An Extensible Framework for Integrating Continually Emerging Heterogeneous Agents in Collaborative Perception

核心概念

HEAL, a novel extensible framework, enables seamless integration of continually emerging heterogeneous agent types into collaborative perception, ensuring high performance and low training costs.

摘要

The paper introduces HEAL, a novel extensible framework for open heterogeneous collaborative perception. The key highlights are:

Collaborative Perception Base Training:

HEAL establishes a unified feature space for initial homogeneous agents using a novel Pyramid Fusion network.
Pyramid Fusion leverages multi-scale and foreground-aware designs to create a robust unified feature space.

New Agent Type Training:

When new heterogeneous agent types emerge, HEAL aligns them to the pre-established unified feature space through a novel backward alignment mechanism.
The backward alignment only requires training the new agent's front-end encoder, presenting extremely low training costs and high extensibility.
This preserves the privacy of new agents' models and data, addressing key concerns for real-world deployment.

Experiments and Evaluation:

HEAL is evaluated on the proposed OPV2V-H dataset and the real-world DAIR-V2X dataset.
HEAL outperforms state-of-the-art methods in perception performance while reducing training parameters by 91.5% when integrating 3 new agent types.
HEAL also demonstrates robustness to pose errors and efficient feature compression.

The paper presents a practical solution for extensible collaborative perception deployment in real-world scenarios with continually emerging heterogeneous agents.

統計資料

The paper does not provide specific numerical data points, but highlights the following key statistics:

HEAL reduces training parameters by 91.5% compared to state-of-the-art methods when integrating 3 new agent types.
HEAL maintains state-of-the-art perception performance even under various pose error conditions.
HEAL retains exceptionally high performance with a 32-fold feature compression ratio, surpassing baseline methods.

引述

"HEAL holds the best performance and the lowest training cost under various agent type combinations."
"Experiments show that no matter what kind of agent combination, HEAL always maintains the best performance and the lowest training cost."

從以下內容提煉的關鍵洞見

An Extensible Framework for Open Heterogeneous Collaborative Perception

by Yifan Lu,Yue... 於 arxiv.org 04-02-2024

https://arxiv.org/pdf/2401.13964.pdf

深入探究

How can HEAL's backward alignment mechanism be extended to handle more complex feature spaces beyond the BEV representation

HEAL's backward alignment mechanism can be extended to handle more complex feature spaces beyond the BEV representation by incorporating additional transformation layers and adaptors. These layers can help map features from different modalities or models into a common feature space, enabling alignment and fusion. By introducing learnable transformations and attention mechanisms, HEAL can adapt to diverse feature representations and ensure effective collaboration among heterogeneous agents with varying sensor setups.

What are the potential challenges in deploying HEAL in real-world scenarios with frequent sensor and model updates across a large fleet of heterogeneous agents

Deploying HEAL in real-world scenarios with frequent sensor and model updates across a large fleet of heterogeneous agents may pose several challenges. One challenge is maintaining compatibility and synchronization among the different agents' sensor data and perception models. Ensuring seamless integration of new agent types while preserving high perception performance and low training costs can be complex. Additionally, managing the scalability and computational resources required for continual training and alignment of new agents in dynamic environments can be a significant challenge. Addressing these challenges will require robust infrastructure, efficient data management, and adaptive training strategies to accommodate evolving agent types effectively.

How can the privacy-preserving aspects of HEAL's training process be further strengthened to address the concerns of automotive companies and other stakeholders

To further strengthen the privacy-preserving aspects of HEAL's training process, several measures can be implemented. One approach is to incorporate differential privacy techniques to protect sensitive information during the training process. By adding noise to the gradients or intermediate representations, HEAL can prevent the leakage of individual agent data while still achieving collaborative perception goals. Additionally, implementing secure multi-party computation protocols can enable agents to collaborate without sharing raw data, enhancing privacy and data security. Furthermore, adopting federated learning approaches can allow agents to train collaboratively while keeping their data decentralized, addressing data privacy concerns for automotive companies and other stakeholders. By combining these privacy-enhancing techniques, HEAL can ensure data and model privacy while enabling effective collaborative perception among heterogeneous agents.