The field of autonomous driving has seen increasing proposed use of machine learning methodologies. However, there are still challenges in applying such methods since autonomous driving involves complex and dynamic interactions with the environment. Supervised learning algorithms such as imitation learning can work in environments represented in the training data set, however, it is impractical or cost-prohibitive to collect data for all possible environments. Reinforcement learning methods can train the agent through trial and error, but it may still fail in new environments. To overcome these shortcomings, we thus propose a meta reinforcement learning (MRL) method to improve the agent's generalization capabilities to new environments. The method is applied to automated lane-changing maneuvers at different traffic congestion levels. Specifically, we train the model at light to moderate traffic conditions under a reinforcement learning framework, and then test it at heavy traffic conditions that are never encountered during training. For performance evaluation, we use both collision rate and success rate of the lane-change maneuvers to quantify the safety and effectiveness of the proposed model. A pretrained model is established as a benchmark, which uses the same network structure and training tasks as our proposed model for fair comparison. Simulation results show that the proposed method achieves an overall success rate up to 20% higher than the benchmark model when it is generalized to the new environment of heavy traffic density. The collision rate is also reduced by up to 18% compared with the benchmark model. Furthermore, the proposed model shows more stable and efficient adaptation capabilities and can achieve 100% successful rate and 0% collision rate with only a few steps of gradient update adaptation.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles


    Beteiligte:
    Ye, Fei (Autor:in) / Wang, Pin (Autor:in) / Chan, Ching-Yao (Autor:in) / Zhang, Jiucai (Autor:in)


    Erscheinungsdatum :

    2021-07-11


    Format / Umfang :

    2074208 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    REINFORCEMENT LEARNING FOR AUTONOMOUS LANE CHANGE

    SHI ZHAN / JIANG YIFEI / LI ANG | Europäisches Patentamt | 2024

    Freier Zugriff




    PROACTIVE LANE CHANGE FOR AUTONOMOUS VEHICLES

    WANG LEI / WU YUJIA / ZHAO YUFEI et al. | Europäisches Patentamt | 2022

    Freier Zugriff