A-TTC: a multimodal fusion framework for personalized truck forward collision warning via dynamic threshold calibration

Heavy trucks experience persistent forward collision risks due to driver heterogeneity and limitations of fixed-threshold Forward Collision Warning (FCW) systems, which fail to adapt to dynamic driver behavior categories and multimodal contextual cues. This study proposes the Adaptive Time-to-Avoidance (A-TTC) framework, a driver-in-the-loop FCW framework developed using a naturalistic dataset of 3,519 video-verified FCW-triggered events from 569 heavy trucks. To capture behavioral variability, a Multimodal Temporal Alignment Neural Network (MMTANN) is introduced, explicitly synchronizing facial cues, road scenes, and vehicle dynamics to infer driver behavior categories (Distracted, Normal, Harsh) with 84.80% accuracy. Leveraging this behavior category awareness, a hybrid approach utilizes a Gradient Boosting Decision Tree (GBDT) for real-time reaction-time prediction and a convolutional-enhanced Transformer (C-Trans) for braking-distance estimation, reducing prediction error (RMSE) by 51.8% over standard LSTM. These parameters dynamically calibrate a personalized TTA threshold against the real-time predicted minimum Time-to-Collision (TTC). In a retrospective event-based evaluation on pre-triggered FCW logs, A-TTC achieved an overall accuracy of 81.42% and reduced the event-level nuisance-warning proportion to 13.74%, while maintaining a threat-event recall of 89.25%. This research provides a data-driven and driver-adaptive approach to enhancing the safety and personalization of commercial vehicle collision avoidance systems.