The Components of Collaborative Joint Perception and Prediction -- A Conceptual Framework
This work addresses challenges in vehicle awareness for connected autonomous vehicles, but it is incremental as it builds on existing collaborative perception concepts with a new framework.
The paper tackles the problem of cumulative errors and visual occlusion in collaborative perception for connected autonomous vehicles by introducing a new task called Collaborative Joint Perception and Prediction (Co-P&P) and providing a conceptual framework for its implementation, which aims to improve motion prediction of surrounding objects to enhance vehicle awareness in complex traffic scenarios.
Connected Autonomous Vehicles (CAVs) benefit from Vehicle-to-Everything (V2X) communication, which enables the exchange of sensor data to achieve Collaborative Perception (CP). To reduce cumulative errors in perception modules and mitigate the visual occlusion, this paper introduces a new task, Collaborative Joint Perception and Prediction (Co-P&P), and provides a conceptual framework for its implementation to improve motion prediction of surrounding objects, thereby enhancing vehicle awareness in complex traffic scenarios. The framework consists of two decoupled core modules, Collaborative Scene Completion (CSC) and Joint Perception and Prediction (P&P) module, which simplify practical deployment and enhance scalability. Additionally, we outline the challenges in Co-P&P and discuss future directions for this research area.