In that sense, an AD pipeline should be tested in countless environments and scenarios, escalating the cost and development time exponentially with a physical approach. Urban complex scenarios are the most challenging situations in the field of Autonomous Driving (AD). We also compare the extracted library with a custom selection of primitives regarding the performance of obtained solutions for a street layout based on a real-world scenario. Primitives are identified based on data from human driving, with the freedom to build libraries of different sizes as a parameter of choice. We illustrate our technique in an autonomous driving application. The library is designed based on primitives with highest occurrences within the data set, while Lie group symmetries from a model are analysed in the available data to allow for structure-exploiting primitives. In this work, we propose a method combining data with a dynamical model to optimally select primitives. In the literature, the library is usually designed by either learning from demonstration, relying entirely on data, or by model-based approaches, with the advantage of exploiting the dynamical system’s property, e.g., symmetries. The selection of primitives is then crucial for assuring feasible solutions and good performance within the motion planner.
Motion planning methods often rely on libraries of primitives.
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