Driver Safety & Scoring

GlonexTrack's driver safety platform combines telemetry-based behaviour analysis, camera AI, and usage-based insurance intelligence into a comprehensive system that protects drivers, reduces accidents, and lowers fleet risk profiles. At its core is a composite safety score calculated from multiple weighted factors including harsh acceleration, harsh braking, sharp cornering, speeding relative to posted limits, and time-of-day risk multipliers — giving fleet managers a single, intuitive metric for each driver's risk profile. Harsh event detection uses accelerometer data and GPS-derived motion analysis to identify dangerous driving moments in real time.

Each event is classified by severity, tagged with precise GPS coordinates, and — when camera hardware is installed — automatically paired with video clips from both road-facing and driver-facing cameras, providing irrefutable context for coaching conversations or incident investigations. The Usage-Based Insurance (UBI) risk scoring engine processes driving behaviour data through actuarially validated models to generate per-driver and per-vehicle risk scores. Explainable API responses break down each score into contributing factors, enabling insurers to offer transparent premium adjustments and fleet operators to target specific behaviours for improvement.

Integration with leading South African and pan-African insurers enables automatic score synchronization for policy management. Compliance monitoring tracks regulated driving hours, mandatory rest periods, licence validity, and vehicle roadworthiness certificates, generating automated warnings as thresholds approach. Gamification features transform safety from a compliance burden into a competitive advantage — driver leaderboards, achievement badges, and team challenges motivate sustained behaviour improvement with measurable results. AI-powered coaching recommendations analyse each driver's event history and safety trends to generate personalized improvement plans. Rather than generic training, drivers receive targeted feedback on their specific weaknesses, supported by video evidence and benchmark comparisons against top-performing peers in the fleet.