提出高效碰撞概率计算方法，兼顾精度与实时性。

In automated driving, crash mitigation is crucial to ensure passenger safety. Accurate avoidance requires precise knowledge of the object's position and orientation. However, sensor noise and occlusions often result in tracking and prediction uncertainties. To account for these uncertainties, estimating the Probability of Collision (POC) is a critical requirement. While Monte Carlo sampling is a common estimation technique, its high computational demand and stochastic nature often render it unsuitable for real-time applications. Analytical POC calculations are simplified by approximating vehicle geometries using circular bounds. While multi-circle approximations offer higher fidelity than a single circumscribed circle, they significantly increase computational complexity. This paper proposes a shape approximation algorithm, MoCCA, which utilizes a single circle for each vehicle, optimized to minimize the relative distance between them. MoCCA maintains a computational efficiency comparable to standard single-circle techniques while reducing over-conservatism. To address the potential underestimation of POC inherent in partial coverage, we establish an upper bound for the approximation error, demonstrating that it depends primarily on inter-vehicle distance and orientation variance. Furthermore, we introduce a safety distance margin that can be calibrated solely based on orientation variance.