Advanced Driver Assistance Systems (ADAS) with proactive alerts have been used to increase driving safety. Such systems’ performance greatly depends on how accurately and quickly the risky situations and maneuvers are detected. Existing ADAS provide warnings based on the vehicle’s operational status, detection of environments, and the drivers’ overt actions (e.g., using turn signals or steering wheels), which may not give drivers as much as optimal time to react. In this paper, we proposed a spatio-temporal attention-based neural network to predict drivers’ lane-change intention by fusing the videos from both in-cabin and forward perspectives. The Convolutional Neural Network (CNN)-Recursive Neural Network (RNN) network architecture was leveraged to extract both the spatial and temporal information. On top of this network backbone structure, the feature maps from different time steps and perspectives were fused using multi-head self-attention at each resolution of the CNN. The proposed model was trained and evaluated using a processed subset of the MIT Advanced Vehicle Technology (MIT-AVT) dataset which contains synchronized CAN data, 11058-second videos from 3 different views, 548 lane-change events, and 274 non-lane-change events performed by 83 drivers. The results demonstrate that the model achieves 87% F1-score within the 1-second validation window and 70% F1-score within the 5-second validation window with real-time performance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    End-to-End Spatio-Temporal Attention-Based Lane-Change Intention Prediction from Multi-Perspective Cameras


    Beteiligte:
    Zhao, Zhouqiao (Autor:in) / Wei, Zhensong (Autor:in) / Tian, Danyang (Autor:in) / Reimer, Bryan (Autor:in) / Gershon, Pnina (Autor:in) / Moradi-Pari, Ehsan (Autor:in)


    Erscheinungsdatum :

    04.06.2023


    Format / Umfang :

    1372931 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multi-attention network for pedestrian intention prediction based on spatio-temporal feature fusion

    Zhang, Xiaofei / Wang, Xiaolan / Zhang, Weiwei et al. | SAGE Publications | 2024


    Track Prediction Based on Spatio-Temporal Attention

    Wang, Peng / Zhang, Junyi / Zhang, Lu et al. | Springer Verlag | 2022


    Predicting Pedestrian Crossing Intention With Feature Fusion and Spatio-Temporal Attention

    Yang, Dongfang / Zhang, Haolin / Yurtsever, Ekim et al. | IEEE | 2022


    LEARNING-BASED APPROACH FOR ONLINE LANE CHANGE INTENTION PREDICTION

    Kumar, P. / Perrollaz, M. / Lefevre, S. et al. | British Library Conference Proceedings | 2013


    Learning-based approach for online lane change intention prediction

    Kumar, Puneet / Perrollaz, Mathias / Lefevre, Stephanie et al. | IEEE | 2013