Evidence map›Paper›PMID 41977894›Full record

ArticleSensors (Basel, Switzerland)2026

A DTW-Based Spatio-Temporal Synchronization Method for Radar and Camera Fusion.

Jingjing Li, Juan Liu, Xiuping Li, Chengliang Zhong, Xiyan Sun

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jingjing LiGuangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China.ORCID 0000-0002-1908-4481
Juan LiuGuangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China.ORCID 0009-0000-7605-9103
Xiuping LiGuangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China.ORCID 0009-0001-4771-9587
Chengliang ZhongGuangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China.ORCID 0009-0006-4475-5060
Xiyan SunGuangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China.ORCID 0000-0002-4227-2499

Funding

Basic Research Capabilities Enhancement Project for Young and Middle-aged Teachers in Guangxi's Universities 2025KY0243Guangxi Science and Technology Base and Talent Special Project: Research and Application of Key Technologies for Precise Navigation Gui Ke AD25069103National Natural Science Foundation of China 62561018Natural Science Foundation of Guangxi 2025GXNSFBA069410the Guangxi Science and Technology Project AB23026120
6 · The paper itself

Abstract

Roadside perception systems, also known as roadside units (RSUs), are critical in Vehicle-to-Everything (V2X) applications, yet spatio-temporal asynchrony between multiple sensors severely compromises the accuracy of fusion. In this paper, a spatio-temporal synchronization method for millimeter-wave (MMW) radar and camera fusion is proposed, integrating target matching based on dynamic time warping (DTW) with spatio-temporal parameter estimation. Leveraging the advantages of DTW in time-series alignment to calculate the similarity between radar and visual trajectories enables target matching and parameter estimation in sparse scenes. This method was validated on a real-world dataset containing over 30 pedestrian trajectories, covering scenarios with varying densities ranging from one to six pedestrians. The results indicate a temporal offset of 0.116 s between the camera and radar. Following synchronization, the average spatial deviation decreased from 1.4358 to 0.1074 m in the x-direction (i.e., across the road) and from 3.0732 to 0.1775 m in the y-direction (i.e., along the road). Consequently, this method provides an efficient solution for deploying roadside perception systems in sparse traffic environments.

Indexed as

DTWradar and camera fusionspatio-temporal synchronization

Identifiers

PMID41977894
PMCPMC13074403

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.