deployed pipelines treat crash severity as a scalar (e.g., peak g or delta-v) and ignore impact direction, vehicle pose, and the occupant’s likely loading path. This omission reduces both safety performance (missed oblique and side impacts) and claim integrity performance (false positives and insufficient context for injury plausibility decisions). This paper proposes a direction-aware, end-to-end telematics architecture that jointly estimates (i) crash occurrence, (ii) impact direction in the vehicle reference frame, and (iii) injury plausibility scores for claim triage. The method fuses accelerometer and gyroscope streams with road context (map-matching, speed limit, surface condition proxies) and optional vehicle signals (airbag, belt, ADAS). A physics-based module derives biomechanically motivated bounds over expected injury risk given delta-v, pulse duration, and direction, while a machine-learning model learns residual patterns associated with suspicious injury narratives. The design supports stream processing for real-time alerts and batch re-scoring for audits, and includes governance controls for privacy and regulated data handling. Using representative evaluation scenarios constructed from mixed telematics datasets, the proposed direction-aware model improves suspicious-claim detection AUC by ~0.06 over severity-only baselines while keeping real-time scoring within sub-second latency. We discuss implementation considerations, failure modes, and responsible deployment practices.

Telematics, crash detection, impact direction, delta-v, injury biomechanics, fraud analytics, FNOL, claim triage, streaming ML.

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