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- W2897066388 abstract "As autonomous driving gains importance, universally applicable motion planning approaches that offer safe and comfortable rides have to be developed. Most planning methods up-to-date still struggle when dealing with dynamic environments. They require extensive parameter-fine tuning in order to generate comfortable and safe solutions and it is not known prior to optimization which set of parameters would produce the “best” solution. Therefore, we introduce a multi-objective optimization that plans a set of trajectories using several weights and targets (e.g. desired velocity or lanes). Thus, reducing the need of extensive parameter fine-tuning and increasing the planner’s capabilities to handle dynamic environments. Furthermore, in order to plan multiple trajectories in real-time, a smart-initialization of the optimization problem is introduced that speeds up the multi-objective optimization further. Due to the proposed architecture that consists of a Planning-, Evaluation-and Selection-module, the planner is capable of providing a high level of comfort and safety - even in the case of non-convergence of the optimization. The novel motion planning approach is evaluated in terms of its applicability and performance." @default.
- W2897066388 created "2018-10-26" @default.
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- W2897066388 date "2018-06-01" @default.
- W2897066388 modified "2023-09-23" @default.
- W2897066388 title "Kinodynamic Motion Planning Using Multi-Objective Optimization" @default.
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- W2897066388 doi "https://doi.org/10.1109/ivs.2018.8500363" @default.
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