Print Email Facebook Twitter Decentralized Conflict Resolution for Autonomous Vehicles Title Decentralized Conflict Resolution for Autonomous Vehicles Author An, Jerry (TU Delft Mechanical, Maritime and Materials Engineering) Contributor Giordano, G. (mentor) Liu, Changliu (graduation committee) Degree granting institution Delft University of Technology Programme Mechanical Engineering | Systems and Control Date 2020-11-20 Abstract This work presents a decentralized optimization conflict resolution method based on a novel Alternating Directions Method of Multipliers (ADMM) variant and model predictive control (MPC). The variant, titled Online Adaptive Alternating Direction Method of Multipliers (OA-ADMM) aims to unify the application of ADMM to online systems, i.e. systems where fast and adaptive real-time optimization is crucial, into one framework. OA-ADMM introduces two user-designed functions: the similarity function (a forgetting factor between two time steps of the online system) and the adaptation function (adjusting the penalty parameters between updates). The similarity function is what allows OA-ADMM to be applied to online systems where conventional optimization is too slow; the adaptation function allows the user to adjust the online feasibility of the system. We prove convergence in the static case and give requirements for online convergence. Combining OA-ADMM and MPC allows for robust decentralized motion planning and control that seamlessly integrates decentralized conflict resolution, instead of using separate subsystems or hierarchical optimization. The additional robustness is achieved by using the adaptation function of OA-ADMM as an additional safety measure, allowing the prioritization of certain constraints for (nearly) unsafe states, whilst the similarity function allows optimization at the desired control frequency. This method is compared with convention ADMM in Matlab, resulting in significant improvements in robustness and conflict resolution speed. Finally, we compare our OA-ADMM and MPC based decentralized conflict resolution method against conventional decentralized conflict resolution methods in the CARLA vehicle simulator. The results show that the OA-ADMM based method has improved performance, safety, robustness, and generality compared with traditional methods. The method also has fewer requirements in terms of prior knowledge (e.g., the geometry of the intersection), making it usable in almost any situation. Subject Cooperative autonomous systemsOptimization algorithmsTransportation systemsdecentralized collision avoidance To reference this document use: http://resolver.tudelft.nl/uuid:71f37308-ab9a-4512-a2ca-f4a3bb737608 Part of collection Student theses Document type master thesis Rights © 2020 Jerry An Files PDF ThesisAnJ_Final_Version.pdf 17.56 MB Close viewer /islandora/object/uuid:71f37308-ab9a-4512-a2ca-f4a3bb737608/datastream/OBJ/view