This paper addresses the autonomous parking for a vehicle in environments with static and dynamic obstacles. Although parking maneuvering has reached the level of fully automated valet parking, there are still many challenges to realize the parking motion planning in the presence of dynamic obstacles. One of the most famous autonomous driving platforms is the Baidu Apollo platform. In the Apollo platform, this problem is solved using the classic method hybrid A*. However, this method has two main downsides. Firstly, it generates in some parking scenarios, trajectories that consist of many partitions with different gear types and sizes. Such trajectories are intractable by a self-driving car when testing the Apollo planner on more realistic data coming from a simulator such as SVL. Secondly, the built-in algorithm does not have the ability to interact with dynamic obstacles, which might lead to a collision in some critical parking scenarios. To overcome these issues, we proposed a method based on reinforcement learning, which uses the RL-policy (from POLAMP) allowing us to take into account the kinematic constraints of the vehicle, static and dynamic obstacles. The proposed method was fully integrated into the Apollo platform with developed Cyber RT nodes, which were used for publishing the parking trajectory from our algorithm to the SVL simulator through a ROS/Cyber bridge. The final model demonstrates transferability to the previously unseen experimental environments and flexibility with respect to built-in hybrid A*.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Learning Adaptive Parking Maneuvers for Self-driving Cars


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Kovalev, Sergey (Herausgeber:in) / Sukhanov, Andrey (Herausgeber:in) / Akperov, Imran (Herausgeber:in) / Ozdemir, Sebnem (Herausgeber:in) / Gorbov, Gregory (Autor:in) / Jamal, Mais (Autor:in) / Panov, Aleksandr I. (Autor:in)

    Kongress:

    International Conference on Intelligent Information Technologies for Industry ; 2022 ; Istanbul, Turkey October 31, 2022 - November 06, 2022



    Erscheinungsdatum :

    31.10.2022


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    PARKING MANAGEMENT METHOD FOR SELF DRIVING CARS

    SHIM HYEONJEONG / PARK SOO JO | Europäisches Patentamt | 2021

    Freier Zugriff

    PARKING MANAGEMENT METHOD FOR SELF DRIVING CARS

    Europäisches Patentamt | 2022

    Freier Zugriff

    Self-Parking Cars – “Killer Application” for Autonomous Driving?

    Lenz, Barbara / Cyganski, Rita | Deutsches Zentrum für Luft- und Raumfahrt (DLR) | 2017

    Freier Zugriff


    Switchable display during parking maneuvers

    BROGGI ALBERTO | Europäisches Patentamt | 2021

    Freier Zugriff