Browsing by Author "Roelofse, Christiaan Roelofse"

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    Probabilistic conflict prediction: an accurate and computationally efficient approach
    (Stellenbosch : Stellenbosch University, 2023-12) Roelofse, Christiaan Roelofse; Van Daalen, Corne E. ; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: Collision (or conflict) prediction is a vital component of motion planning for autonomous vehicles to ensure safe operation, both in the context of autonomous navigation and in the context of an advisory system for manned vehicles. Prediction methods must be accurate to know whether motion planning corrections are required. However, computationally efficient prediction methods are Essential in order to ensure that motion planning corrections are brought about in a timely manner. Efficient prediction methods are especially crucial when testing large sets of candidate trajectories for conflict, given the accumulation of computational cost for each candidate. This dissertation presents a probabilistic conflict prediction method that demonstrates the same accuracy as existing methods, but at a significantly reduced computational cost. This is achieved by a novel reformulation of the conflict prediction problem in terms of the first-passage time using a dimension-reduction transform. First-passage time distributions are analytically derived for a subset of Gaussian motion models which describe vehicle motion. The proposed method is applicable for stochastic processes where the vehicle mean motion can be approximated by linear segments, and the conflict boundary is modelled as – or approximated by – either piece-wise straight lines in 2-D, or neighbouring planes in 3-D. The proposed method was tested in simulation and compared to state-of-the-art conflict prediction methods. These comparison methods consist of two probability flow methods, as well as an instantaneous conflict probability method. The results demonstrate a significant decrease of computation time.