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

TL;DR

This work addresses occlusion and cumulative perception errors in connected autonomous vehicle networks by proposing Collaborative Joint Perception and Prediction (Co-P&P), a decoupled framework with Collaborative Scene Completion (CSC) and Joint Perception and Prediction (PandP). It integrates LiDAR-centric perception with multi-agent interactions, map cues, and a collaboration trigger to enable selective data sharing via V2X, aiming to improve motion prediction and situational awareness. The authors outline evaluation strategies, including ARCV and task-agnostic metrics, and discuss simulation and real-world considerations, emphasizing domain gaps, localization, and data requirements. The framework provides a scalable blueprint for deploying collaborative perception in dynamic traffic while identifying key challenges and the need for large-scale datasets to advance practical adoption.

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.

Figures (4)

• Figure 1: Schematic diagram of Collaborative Perception. The diagram illustrates a scenario at an intersection where two collaborate to enhance perception. The ego vehicle (blue) has a limited field of view due to occlusions, such as trees and buildings, which block its line of sight to a vehicle turning left. A collaborating vehicle (orange) positioned across the intersection shares its sensor data, expanding the ego vehicle's awareness. The shaded areas represent the for each vehicle
• Figure 2: Schematic diagram of Collaborative Joint Perception and Prediction.The system combines GPS/GNSS-INS and map-based localization for precise positioning. Sensing data (point clouds, poses) from collaborative vehicle and roadside unit are processed and shared with the ego vehicle, enabling to provide a comprehensive LiDAR frame. To optimize bandwidth usage, intermediate features are shared for scene completion instead of raw point clouds are shared via . The collaboration trigger manages activation. The module integrates localization, map, and LiDAR data to jointly enhance perception and prediction, which feeds into the risk assessment and planning and control modules for real-time decision-making.
• Figure 3: Schematic diagram of .
• Figure 4: Schematic diagram of CSC.