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

2025-01-27

AI Summary

Dynamic object prediction in complex traffic scenarios suffers from significant inaccuracies due to visual occlusions and perception errors. Method: This paper introduces the novel task of Collaborative Perception and Prediction (Co-P&P), proposing a decoupled two-module framework: (i) Collaborative Scene Completion (CSC), which fuses multi-vehicle perspectives via V2X communication to mitigate occlusions; and (ii) Joint Perception and Prediction (P&P), enabling end-to-end co-optimization of perception and motion forecasting. The formulation is the first principled formalization of Co-P&P within a deployable, scalable V2X architecture. Contribution/Results: Experiments demonstrate that the framework substantially reduces cumulative perception error and significantly improves both robustness and accuracy of trajectory prediction under multi-vehicle collaboration. It establishes a new paradigm for high-reliability, V2X-enabled autonomous driving perception.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

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.

Problem

• Collaborative Perception
• V2X Communication
• Object Detection and Prediction

Innovation

• Cooperative Perception and Prediction
• Collaborative Scene Completion
• Autonomous Vehicles Safety and Efficiency