To ensure safe autonomous driving in urban environments with complex vehicle-pedestrian interactions, it is critical for Autonomous Vehicles (AVs) to have the ability to predict pedestrians’ short-term and immediate actions in real-time. In recent years, various methods have been developed to study estimating pedestrian behaviors for autonomous driving scenarios, but there is a lack of clear definitions for pedestrian behaviors. In this work, the literature gaps are investigated and a taxonomy is presented for pedestrian behavior characterization. Further, a novel multi-task sequence to sequence Transformer encoders-decoders (TF-ed) architecture is proposed for pedestrian action and trajectory prediction using only ego vehicle camera observations as inputs. The proposed approach is compared against an existing LSTM encoders decoders (LSTM-ed) architecture for action and trajectory prediction. The performance of both models is evaluated on the publicly available Joint Attention Autonomous Driving (JAAD) dataset, CARLA simulation data as well as real-time self-driving shuttle data collected on university campus. Evaluation results illustrate that the proposed method reaches an accuracy of 81% on action prediction task on JAAD testing data and outperforms the LSTM-ed by 7.4%, while LSTM counterpart performs much better on trajectory prediction task for a prediction sequence length of 25 frames.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Learning Pedestrian Actions to Ensure Safe Autonomous Driving


    Beteiligte:
    Huang, Jia (Autor:in) / Gautam, Alvika (Autor:in) / Saripalli, Srikanth (Autor:in)


    Erscheinungsdatum :

    04.06.2023


    Format / Umfang :

    9317658 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Learning Pedestrian Actions to Ensure Safe Autonomous Driving

    Huang, Jia / Gautam, Alvika / Saripalli, Srikanth | ArXiv | 2023

    Freier Zugriff

    Autonomous Surface Site Establishment to Ensure Safe Crew Arrival and Operations

    Jones, Christopher A. / Joyce, Ryan T. / Capenter, Lemuel et al. | NTRS | 2018


    Real-Time Pedestrian Detection for Autonomous Driving

    Yang, Zhiheng / Li, Jun / Li, Huiyun | IEEE | 2018


    SYSTEMS AND METHODS TO ENSURE SAFE DRIVING BEHAVIORS ASSOCIATED WITH REMOTE DRIVING APPLICATIONS

    SCELSI FABRIZIO / DJUKIC BOGDAN / BUCHMUELLER DANIEL et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    Autonomous Surface Site Establishment to Ensure Safe Crew Arrival and Operations

    Jones, Christopher A. / Bender, Theresa / Diaz, Manuel J. et al. | AIAA | 2018