Methodology of hierarchical collision avoidance for high‐speed self‐driving vehicle based on motion‐decoupled extraction of scenarios

Collision avoidance is an important requirement for self‐driving systems, particularly in high‐speed scenarios, where a multi‐state coupled motion makes it difficult to simultaneously reach the required accuracy, efficiency, and universal feasibility for different obstacle‐avoidance behaviour. For a coupled multi‐state complexity, a hierarchical collision‐avoidance strategy is proposed that refines the requirements for travelling under such a scenario into two levels, general and special. At the general level, the moving elliptical contour of the subject vehicle is regularised as a settled circle through a projective transformation, which attempts to determine the subject‐motion‐decoupled scenario. Throughout the transformation, all positional relationships between the subject and the object vehicles are retained using invariants. At the special level, a group of relative critical collision trajectories is achieved through a feature‐distance‐based multi‐dimensional geometric optimisation model. Under the motion‐decoupled scenario, a precise collision avoidance condition is constructed by mathematically expressing the relative critical collision trajectory group using a parameterised spatio‐temporal curvilinear interpolation model, which provides a reasonable safety redundancy and trajectory domain to ensure both the efficiency and accuracy of the computation. In a simulation, planning trajectories using this collision‐avoidance strategy is adaptive for different collision‐avoidance behaviour and are more efficient than those of other algorithms.