Lighting Unit Grouping for Adaptive Headlight Detection

The patent groups adjacently arranged lighting units into a single lighting cluster when they meet specific spatial and temporal criteria. Instead of evaluating each lighting unit independently, the system combines multiple units into one cluster entity, which prevents localized illuminated regions (like pedestrian crossings or traffic lights) from being misidentified as continuous street lighting. This merging approach maintains detection accuracy while significantly reducing false positives.

The system performs preliminary grouping of lighting units into clusters before evaluating whether continuous street lighting is present. By pre-organizing lighting units into spatial-temporal clusters based on distance and time thresholds, the evaluation process can work with consolidated cluster data rather than individual units, improving both accuracy and reliability in the subsequent detection phase.

The system performs preliminary grouping of lighting units into clusters based on spatial and temporal criteria before the final evaluation of continuous street lighting. This pre-processing step organizes lighting data into meaningful clusters, allowing the system to make reliable detection decisions more quickly by evaluating cluster patterns rather than individual units, thus reducing reaction time without sacrificing reliability.

The grouping criteria for lighting units are dynamic and adaptable. The system uses distance thresholds and time thresholds that can be adjusted based on vehicle speed and other contextual factors. This dynamic approach allows the system to maintain high detection reliability while adapting its confirmation requirements to current driving conditions, thereby optimizing reaction time across different scenarios.

The detection system is segmented into distinct functional modules: a grouping module that clusters lighting units based on spatial-temporal criteria, and an evaluation module that assesses continuous street lighting based on cluster patterns. This segmentation allows the complex grouping logic to be isolated and managed separately, reducing the apparent complexity in the overall system architecture while maintaining high reliability through specialized processing in each module.

The system manages complexity by utilizing parameter-based grouping criteria (distance threshold, time threshold, spatial arrangement) that can be adjusted without changing the core grouping logic. By changing parameters rather than restructuring the grouping algorithm, the system maintains simplicity in its core logic while adapting to different operating conditions, thus reducing false positives without proportionally increasing device complexity.