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.

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A robot localization problem demands a fair comparison of the positioning algorithms. A reference trajectory of the robot's movement is needed to estimate errors and evaluate a quality of the localization. In this article, we propose the Prior Distribution Refinement method for generating a reference trajectory of a mobile robot with the Monte Carlo-based localization system. The proposed approach can be applied for both indoor and outdoor environments of an arbitrary size without the need for expensive position tracking sensors or intervention in the testing infrastructure. The reference trajectory is generated by running the algorithm over a so-called Particles' Transition Graph, obtained from a resampling stage of Monte Carlo localization. The prior distribution of particles is then refined by forward-backward propagation through the graph and exploring the connections between particles. The Viterbi algorithm is applied afterwards to generate a reference trajectory based on refined particles' distribution. We demonstrate that such an approach is capable of generating accurate estimates of a mobile robot's position and orientation with the only requirement of moderate quality of localization system being used as a core algorithm for iterative optimization.

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