Robust Perception is vital in automotive Cyber-Physical Systems (CPS). Although the supporting technologies have advanced recently, enabling robust perception remains challenging for researchers and industry alike. The highly variable scenarios in complex urban environments can lead to erroneous perceptions, which are factors in most driver-related crashes. In this chapter, we present our experience developing AV perception models capable of better understanding driving scenes, thus improving their robustness. Specifically, we propose using scene-graphs as a better Intermediate Representation (IR) for road scenes. Besides, we develop a novel spatio-temporal graph learning approach based on scene-graph representations for modeling the risk of driving maneuvers. Our approach better understands driving scenes and converts them into an estimated risk level by leveraging a network architecture consisting of a Multi-Relation Graph Convolution Network (MR-GCN), a Long-Short Term Memory Network (LSTM), and self-attention layers. We demonstrate how a scene-graph approach for AV perception enables the AV to better assess risk across various driving maneuvers than state of the art, thus being more robust. Moreover, our approach can more effectively transfer knowledge learned from simulated data to real-world driving scenarios. Lastly, we show how adding spatial and temporal attention layers to our approach improves its explainability.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Scene-Graph Embedding for Robust Autonomous Vehicle Perception




    Publication date :

    2023-03-27


    Size :

    20 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    METHODS FOR SPATIO-TEMPORAL SCENE-GRAPH EMBEDDING FOR AUTONOMOUS VEHICLE APPLICATIONS

    AL FARUQUE MOHAMMAD ABDULLAH / YU SHIH-YUAN / MALAWADE ARNAV VAIBHAV et al. | European Patent Office | 2023

    Free access


    Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions

    Yu, Shih-Yuan / Malawade, Arnav Vaibhav / Muthirayan, Deepan et al. | IEEE | 2022


    METHOD FOR GENERATING A WIDE-AREA PERCEPTION SCENE GRAPH

    HOVIS JEFFREY GENE / SZCZERBA MICHAEL BERNHARD | European Patent Office | 2018

    Free access

    Processing of LiDAR for Traffic Scene Perception of Autonomous Vehicles

    Urmila., O / Megalingam, Rajesh Kannan | IEEE | 2020