The autonomous driving industry has undergone leaps and bounds of research to reach the mainstream market, with major players investing heavily to improve the technology further. Industry and academia are currently working to make the technology safe, reliable and robust. Autonomous racing provides this opportunity, to improve the technology to the point, where it can utilize the full physical capability of the vehicle in a wide range of operational conditions. Multiple functionalities are required to make the car autonomous, this thesis focuses on the path planning module for autonomous racing. We evaluated the performance of dynamic models, using different adaptive dynamic constraints, implemented for path planning. The evaluation is based on framework for optimization based motion planning[1] The optimization problem is solved by "Sampling Augmented Real Time Iteration (SAARTI) motion planning scheme". Four different models were studied during this thesis and include dynamic bicycle models, with static and dynamic constraints. Parameters affecting the planning performance were identified, and the trade-off between model complexity and planning horizon, was investigated by varying these parameters and the differences in performance was studied. The generalizability of results for different driving conditions was investigated for these parameter configurations. Batch simulations were performed to account for various possible scenarios of different parameter configurations, to ensure results closest to reality. The simulation was conducted with, hardware in the loop setup running the planning node, to get a realistic estimation of the computation resources. Batch simulations were instrumental in showing interesting trends of how the input parameters affected the planning performance. Simulations provided extensive proof of the different dynamic constraints improving the planning performance, over the basic dynamic model under extreme driving conditions. When reviewing the results from the simulations, the ...


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    Title :

    Traction Adaptive trajectory planning for autonomous racing ; Greppadaptiv rörelseplanering för autonom racing



    Publication date :

    2020-01-01


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629