This paper presents a vision-based collision-warning system for ADAS in intelligent vehicles, with a focus on urban scenarios. In most current systems, collision warnings are based on radar, or on monocular vision using pattern recognition. Since detecting collisions is a core functionality of intelligent vehicles, redundancy is essential, so that we explore the use of stereo vision. First, our approach is generic and class-agnostic, since it can detect general obstacles that are on a colliding path with the ego-vehicle without relying on semantic information. The framework estimates disparity and flow from a stereo video stream and calculates stixels. Then, the second contribution is the use of the new asteroids concept as a consecutive step. This step samples particles based on a probabilistic uncertainty analysis of the measurement process to model potential collisions. Third, this is all enclosed in a Bayesian histogram filter around a newly introduced time-to-collision versus angle-of-impact state space. The evaluation shows that the system correctly avoids any false warnings on the real-world KITTI dataset, detects all collisions in a newly simulated dataset when the obstacle is higher than 0.4m, and performs excellent on our new qualitative real-world data with near-collisions, both in daytime and nighttime conditions.