As the number and variety of traffic tools continue to increase, avoiding collisions is becoming increasingly challenging. To proactively prevent collisions, it’s essential to quantify the associated risk. While the Monte Carlo method offers superior accuracy in this regard, its reliance on extensive sampling renders it time-consuming and challenging to implement. This paper introduces a quantification method for collision risk that incorporates a safety distance and calculates collision probabilities for trajectories with uncertainty. Simulation results show that the proposed method performs well in terms of both accuracy and efficiency compared to the MC method and can be applied to large-scale trajectory data.