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Latency-Aware Collaborative Perception

Zixing Lei, Shunli Ren, Yue Hu, Wenjun Zhang, Siheng Chen

arXiv:2207.08560v4cs.CVcs.RO

TL;DR

Realistic communication latency makes asynchronous collaborative perception unreliable, motivating a latency-aware approach. The paper introduces SyncNet to synchronize multi-agent features at a common time stamp, and experiments show improved collaborative perception under latency while retaining superiority over single-agent perception under severe latency.

  • Problem

    Existing collaborative perception methods generally assume ideal communication, although inevitable latency can degrade perception and create risks in applications such as autonomous driving.

  • Method

    The paper proposes a latency-aware collaborative perception system with SyncNet, which uses historical collaboration information, feature-attention symbiotic estimation, and time modulation to synchronize features before fusion.

  • Results

    The proposed system improves collaborative perception in communication-latency scenarios and significantly and consistently outperforms single-agent perception under varying latency.

  • Takeaways & Limitations

    Latency-aware feature synchronization can mitigate the effect of inevitable communication latency while preserving the benefits of collaborative perception.

  • Takeaways & Limitations

    The formulation assumes collaboration occurs at discrete time stamps and treats latency as discrete, with experiments reporting minor mismatch after reasonably fine discretization of continuous time.

Abstract

from arXiv · show

Collaborative perception has recently shown great potential to improve perception capabilities over single-agent perception. Existing collaborative perception methods usually consider an ideal communication environment. However, in practice, the communication system inevitably suffers from latency issues, causing potential performance degradation and high risks in safety-critical applications, such as autonomous driving. To mitigate the effect caused by the inevitable latency, from a machine learning perspective, we present the first latency-aware collaborative perception system, which actively adapts asynchronous perceptual features from multiple agents to the same time stamp, promoting the robustness and effectiveness of collaboration. To achieve such a feature-level synchronization, we propose a novel latency compensation module, called SyncNet, which leverages feature-attention symbiotic estimation and time modulation techniques. Experiments results show that the proposed latency aware collaborative perception system with SyncNet can outperforms the state-of-the-art collaborative perception method by 15.6% in the communication latency scenario and keep collaborative perception being superior to single agent perception under severe latency.

1 Introduction

Collaborative perception can exceed single-agent capabilities, but realistic communication latency creates asynchronous information that can severely damage performance. The paper introduces SyncNet to synchronize perceptual features before fusion and reports robust improvements under latency.

  • Motivation: Communication latency can make collaborative perception perform worse than single-agent perception in safety-critical settings.Varying channel latency creates severe time asynchrony and can cause missed or incorrect detections.
  • Latency-aware system: The proposed system adapts asynchronous perceptual features from multiple agents to a common time stamp before aggregation.It synchronizes collaboration features before fusion rather than directly aggregating asynchronous inputs.
  • SyncNet: SyncNet estimates intermediate features and collaboration attention jointly using historical collaboration information.The feature-attention symbiotic estimation allows the two coupled information types to mutually enhance each other.
  • SyncNet: Unlike common time-series prediction, SyncNet performs feature-level estimation of coupled features and associated collaboration attention.The design differs from output-level prediction and single-output estimation.
  • Results: The system significantly and consistently outperforms single-agent perception under varying communication latency.Experiments evaluate collaborative 3D object detection on the V2X-Sim dataset.
  • Contributions: The paper formulates communication latency as a collaborative-perception challenge and proposes SyncNet as a latency compensation module.The contribution targets robust multi-agent perception by mitigating inevitable communication latency.

2 Related Work

The related work spans V2V communication protocols, collaborative perception architectures, and time-series prediction. This paper positions latency compensation as a machine-learning problem rather than relying on communication improvements alone.

  • V2V communication: V2V communication uses IEEE 802.11p and cellular-network standards, including LTE-V2X.Despite progress in V2V networking, communication latency remains a challenge for collaborative perception.
  • Collaborative perception: Prior collaborative perception methods use mechanisms including handshakes, multi-round graph message passing, and knowledge distillation.These approaches share perceived information while balancing communication cost and perception performance.
  • Time-series prediction: Time-series prediction methods infer future signals from historical data, while this work uses prediction techniques to recover latency-missing collaboration information.The paper distinguishes its goal of maximizing final perception performance from standard time-series prediction.

3 Methodology

The latency-aware collaborative perception system synchronizes asynchronous features at a common timestamp using SyncNet, which estimates features and collaboration attention before time-modulated fusion. Its design includes feature-attention symbiotic estimation, historical-feature compensation, and auxiliary supervision for intermediate estimates.

  • Latency-aware collaborative perception system: The system models discrete, time-varying communication latency and aims to improve each agent’s perception under nonideal communication.Latency is represented between communicating agents, with experiments indicating minor mismatch when continuous time is discretized using reasonably small intervals.
  • Latency-aware collaborative perception system: SyncNet estimates the current collaboration feature and attention from historical features and the ego agent’s real-time feature, enabling feature-level synchronization.The latency compensation module uses stored historical frames and received asynchronous features to estimate missing information at the target timestamp.
  • SyncNet: latency compensation module: SyncNet combines feature-attention symbiotic estimation with time modulation to produce a latency-compensated collaboration feature.FASE uses a dual-branch pyramid LSTM, while time modulation adaptively combines estimated and asynchronous feature or attention values according to confidence.
  • SyncNet: latency compensation module: The dual-branch estimator jointly infers collaboration features and attention while allowing each branch to use the other’s information to focus on informative regions.The design keeps the branches independent, avoids cascaded failure, and recurrently uses prior estimated feature maps and attention.
  • Loss function: The training objective supervises perception output, fused features, intermediate estimated features, and collaboration attention.The loss terms provide supervision at multiple levels, including the final output and intermediate feature and attention estimates.

4 Experiments

Experiments evaluate latency-aware collaborative perception on V2X-Sim using asynchronous multi-agent LiDAR data, AP@0.5/0.7, baseline comparisons, ablations, and qualitative analyses. SyncNet improves robustness under latency, with feature-attention estimation and time modulation contributing complementary benefits.

  • Experimental setup: The evaluation uses V2X-Sim for collaborative 3D object detection, with 80 training scenes, 11 test scenes, and 2.67 agents per sample on average.Latency is simulated using asynchronous timestamps and exponentially distributed latency times.
  • Experimental setup: Performance is measured with Average Precision at IoU thresholds of 0.5 and 0.7 under fixed or randomly generated communication latency.The encoded transmitted feature has dimension 32 × 32 × 256, and latency may be sampled from an exponential distribution.
  • Quantitative evaluation: SyncNet consistently outperforms DiscoNet under varying latency, improving AP@0.5/0.7 by up to 15.6%/12.6%.It also remains superior to No Collaboration with communication latency of up to 10 frames, while DiscoNet can fall below No Collaboration at high latency.
  • Quantitative evaluation: SyncNet improves other collaborative frameworks by up to 11.8%/8.7% in AP@0.5, including V2VNet and a transformer-based fusion module.The comparison evaluates each framework with and without SyncNet.
  • Ablation study: With three historical frames, SyncNet balances computation efficiency and performance; four frames provide only marginal additional benefit.The study reports that k = 3 significantly outperforms k = 2, whereas k = 4 adds marginal benefit.
  • Qualitative evaluation: Qualitative results show SyncNet recovering true vehicle positions more accurately than latency-affected DiscoNet and vanilla estimation.Its attention estimation also reduces weights at noisy positions, helping capture informative regions and avoid cascading errors.

5 Conclusions

The paper introduces latency-aware collaborative perception and SyncNet for feature-level time-domain synchronization in intermediate collaboration methods. Quantitative and qualitative experiments show improved perception performance under communication latency and effective mitigation of the latency issue.

  • Conclusion: SyncNet is a latency compensation module that jointly estimates intermediate features and attention weights while applying time modulation.The module is designed for time-domain synchronization and fits existing intermediate collaboration methods.
  • Conclusion: Experiments show that SyncNet improves perception performance and effectively addresses communication latency in collaborative perception.The conclusion is supported by comprehensive quantitative and qualitative experiments.
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