Intersections account for a significant proportion of bicycle-to-vehicle accidents, where early recognition of a cyclist’s turning intention is essential for collision avoidance and trustworthy warnings. We present a model-based framework that combines map-derived routes with sensor data to estimate maneuver probabilities from position, heading, speed, and yaw rate. Focusing on left/right turns versus going straight, we evaluate how detection accuracy evolves under different temporal horizons and reference points. Results show that incorporating trajectory prediction improves early detection, enabling detection of right-turn intentions up to 3 s before the conflict point. Position provides the most reliable late-stage confirmation within the final 2 s (more than 80% accuracy), while speed provides earlier but less stable cues. These findings reveal the variable-wise, stage-dependent reliability of kinematic variables, providing interpretable guidance for intelligent transportation systems safety systems, including advanced driver assistance systems and vehicle-to-everything applications to protect cyclists at intersections.