  In order for autonomous vehicles to become a part of the Intelligent
Transportation Ecosystem, they are required to guarantee a particular level of
safety. For that to happen a safe vehicle control algorithms need to be
developed, which include assessing the probability of a collision while driving
along a given trajectory and selecting control signals that minimize this
probability. In this paper, we propose a speed control system that estimates a
collision probability taking into account static and dynamic obstacles as well
as ego-pose uncertainty and chooses the maximum safe speed. For that, the
planned trajectory is converted by the control system into control signals that
form input for the dynamic vehicle model. The model predicts a real vehicle
path. The predicted trajectory is generated for each particle -- a weighted by
a probability hypothesis of the localization system about the vehicle pose.
Based on the predicted particles' trajectories, the probability of collision is
calculated, and a decision is made on the maximum safe speed. The proposed
algorithm was validated on the real autonomous vehicle. The experimental
results demonstrate that the proposed speed control system reduces the vehicle
speed to a safe value when performing maneuvers and driving through narrow
openings. Therefore the observed behavior of the system is mimicking a human
driver behavior when driving in difficult and ambiguous traffic situations.
