time synchronization

This work addresses the degradation in fusion quality arising from spatiotemporal heterogeneity in vehicular collaborative perception, caused by clock asynchrony, communication delays, and motion discrepancies. To mitigate these issues, the authors propose a dynamic compensation method that jointly models network time synchronization and Age of Information (AoI). By establishing a unified time reference and leveraging AoI to estimate communication latency, the approach enables precise spatiotemporal alignment of multi-vehicle perception features. Furthermore, it performs uncertainty-aware dynamic weighted fusion based on alignment quality and AoI. This is the first method to synergistically integrate network synchronization with AoI modeling for compensating time-varying clock drift and communication delays. Experimental results in simulated environments with clock drift and link delays demonstrate significant improvements over existing baselines, effectively enhancing the consistency and accuracy of collaborative perception.

Heterogeneous camera systems—e.g., visible-light/infrared, professional/consumer-grade, or audio-equipped/audio-less setups—lack hardware synchronization in real-world scenarios, leading to significant spatiotemporal misalignment across multi-view videos. Method: This paper proposes a vision-based time-encoding method leveraging a custom-designed LED Clock. By embedding temporal exposure timestamps within frames using red and infrared LEDs, the approach achieves cross-modal, audio-free, and external-timecode-free millisecond-level synchronization. It further integrates RMSE-optimized temporal alignment with joint multi-device calibration. Contribution/Results: The method reduces synchronization residuals to 1.34 ms—substantially outperforming existing optical signal, audio-based, and timecode synchronization schemes. Validated in large-scale surgical recordings involving over 25 heterogeneous cameras, it significantly improves downstream tasks including multi-view 3D reconstruction and pose estimation.

High-precision online estimation algorithms for robotics are highly sensitive to sensor timestamp accuracy; however, existing synchronization solutions struggle to simultaneously achieve real-time operation, low cost, and high temporal precision. To address this, we propose a real-time, trigger-based time synchronization system built on commodity hardware. Our approach employs a hardware-triggered mechanism to jointly schedule heterogeneous sensors operating at different frequencies, and integrates an enhanced clock synchronization protocol with nanosecond-resolution timestamping to ensure precise coordination between sensors and the onboard computer. Crucially, the system eliminates reliance on expensive dedicated timing hardware, thereby substantially mitigating the impact of timing errors on online estimation. Experimental evaluation on a physical robot platform demonstrates sub-microsecond synchronization accuracy, along with significant improvements in both estimation robustness and real-time performance.

In collaborative perception, hardware heterogeneity induces feature-domain shift, while communication latency causes temporal misalignment—jointly degrading feature quality and accumulating cross-node errors. To address these challenges at the feature-level fusion stage, we propose a systematic alignment framework: (1) a consistency-preserving domain alignment module mitigates inter-device feature distribution discrepancies; (2) a progressive temporal alignment module corrects dynamic timing offsets via multi-scale motion modeling and two-stage compensation; and (3) an observability-constrained discriminator and instance-aware hierarchical aggregation strategy enhance semantic consistency. Evaluated on three benchmark datasets, our method achieves state-of-the-art performance and demonstrates significantly improved robustness under high communication latency and pose estimation errors.

This work addresses the challenge of time synchronization among heterogeneous sensors in roadside and vehicle-mounted multi-LiDAR–multi-camera systems by proposing an open-source, modular, and scalable hardware synchronization solution. Using the LiDAR synchronization pulse as a reference, the system employs programmable delay circuits to generate independent trigger signals for each camera, enabling flexible and precise spatiotemporal alignment. The architecture supports arbitrary combinations of sensor counts and has been validated on both a three-camera roadside platform and a seven-camera vehicular setup. Experimental results demonstrate significantly improved spatial consistency between point clouds and images, while the design ensures robustness, reproducibility, and ease of deployment.

This work proposes TimeCast, a novel framework for real-time time-to-failure prediction in multi-sensor data streams characterized by dynamically evolving patterns. TimeCast addresses this challenge by online identification of dynamic evolution phases within the data stream and constructing dedicated predictive models for each phase, thereby adaptively capturing time-varying dependencies among sensors. The framework innovatively integrates dynamic phase detection with phase-aware modeling, leveraging a linearly scalable online learning algorithm to achieve high prediction accuracy while substantially reducing computational overhead. Experimental evaluations on real-world datasets demonstrate that TimeCast significantly outperforms existing methods in terms of prediction accuracy for event occurrence time, capability in identifying dynamic changes, and computational efficiency.

Existing event camera simulators rely on frame-based sequences to infer event timestamps, struggling with fast motion and occlusions, which degrades simulation accuracy. This work proposes a continuous-time event simulator based on dynamic 3D Gaussian splatting that explicitly models per-pixel brightness change rates through a 3D scene representation, enabling precise prediction of threshold-crossing times. The method is the first to generate multiple events within a single rendering step without temporal upsampling. It further incorporates an occlusion-aware adaptive time-stepping scheme and a tile-based arbiter to emulate real sensor bandwidth constraints. Evaluated on RGB–event paired benchmarks, the approach achieves state-of-the-art fidelity in simulated event streams and demonstrates superior transfer performance in downstream tasks.

This work addresses the tension between information timeliness and limited communication resources in multi-region cooperative perception, which critically constrains sensing performance. To this end, the authors propose a dynamic scheduling method that integrates Age of Information (AoI) and communication overhead into a timeliness-aware penalty function. Leveraging Lyapunov optimization, they design the TAMP algorithm to transform the long-term perception performance loss minimization problem into a per-time-slot priority ranking task. The approach jointly optimizes cooperative perception feature scheduling and wireless resource allocation. Evaluated on the RCooper dataset under intersection and corridor scenarios, the method achieves up to a 27% improvement in average precision (AP) over the best-performing baseline.

This work proposes a fully fixed-point, non-iterative, streaming optical flow algorithm tailored for efficient deployment on resource-constrained FPGAs. By partitioning asynchronous event streams into fixed-time windows and representing them as 1-bit spatial occupancy grids, the method evaluates multiple velocity hypotheses in parallel using only integer logic—comprising shift registers, counters, comparators, and LUT-based multipliers—without requiring frame reconstruction, floating-point arithmetic, or division operations. A single-axis prototype was successfully implemented on a Xilinx Artix-7 FPGA, occupying less than 2 kB of memory and achieving 99.5% directional accuracy under event densities of 10–40%. To the best of our knowledge, this is the first demonstration of low-latency, sparse velocity estimation on an FPGA without relying on DSP blocks or dedicated dividers.

Long-term, non-continuous geometric and appearance changes in multi-epoch scenarios (e.g., urban mapping, construction site monitoring) violate the static or dynamic scene assumptions underlying existing 3D reconstruction methods, leading to failure. To address this, we propose Temporal-Modulated Gaussians (TM-Gaussians), a unified anchor-based representation enabling consistent cross-epoch modeling. TM-Gaussians are the first method to explicitly decouple stable and evolving scene components, overcoming the modeling bottleneck under non-continuous temporal changes. Our approach integrates temporal modulation, differentiable rendering, and joint spatio-temporal optimization, trained and validated on our newly introduced ChronoScene dataset—a large-scale real-world and synthetic benchmark for time-varying scenes. Extensive experiments demonstrate significant improvements in both reconstruction accuracy and temporal consistency over diverse baselines. Code and the ChronoScene dataset are publicly released.