Driver Hazard Perception Modeling From Gaze and Vehicle Behavior

The system creates a digital twin model that copies and simulates the driver's actual driving behavior patterns from real-world data, rather than relying on laboratory video clip assessments. This digital twin replicates the driver's genuine hazard perception and response characteristics under real driving conditions, eliminating the artificiality of lab settings while maintaining assessment control.

The patent introduces an intermediary AI assessment system that mediates between the driver and hazard evaluation. Instead of directly assessing drivers in lab settings or relying on self-reporting, the system uses intermediate digital twin models and behavioral analysis algorithms to objectively evaluate hazard perception through real driving data, removing the subjectivity and artificial awareness present in laboratory assessments.

The system segments the assessment process into two distinct phases: an offline training phase where individual driver digital twin models are created from historical data, and an online assessment phase where these personalized models evaluate current driving behavior. This segmentation allows the system to maintain simplicity during actual assessment while incorporating detailed personalization through pre-trained models.

The system performs preliminary actions by training personalized digital twin models offline using historical driving data before actual hazard perception assessment is needed. This preliminary model training captures individual driver characteristics, behaviors, and response patterns, enabling accurate personalized assessment during online operation without adding complexity to the real-time assessment process.

The system dynamically adapts its assessment process by using pre-trained digital twin models that can be selectively applied based on driving conditions and contexts. Rather than running complex analyses for all scenarios, the system dynamically selects appropriate assessment strategies and data processing levels, reducing computational complexity while maintaining precision when needed.

The digital twin models serve themselves by being trained once on historical data and then autonomously performing repeated assessments without requiring reprocessing of raw data. The models self-manage the complexity of pattern recognition and behavioral analysis, converting complex real-time data into simplified hazard perception evaluations automatically.