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Automated parking is a self-driving feature that has been available in cars for several years now. However, parking assistants in currently produced cars fail to park in more complex scenarios and require the driver to move the car to an expected starting position before the assistant is activated. We overcome these limitations by proposing a planning algorithm consisting of two stages: (1) a geometric planner for maneuvering inside the parking slot and (2) an Rapidly-exploring Random Trees (RRT)-based planner that finds a collision-free path from the initial position to the slot entry. Evaluation of computational experiments demonstrates that improvements over commonly used RRT extensions reduce the parking path cost by 21% and reduce the computation time by 79.5%. The suitability of the algorithm for real-world parking scenarios was verified in physical experiments with Porsche Cayenne.
Vlasak et al. (Tue,) studied this question.