S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Reality (2307.07935v4)

Abstract: Due to the lack of enough real multi-agent data and time-consuming of labeling, existing multi-agent cooperative perception algorithms usually select the simulated sensor data for training and validating. However, the perception performance is degraded when these simulation-trained models are deployed to the real world, due to the significant domain gap between the simulated and real data. In this paper, we propose the first Simulation-to-Reality transfer learning framework for multi-agent cooperative perception using a novel Vision Transformer, named as S2R-ViT, which considers both the Deployment Gap and Feature Gap between simulated and real data. We investigate the effects of these two types of domain gaps and propose a novel uncertainty-aware vision transformer to effectively relief the Deployment Gap and an agent-based feature adaptation module with inter-agent and ego-agent discriminators to reduce the Feature Gap. Our intensive experiments on the public multi-agent cooperative perception datasets OPV2V and V2V4Real demonstrate that the proposed S2R-ViT can effectively bridge the gap from simulation to reality and outperform other methods significantly for point cloud-based 3D object detection.

• The paper introduces S2R-ViT, a novel vision transformer that mitigates both deployment and feature gaps in multi-agent cooperative perception.
• It leverages S2R-UViT’s multi-head self-attention and uncertainty modules to counter GPS errors and communication latency in vehicle networks.
• S2R-AFA applies adversarial domain adaptation to extract domain-invariant features, significantly boosting 3D object detection performance on real-world datasets.

S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Reality

Introduction

The paper examines the significant domain gap between simulated and real-world data in multi-agent cooperative perception frameworks. It introduces a Simulation-to-Reality transfer learning framework leveraging a novel Vision Transformer called S2R-ViT. This research focuses on addressing the Deployment Gap and Feature Gap within the simulation-to-reality context, particularly in Vehicle-to-Vehicle (V2V) cooperative perception systems for point cloud-based 3D object detection.

Domain Gap Analysis

The primary issues identified are the Deployment Gap and Feature Gap.

• Deployment Gap: It includes localization errors due to GPS inaccuracy and communication latency, leading to misalignment in data from multiple autonomous agents.

  Figure 1: Illustration of the domain gap (Deployment Gap, Feature Gap) for multi-agent cooperative perception from simulation to reality.

• Feature Gap: The disparity in feature distributions from integrated sensors in the simulation versus real-world conditions, such as differing LiDAR configurations and environmental complexities.

S2R-ViT Architecture

The proposed S2R-ViT consists of two critical components: the S2R-UViT module and the S2R-AFA module.

Figure 2: Overview of the proposed S2R-ViT leveraging S2R-UViT and S2R-AFA modules.

S2R-UViT: Simulation-to-Reality Uncertainty-aware Vision Transformer

S2R-UViT is designed to address uncertainties arising from the deployment gap by enhancing feature interactions. It consists of:

1. Local-and-Global Multi-head Self Attention (LG-MSA): This module enhances the comprehension of both local details and global context in agent feature spaces. Features from multiple agents are processed in parallel attention layers that capture positional accuracy across various scopes.
2. Uncertainty-Aware Module (UAM): Utilizes an Uncertainty Prediction Network to determine the reliability of spatial features across agents, manipulating high-uncertainty feature spaces to suppress their negative influences on ego-agent data.

   Figure 3: Architecture of the proposed S2R-UViT module.

S2R-AFA: Agent-based Feature Adaptation

S2R-AFA aims to reduce the feature gap by applying adversarial domain adaptation methods. It employs inter-agent and ego-agent domain discriminators to force extraction of domain-invariant features. The feature adaptation loss guides these discriminators adversarially, enhancing the model's ability to handle real-world data inputs effectively.

Experimental Results

Experiments were conducted using the OPV2V and V2V4Real datasets. The S2R-ViT framework demonstrated substantial improvements in addressing both deployment and feature gaps when evaluated on simulated and real-world scenarios.

Deployment-Gap Scenario

For testing on CARLA Towns under both perfect and noisy conditions, S2R-UViT exhibited robustness against GPS errors and communication latency, outperforming state-of-the-art methods in maintaining high object detection precision.

Figure 4: Robustness in Deployment-Gap Scenario showcasing resilience to GPS errors and communication latency.

Sim2Real Scenario

In the Sim2Real setting, leveraging domain adaptation methods, S2R-ViT exhibited enhanced performance on the V2V4Real testing dataset, highlighting its superiority in extracting domain-invariant features crucial for real-world application.

Figure 5: Visualization example of point cloud-based 3D object detection on V2V4Real, demonstrating S2R-ViT's real-world efficacy.

Conclusions

The development of S2R-ViT marks an advance in addressing the inherent challenges of translating multi-agent cooperative perception models from simulated environments to real-world applications. S2R-ViT effectively mitigates both deployment and feature gaps through innovative transformer-based architectures, enhancing the precision and reliability of autonomous perception systems. Future work could explore extending these methodologies to other sensory inputs and more complex operational scenarios.