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.


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    Title :

    Kinodynamic Motion Planning Using Multi-Objective optimization


    Contributors:


    Publication date :

    2018-06-01


    Size :

    857284 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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