In the realm of autonomous driving, motion planning for the ego vehicle necessitates the prediction of surrounding vehicles' motions. This prediction traditionally relies on object tracking modules containing several sensors to gauge vehicle positions and velocities. However, existing physical or maneuver-based model approaches overlook important aspects of vehicle interactions that significantly affect actual vehicle movement. Ignoring these interactions can lead to inadequate ego vehicle's motion planning. Addressing this gap, this paper proposes a novel approach: the Collision Probability Field (CPF)-based interaction-aware longitudinal motion prediction. Our methodology uniquely integrates the CPF, derived from the uncertainty of sensing information, to account for the probabilistic state of vehicle positions and velocities. This allows the prediction algorithm to consider not just the static data, but also the dynamic interactions between vehicles such as collision. Our approach was tested in various scenarios, including lane changes with an approaching vehicle from behind and different driver behavior models in real-world conditions. Our findings demonstrate a significant improvement in prediction accuracy for the motion planning of ego vehicle, highlighting the importance of interaction-aware predictions in autonomous driving systems.