Situational Collective Perception: Adaptive and Efficient Collective Perception in Future Vehicular Systems

Abstract

With the emergence of Vehicle-to-everything (V2X) communication, vehicles and other road users can perform Collective Perception (CP), whereby they exchange their individually detected environment to increase the collective awareness of the surrounding environment. To detect and classify the surrounding environmental objects, preprocessed sensor data (e.g., point-cloud data generated by a Lidar) in each vehicle is fed and classified by onboard Deep Neural Networks (DNNs). The main weakness of these DNNs is that they are commonly statically trained with context-agnostic data sets, limiting their adaptability to specific environments. This may eventually prevent the detection of objects, causing safety disasters. Inspired by the Federated Learning (FL) approach, in this work we tailor a collective perception architecture, introducing Situational Collective Perception (SCP) based on dynamically trained and situational DNNs, and enabling adaptive and efficient collective perception in future vehicular networks.