Runtime-bounded tunable motion planning for autonomous driving

Trajectory planning methods for on-road autonomous driving are commonly formulated to optimize a Single Objective calculated by accumulating Multiple Weighted Feature terms (SOMWF). Such formulation typically suffers from the lack of planning tunability. Two main causes are the lack of physical intuition and relative feature prioritization due to the complexity of SOMWF, especially when the number of features is big. This paper addresses this issue by proposing a framework with multiple tunable phases of planning, along with two novel techniques: Optimization-free trajectory smoothing/nudging. Sampling-based trajectory search with cascaded ranking.

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