The Components of Collaborative Joint Perception and Prediction -- A Conceptual Framework

TL;DR

This paper introduces a new framework for collaborative perception and prediction in connected autonomous vehicles, aiming to improve motion prediction accuracy and vehicle awareness through V2X communication.

Contribution

It proposes a novel conceptual framework with decoupled modules for collaborative perception and prediction, addressing deployment scalability and outlining future research challenges.

Findings

Framework enhances perception accuracy in complex scenarios

Decoupled modules simplify deployment and scalability

Outlines future research directions in Co-P&P

Abstract

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.