{"id":35341,"date":"2026-06-16T16:41:35","date_gmt":"2026-06-16T08:41:35","guid":{"rendered":"https:\/\/aiportek.com\/?p=35341"},"modified":"2026-06-16T16:41:45","modified_gmt":"2026-06-16T08:41:45","slug":"simdata-aisim-virtual-dataset-autonomous-driving-perception","status":"publish","type":"post","link":"https:\/\/aiportek.com\/en\/simdata-aisim-virtual-dataset-autonomous-driving-perception\/","title":{"rendered":"[Hongke Solutions] SimData High-Fidelity Virtual Dataset Solution: Perception Training for Autonomous Driving Based on aiSim"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"35341\" class=\"elementor elementor-35341\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-303a47ec elementor-section-stretched elementor-section-full_width elementor-section-height-min-height elementor-section-content-middle elementor-section-height-default elementor-section-items-middle\" data-id=\"303a47ec\" data-element_type=\"section\" data-settings=\"{&quot;stretch_section&quot;:&quot;section-stretched&quot;,&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-27d5e225\" data-id=\"27d5e225\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-4e369ae3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4e369ae3\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-6555484e\" data-id=\"6555484e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-397ef20e elementor-widget elementor-widget-heading\" data-id=\"397ef20e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Hongke's latest articles<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<div class=\"elementor-element elementor-element-4b5c0d9b elementor-absolute elementor-widget elementor-widget-heading\" data-id=\"4b5c0d9b\" data-element_type=\"widget\" data-settings=\"{&quot;_position&quot;:&quot;absolute&quot;}\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">HongKe<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6d18033c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6d18033c\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1f96cecf\" data-id=\"1f96cecf\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fd50b9b elementor-widget elementor-widget-text-editor\" data-id=\"fd50b9b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d303089 elementor-widget elementor-widget-text-editor\" data-id=\"d303089\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-4b0e7b4e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4b0e7b4e\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-7a4b7cfc\" data-id=\"7a4b7cfc\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-79a3214 elementor-widget elementor-widget-heading\" data-id=\"79a3214\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">[Hongke Solutions] SimData: A High-Fidelity Virtual Dataset Generation Solution Based on aiSim<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-226f412 elementor-widget elementor-widget-post-info\" data-id=\"226f412\" data-element_type=\"widget\" data-widget_type=\"post-info.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul class=\"elementor-inline-items elementor-icon-list-items elementor-post-info\">\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-repeater-item-2358f4d elementor-inline-item\" itemprop=\"author\">\n\t\t\t\t\t\t<a href=\"https:\/\/aiportek.com\/en\/author\/hongketechnology\/\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-user-circle\" viewbox=\"0 0 496 512\" 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elementor-inline-item\" itemprop=\"datePublished\">\n\t\t\t\t\t\t<a href=\"https:\/\/aiportek.com\/en\/2026\/06\/16\/\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-calendar\" viewbox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M12 192h424c6.6 0 12 5.4 12 12v260c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V204c0-6.6 5.4-12 12-12zm436-44v-36c0-26.5-21.5-48-48-48h-48V12c0-6.6-5.4-12-12-12h-40c-6.6 0-12 5.4-12 12v52H160V12c0-6.6-5.4-12-12-12h-40c-6.6 0-12 5.4-12 12v52H48C21.5 64 0 85.5 0 112v36c0 6.6 5.4 12 12 12h424c6.6 0 12-5.4 12-12z\"><\/path><\/svg>\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text elementor-post-info__item elementor-post-info__item--type-date\">\n\t\t\t\t\t\t\t\t\t\t<time>June 16, 2026<\/time>\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t<\/li>\n\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c027dd7 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"c027dd7\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9aba933 elementor-widget elementor-widget-heading\" data-id=\"9aba933\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-small\">01 Preamble<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d3381e7 elementor-widget elementor-widget-text-editor\" data-id=\"d3381e7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p data-path-to-node=\"5\">In the research and development of autonomous driving perception systems, the performance of AI models is highly dependent on large-scale, high-quality perception datasets. Currently, widely adopted open-source datasets in the industry include KITTI, nuScenes, and the Waymo Open Dataset, among others; these datasets have laid a critical foundation for the evolution of autonomous driving algorithms.<\/p><p data-path-to-node=\"6\">However, building real-world on-site perception datasets is no easy task\u2014companies not only need to invest significant human, material, and time resources, but must also contend with numerous severe challenges, including data collection constraints, privacy compliance, time-consuming data annotation, and the difficulty of capturing extreme\/ rare scenarios.<\/p><p data-path-to-node=\"7\">Against this backdrop, high-fidelity virtual datasets are rapidly emerging as a new frontier in the research of perception algorithms for autonomous driving. Through virtual data generated by simulation platforms, R&amp;D teams can not only rapidly scale up the volume of data but also flexibly simulate complex road conditions, adverse weather, and rare unexpected events, thereby providing perception models with more comprehensive and diverse training samples.<\/p><p data-path-to-node=\"8\">Based on this,<b data-path-to-node=\"8\" data-index-in-node=\"4\">HOSCO<\/b>We have launched a brand-new high-fidelity virtual dataset\u2014<b data-path-to-node=\"8\" data-index-in-node=\"22\">SimData<\/b>. SimData Deep Trust <b data-path-to-node=\"8\" data-index-in-node=\"43\">aiSim simulation platform<\/b>With its high-precision physical modeling and realistic visual rendering capabilities, it can efficiently generate synchronized data from multiple sensors (including cameras, LiDAR, millimeter-wave radar, IMUs, and more), perfectly achieving multimodal characteristics that closely align with real-world data.<\/p><p data-path-to-node=\"9\">SimData's data structure strictly follows <b data-path-to-node=\"9\" data-index-in-node=\"18\">nuScenes Data Set Format Specification<\/b>, developers can directly use the official <code data-path-to-node=\"9\" data-index-in-node=\"49\">nuscenes-devkit<\/code> The tool performs data analysis and visualization, significantly reducing the time and effort required for R&amp;D personnel to adapt to and master it.<\/p><p data-path-to-node=\"10\">This article will provide an in-depth analysis of SimData\u2019s core features and development process, and comprehensively demonstrate its actual performance in typical autonomous driving perception tasks.<b data-path-to-node=\"10\" data-index-in-node=\"51\">SimData Official Release<\/b>The report and related comparison test results will be released shortly, so please stay tuned.<b data-path-to-node=\"10\" data-index-in-node=\"86\">HOSCO<\/b>The latest updates and technical insights.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a6e10d8 elementor-widget elementor-widget-heading\" data-id=\"a6e10d8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-small\">02 Analysis of the SimData Development Process and Architecture<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d3aca9a elementor-widget elementor-widget-heading\" data-id=\"d3aca9a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-small\">Sensor Layout<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c12e546 elementor-widget elementor-widget-text-editor\" data-id=\"c12e546\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p data-path-to-node=\"14\">In the aiSim simulation platform, we have rigorously replicated the sensor configuration and layout of the nuScenes dataset to ensure a high degree of consistency in data structure and multimodal synchronization.<\/p><p data-path-to-node=\"15\">The simulation vehicle is equipped with 6 surround cameras, 5 millimeter-wave radars, 1 LiDAR, 1 inertial measurement unit (IMU), and 1 global positioning system (GPS). The sampling frequency for both the cameras and radars is set to 40 Hz, while the LiDAR operates at a high sampling frequency of 80 Hz, perfectly meeting the requirements for high-temporal-precision, multi-sensor synchronized data acquisition.<\/p><p data-path-to-node=\"16\">The spatial layout, positions, and orientations of the various sensors are shown in the figure below:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c2bbd8e elementor-widget elementor-widget-image\" data-id=\"c2bbd8e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"682\" height=\"189\" src=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/\u5c4f\u5e55\u622a\u56fe-2026-06-16-143340.png\" class=\"attachment-large size-large wp-image-35346\" alt=\"\" srcset=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/\u5c4f\u5e55\u622a\u56fe-2026-06-16-143340.png 682w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/\u5c4f\u5e55\u622a\u56fe-2026-06-16-143340-300x83.png 300w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/\u5c4f\u5e55\u622a\u56fe-2026-06-16-143340-18x5.png 18w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/\u5c4f\u5e55\u622a\u56fe-2026-06-16-143340-600x166.png 600w\" sizes=\"(max-width: 682px) 100vw, 682px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Overall view (left), front view (right)<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4c4221d elementor-widget elementor-widget-image\" data-id=\"4c4221d\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"685\" height=\"188\" src=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/\u5c4f\u5e55\u622a\u56fe-2026-06-16-143403.png\" class=\"attachment-large size-large wp-image-35347\" alt=\"\" srcset=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/\u5c4f\u5e55\u622a\u56fe-2026-06-16-143403.png 685w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/\u5c4f\u5e55\u622a\u56fe-2026-06-16-143403-300x82.png 300w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/\u5c4f\u5e55\u622a\u56fe-2026-06-16-143403-18x5.png 18w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/\u5c4f\u5e55\u622a\u56fe-2026-06-16-143403-600x165.png 600w\" sizes=\"(max-width: 685px) 100vw, 685px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Left view (left), top view (right)<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-54d03fe elementor-widget elementor-widget-text-editor\" data-id=\"54d03fe\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p data-path-to-node=\"18\">Unlike the nuScenes dataset, all sensors in SimData use a unified <b data-path-to-node=\"18\" data-index-in-node=\"42\">FLU (Forward-Left-Up) Coordinate System<\/b>; whereas in the original nuScenes dataset, the camera sensors use <b data-path-to-node=\"18\" data-index-in-node=\"94\">RDF (Right-Down-Forward) Coordinate System<\/b>The<\/p><p data-path-to-node=\"19\">During the dataset construction process, we have performed extremely rigorous coordinate system transformations and alignment optimizations on all annotation files to ensure that the coordinate definitions are logically consistent with nuScenes. Consequently, when deploying and using SimData, users do not need to expend additional effort addressing coordinate discrepancies; their data parsing and secondary development experience remain seamlessly consistent with native nuScenes. The figure below illustrates the typical layout of various sensors in nuScenes and their coordinate system definitions.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-437d902 elementor-widget elementor-widget-image\" data-id=\"437d902\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1024\" height=\"533\" src=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-23-1024x533.webp\" class=\"attachment-large size-large wp-image-35345\" alt=\"\" srcset=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-23-1024x533.webp 1024w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-23-300x156.webp 300w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-23-768x400.webp 768w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-23-18x9.webp 18w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-23-600x312.webp 600w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-23.webp 1080w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3b6d788 elementor-widget elementor-widget-heading\" data-id=\"3b6d788\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-small\">Data Structures<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-99ea62d elementor-widget elementor-widget-text-editor\" data-id=\"99ea62d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p data-path-to-node=\"21\">The SimData dataset is fully compatible with nuScenes in terms of structural design and directory organization. For algorithm engineers and developers already familiar with the nuScenes framework, deploying, using, and parsing SimData requires no additional adaptation, conversion, or learning effort.<\/p><p data-path-to-node=\"22\">The figure below illustrates the overall directory structure of the SimData dataset. nuScenes follows the same organizational structure, with the goal of achieving seamless compatibility and tool-level interoperability.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bfd2185 elementor-widget elementor-widget-image\" data-id=\"bfd2185\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"375\" height=\"517\" src=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-24.webp\" class=\"attachment-large size-large wp-image-35344\" alt=\"\" srcset=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-24.webp 375w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-24-218x300.webp 218w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-24-9x12.webp 9w\" sizes=\"(max-width: 375px) 100vw, 375px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-af80536 elementor-widget elementor-widget-text-editor\" data-id=\"af80536\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p data-path-to-node=\"23\">The specific core directories and file structure are described below:<\/p><ul data-path-to-node=\"24\"><li><p data-path-to-node=\"24,0,0\"><b data-path-to-node=\"24,0,0\" data-index-in-node=\"0\">maps folder<\/b> This repository contains all high-definition map (HD Map) image files, which are primarily used to provide precise geospatial information and serve as background references for scenes.<\/p><\/li><li><p data-path-to-node=\"24,1,0\"><b data-path-to-node=\"24,1,0\" data-index-in-node=\"0\">samples folder<\/b> Stores keyframe data for various types of sensors, specifically including:<\/p><ul data-path-to-node=\"24,1,1\"><li><p data-path-to-node=\"24,1,1,0,0\">6-channel camera feed (<code data-path-to-node=\"24,1,1,0,0\" data-index-in-node=\"12\">.jpg<\/code> (Format)<\/p><\/li><li><p data-path-to-node=\"24,1,1,1,0\">5-channel millimeter-wave radar point cloud (<code data-path-to-node=\"24,1,1,1,0\" data-index-in-node=\"11\">.pcd<\/code> (Format)<\/p><\/li><li><p data-path-to-node=\"24,1,1,2,0\">1-channel LiDAR point cloud (<code data-path-to-node=\"24,1,1,2,0\" data-index-in-node=\"17\">.bin<\/code> (Format) In this process, the system selects one frame of data every 0.5 seconds as a keyframe and saves it with high precision.<\/p><\/li><\/ul><\/li><li><p data-path-to-node=\"24,2,0\"><b data-path-to-node=\"24,2,0\" data-index-in-node=\"0\">sweeps folder<\/b> Store continuous sensor data (excluding keyframes), which is primarily used to construct temporal information and perform advanced perception tasks such as multi-frame fusion.<\/p><\/li><li><p data-path-to-node=\"24,3,0\"><b data-path-to-node=\"24,3,0\" data-index-in-node=\"0\">v1.0-* Folder<\/b> Stores annotations and metadata for sensors. All files are in <code data-path-to-node=\"24,3,0\" data-index-in-node=\"56\">.json<\/code> The file format includes core elements such as timestamps, ego-pose parameters, labels, and scene descriptions.<\/p><\/li><\/ul><p data-path-to-node=\"25\">Each <code data-path-to-node=\"25\" data-index-in-node=\"3\">.json<\/code> The annotated network of relationships between files is also fully consistent with the nuScenes dataset. This is illustrated in detail below using the official nuScenes file structure diagram:<\/p><p data-path-to-node=\"26\">In the SimData dataset, the information blocks in each data file are identified by a globally unique <b data-path-to-node=\"26\" data-index-in-node=\"37\">UUID (Universally Unique Identifier)<\/b> used as tokens for unique identification. These tokens serve as the link between different data dimensions within the dataset. Users simply need to <code data-path-to-node=\"26\" data-index-in-node=\"126\">sample.json<\/code>,<code data-path-to-node=\"26\" data-index-in-node=\"138\">sample_data.json<\/code> cap (a poem) <code data-path-to-node=\"26\" data-index-in-node=\"157\">sample_annotation.json<\/code> With these three core documents, you can efficiently obtain the vast majority of annotated data and structured information.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-982c3ef elementor-widget elementor-widget-image\" data-id=\"982c3ef\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"980\" height=\"1024\" src=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-25-1-980x1024.webp\" class=\"attachment-large size-large wp-image-35343\" alt=\"\" srcset=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-25-1-980x1024.webp 980w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-25-1-287x300.webp 287w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-25-1-768x803.webp 768w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-25-1-11x12.webp 11w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-25-1-600x627.webp 600w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-25-1.webp 1080w\" sizes=\"(max-width: 980px) 100vw, 980px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e8e4b0a elementor-widget elementor-widget-text-editor\" data-id=\"e8e4b0a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h4 data-path-to-node=\"27\">sample.json<\/h4><p data-path-to-node=\"28\"><code data-path-to-node=\"28\" data-index-in-node=\"0\">sample.json<\/code> The document provides a detailed record of the basic core information for keyframes.<\/p><ul data-path-to-node=\"29\"><li><p data-path-to-node=\"29,0,0\">Each keyframe corresponds to a unique <code data-path-to-node=\"29,0,0\" data-index-in-node=\"14\">sample_token<\/code>, used to precisely identify that frame of data.<\/p><\/li><li><p data-path-to-node=\"29,1,0\">Developers use <code data-path-to-node=\"29,1,0\" data-index-in-node=\"6\">scene_token<\/code> You can <code data-path-to-node=\"29,1,0\" data-index-in-node=\"22\">scene.json<\/code> Quickly locate the specific simulation scenario to which the sample belongs in the file.<\/p><\/li><li><p data-path-to-node=\"29,2,0\">The file also provides the previous frame (<code data-path-to-node=\"29,2,0\" data-index-in-node=\"11\">prev<\/code>) and the next frame (<code data-path-to-node=\"29,2,0\" data-index-in-node=\"21\">next<\/code>) Token pointers can be used to construct continuous frame relations.<\/p><\/li><\/ul><h4 data-path-to-node=\"30\">sample_data.json<\/h4><p data-path-to-node=\"31\">utilization <code data-path-to-node=\"31\" data-index-in-node=\"3\">sample_token<\/code>Developers can <code data-path-to-node=\"31\" data-index-in-node=\"22\">sample_data.json<\/code> Comprehensively obtain detailed multisensor data for the corresponding frame, specifically including:<\/p><ul data-path-to-node=\"32\"><li><p data-path-to-node=\"32,0,0\"><code data-path-to-node=\"32,0,0\" data-index-in-node=\"0\">ego_pose_token<\/code>: The reference to the vehicle's ego-pose can be found in <code data-path-to-node=\"32,0,0\" data-index-in-node=\"38\">ego_pose.json<\/code> to obtain precise pose information (including 3D position and orientation) for that specific moment.<\/p><\/li><li><p data-path-to-node=\"32,1,0\"><code data-path-to-node=\"32,1,0\" data-index-in-node=\"0\">calibrated_sensor_token<\/code>: Calibration parameters for the corresponding sensor can be found in <code data-path-to-node=\"32,1,0\" data-index-in-node=\"62\">calibrated_sensor.json<\/code> Look up the sensor's intrinsic and extrinsic parameters.<\/p><\/li><li><p data-path-to-node=\"32,2,0\"><code data-path-to-node=\"32,2,0\" data-index-in-node=\"0\">filename<\/code>: The file path of the sensor's raw data. If the data comes from a camera, it will also include the image height (<code data-path-to-node=\"32,2,0\" data-index-in-node=\"54\">height<\/code>) and width (<code data-path-to-node=\"32,2,0\" data-index-in-node=\"65\">width<\/code>\uff09\u3002<\/p><\/li><li><p data-path-to-node=\"32,3,0\"><code data-path-to-node=\"32,3,0\" data-index-in-node=\"0\">timestamp<\/code>: Timestamp (unit: microseconds), used for hard time synchronization among multiple sensors.<\/p><\/li><li><p data-path-to-node=\"32,4,0\"><code data-path-to-node=\"32,4,0\" data-index-in-node=\"0\">is_key_frame<\/code>: Boolean, used to indicate whether a specific frame is a keyframe.<\/p><\/li><li><p data-path-to-node=\"32,5,0\"><code data-path-to-node=\"32,5,0\" data-index-in-node=\"0\">next \/ prev<\/code>: Tokens pointing to the next and previous frames, respectively, thereby enabling precise temporal association.<\/p><\/li><\/ul><h4 data-path-to-node=\"33\">sample_annotation.json<\/h4><p data-path-to-node=\"34\"><code data-path-to-node=\"34\" data-index-in-node=\"0\">sample_annotation.json<\/code> The file accurately records the 3D annotations of the detected objects in each keyframe, allowing for full <code data-path-to-node=\"34\" data-index-in-node=\"79\">sample_token<\/code> Perform a cross-table join. The main key fields included are as follows:<\/p><ol start=\"1\" data-path-to-node=\"35\"><li><p data-path-to-node=\"35,0,0\"><code data-path-to-node=\"35,0,0\" data-index-in-node=\"0\">instance_token<\/code>: A unique identifier for an object instance. Developers can <code data-path-to-node=\"35,0,0\" data-index-in-node=\"49\">instance.json<\/code> Look up the instance corresponding to <code data-path-to-node=\"35,0,0\" data-index-in-node=\"74\">category_token<\/code>(category information), as well as the keyframe tokens for the object's first and last appearances. Through <code data-path-to-node=\"35,0,0\" data-index-in-node=\"121\">category_token<\/code> Then you can further <code data-path-to-node=\"35,0,0\" data-index-in-node=\"143\">category.json<\/code> Retrieve the specific semantic category name (Category Name) for that instance.<\/p><\/li><li><p data-path-to-node=\"35,1,0\"><code data-path-to-node=\"35,1,0\" data-index-in-node=\"0\">visibility_token<\/code>: Visibility rating (divided into four levels; a higher number indicates greater visibility of the object). For specific definitions, see <code data-path-to-node=\"35,1,0\" data-index-in-node=\"57\">visibility.json<\/code> ...can be viewed there.<\/p><\/li><li><p data-path-to-node=\"35,2,0\">Geometry and pose information of the target object; these pose data are precisely defined in the sensor coordinate system:<\/p><ul data-path-to-node=\"35,2,1\"><li><p data-path-to-node=\"35,2,1,0,0\">Center point location (<code data-path-to-node=\"35,2,1,0,0\" data-index-in-node=\"7\">translation<\/code>)<\/p><\/li><li><p data-path-to-node=\"35,2,1,1,0\">Dimensions (<code data-path-to-node=\"35,2,1,1,0\" data-index-in-node=\"6\">size<\/code>)<\/p><\/li><li><p data-path-to-node=\"35,2,1,2,0\">Rotation angle (<code data-path-to-node=\"35,2,1,2,0\" data-index-in-node=\"6\">rotation<\/code>), using quaternions (<code data-path-to-node=\"35,2,1,2,0\" data-index-in-node=\"21\">Quaternion<\/code>) format.<\/p><\/li><\/ul><\/li><li><p data-path-to-node=\"35,3,0\">Point Cloud Statistics: The number of LiDAR points contained within the bounding box (<code data-path-to-node=\"35,3,0\" data-index-in-node=\"61\">num_lidar_pts<\/code>) and the number of millimeter-wave radar points (<code data-path-to-node=\"35,3,0\" data-index-in-node=\"84\">num_radar_pts<\/code>\uff09\u3002<\/p><\/li><li><p data-path-to-node=\"35,4,0\">Frame Association: Accurately records the token identifiers corresponding to the target instance in the previous and subsequent frames, respectively.<\/p><\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6dc69ef elementor-widget elementor-widget-heading\" data-id=\"6dc69ef\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-small\">03 Examples of SimData and Perception Model Deployment<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fd3a6a0 elementor-widget elementor-widget-heading\" data-id=\"fd3a6a0\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-small\">Usage and Truth Value Visualization<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f578c1e elementor-widget elementor-widget-text-editor\" data-id=\"f578c1e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p data-path-to-node=\"17\">SimData supports direct use <code data-path-to-node=\"40\" data-index-in-node=\"15\">nuScenes-devkit<\/code> When analyzing data, the actual methods for calling and using it are exactly the same as those for the native nuScenes dataset. Here is an example of how to call the code:<\/p><p data-path-to-node=\"17\">from nuscenes.nuscenes import NuScenes<br \/>nusc = NuScenes(version=\u2019v1.0-custom\u2019, dataroot=data_path, verbose=True)<\/p><p data-path-to-node=\"42\">Once the instantiated object has been successfully retrieved, developers can directly utilize the comprehensive toolchain provided by nuScenes to perform in-depth analysis of the SimData dataset and train perception models. In conjunction with <code data-path-to-node=\"42\" data-index-in-node=\"93\">cv2<\/code> maybe <code data-path-to-node=\"42\" data-index-in-node=\"99\">matplotlib<\/code> visualization libraries, you can intuitively visualize datasets in 3D:<\/p><ul data-path-to-node=\"43\"><li><p data-path-to-node=\"43,0,0\">6-channel camera image output with Ground Truth (GT) bounding boxes:<\/p><\/li><li><p data-path-to-node=\"43,1,0\">Synchronized LiDAR point cloud data enables the simultaneous generation of precise annotations from a bird\u2019s-eye view (BEV):<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cfaeed2 elementor-widget elementor-widget-image\" data-id=\"cfaeed2\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"689\" height=\"179\" src=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/\u5c4f\u5e55\u622a\u56fe-2026-06-16-143635.png\" class=\"attachment-large size-large wp-image-35348\" alt=\"\" srcset=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/\u5c4f\u5e55\u622a\u56fe-2026-06-16-143635.png 689w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/\u5c4f\u5e55\u622a\u56fe-2026-06-16-143635-300x78.png 300w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/\u5c4f\u5e55\u622a\u56fe-2026-06-16-143635-18x5.png 18w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/\u5c4f\u5e55\u622a\u56fe-2026-06-16-143635-600x156.png 600w\" sizes=\"(max-width: 689px) 100vw, 689px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1fa4d42 elementor-widget elementor-widget-heading\" data-id=\"1fa4d42\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-small\">Demonstration of bevformer Detection Results<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4684a47 elementor-widget elementor-widget-text-editor\" data-id=\"4684a47\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p data-path-to-node=\"45\">The following are pre-trained weights that were trained directly using the native nuScenes dataset, using <code data-path-to-node=\"45\" data-index-in-node=\"47\">BEVFormer-tiny<\/code> The following demonstrates the model's actual performance in object detection without any SimData-based incremental training or fine-tuning (zero-shot inference):<\/p><ol start=\"1\" data-path-to-node=\"46\"><li><p data-path-to-node=\"46,0,0\">BEVFormer Official Repository:<a class=\"ng-star-inserted\" href=\"https:\/\/github.com\/fundamentalvision\/BEVFormer\/tree\/master\" target=\"_blank\" rel=\"noopener\" data-hveid=\"6\">https:\/\/github.com\/fundamentalvision\/BEVFormer\/tree\/master<\/a><\/p><\/li><li><p data-path-to-node=\"46,1,0\">Authoritative Academic Papers on BEVFormer:<a class=\"ng-star-inserted\" href=\"https:\/\/arxiv.org\/pdf\/2203.17270\" target=\"_blank\" rel=\"noopener\" data-hveid=\"7\">https:\/\/arxiv.org\/pdf\/2203.17270<\/a><\/p><\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0129fec elementor-widget elementor-widget-heading\" data-id=\"0129fec\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-small\">Conclusion<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-46cd2df elementor-widget elementor-widget-text-editor\" data-id=\"46cd2df\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p data-path-to-node=\"49\">This paper provides an in-depth discussion of the critical importance of virtual datasets in the research and practical implementation of autonomous driving perception algorithms, and offers a comprehensive introduction to SimData\u2014a brand-new, high-fidelity virtual perception dataset generated using the aiSim high-precision simulation platform.<\/p><p data-path-to-node=\"50\">The paper provides a detailed explanation of SimData\u2019s data architecture, underlying schema, and specific parsing methods. It also conducts cross-dataset validation using mainstream open-source perception models (such as BEVFormer), thereby strongly demonstrating the high usability and technical effectiveness of this synthetic dataset in real-world R&amp;D environments.<\/p><p data-path-to-node=\"51\">Moving forward,<b data-path-to-node=\"51\" data-index-in-node=\"3\">Hongke Team<\/b>(Hongke Team) will be releasing more detailed data testing and metric comparison reports in the coming weeks to further quantify and validate the high domain consistency between SimData and real-world datasets. Through this series of in-depth technical studies, we have not only demonstrated the extreme high-fidelity characteristics of the aiSim simulation environment but also provided researchers and autonomous driving developers worldwide with a high-quality, plug-and-play, and highly scalable virtual perception data resource, continuing to provide strong support for the research, iteration, and model training of autonomous driving perception algorithms.<\/p><p data-path-to-node=\"52\">Please stay tuned<b data-path-to-node=\"52\" data-index-in-node=\"6\">HOSCO<\/b>Further information regarding<b data-path-to-node=\"52\" data-index-in-node=\"12\">Official Virtual Dataset<\/b>A major announcement! If you\u2019d like to learn more about our solutions for autonomous driving simulation and virtual datasets, please feel free to contact us.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-45ed7c8 elementor-widget elementor-widget-button\" data-id=\"45ed7c8\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/aiportek.com\/adas-simulator-aisim\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Go to aiSim Product Page<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t<div class=\"elementor-element elementor-element-9049411 e-flex e-con-boxed e-con e-parent\" data-id=\"9049411\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-cfcf4de e-flex e-con-boxed e-con e-parent\" data-id=\"cfcf4de\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-dbc1b58 elementor-widget elementor-widget-heading\" data-id=\"dbc1b58\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">Other Articles<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-74adc8f elementor-posts--align-left elementor-grid-3 elementor-grid-tablet-2 elementor-grid-mobile-1 elementor-posts--thumbnail-top elementor-card-shadow-yes elementor-posts__hover-gradient elementor-widget elementor-widget-posts\" data-id=\"74adc8f\" data-element_type=\"widget\" data-settings=\"{&quot;cards_row_gap&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:100,&quot;sizes&quot;:[]},&quot;cards_columns&quot;:&quot;3&quot;,&quot;cards_columns_tablet&quot;:&quot;2&quot;,&quot;cards_columns_mobile&quot;:&quot;1&quot;,&quot;cards_row_gap_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;cards_row_gap_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"posts.cards\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-posts-container elementor-posts elementor-posts--skin-cards elementor-grid\" role=\"list\">\n\t\t\t\t<article class=\"elementor-post elementor-grid-item post-35341 post type-post status-publish format-standard has-post-thumbnail hentry category-18 tag-aimotive tag-64\" role=\"listitem\">\n\t\t\t<div class=\"elementor-post__card\">\n\t\t\t\t<a class=\"elementor-post__thumbnail__link\" href=\"https:\/\/aiportek.com\/en\/simdata-aisim-virtual-dataset-autonomous-driving-perception\/\" tabindex=\"-1\" target=\"_blank\"><div class=\"elementor-post__thumbnail\"><img loading=\"lazy\" decoding=\"async\" width=\"1080\" height=\"562\" src=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-23.webp\" class=\"attachment-full size-full wp-image-35345\" alt=\"\" srcset=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-23.webp 1080w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-23-300x156.webp 300w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-23-1024x533.webp 1024w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-23-768x400.webp 768w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-23-18x9.webp 18w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/640-23-600x312.webp 600w\" sizes=\"(max-width: 1080px) 100vw, 1080px\" \/><\/div><\/a>\n\t\t\t\t<div class=\"elementor-post__badge\">Hongke Case<\/div>\n\t\t\t\t<div class=\"elementor-post__text\">\n\t\t\t\t<h3 class=\"elementor-post__title\">\n\t\t\t<a href=\"https:\/\/aiportek.com\/en\/simdata-aisim-virtual-dataset-autonomous-driving-perception\/\" target=\"&quot;_blank&quot;\">\n\t\t\t\t[Hongke Solutions] SimData High-Fidelity Virtual Dataset Solution: Perception Training for Autonomous Driving Based on aiSim\t\t\t<\/a>\n\t\t<\/h3>\n\t\t\t\t<div class=\"elementor-post__excerpt\">\n\t\t\t<p>Hongke has launched SimData, a high-fidelity virtual dataset built on the aiSim simulation platform and fully compatible with the nuScenes format. It provides high-quality multimodal training data for autonomous driving perception algorithms, LiDAR, and BEV models, effectively addressing the challenge of data collection in extreme scenarios (Edge Cases). Click now to learn about the development process and see real-world results!<\/p>\n\t\t<\/div>\n\t\t\n\t\t<a class=\"elementor-post__read-more\" href=\"https:\/\/aiportek.com\/en\/simdata-aisim-virtual-dataset-autonomous-driving-perception\/\" aria-label=\"Read more about \u3010\u8679\u79d1\u65b9\u6848\u3011 SimData\u9ad8\u4fdd\u771f\u865b\u64ec\u6578\u64da\u96c6\u65b9\u6848\uff1a\u57fa\u65bcaiSim\u7684\u81ea\u52d5\u99d5\u99db\u611f\u77e5\u8a13\u7df4\" tabindex=\"-1\" target=\"_blank\">\n\t\t\tRead more\t\t<\/a>\n\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-post__meta-data\">\n\t\t\t\t\t<span class=\"elementor-post-author\">\n\t\t\tHongKeTechnology\t\t<\/span>\n\t\t\t\t<span class=\"elementor-post-date\">\n\t\t\tJune 16, 2026\t\t<\/span>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/article>\n\t\t\t\t<article class=\"elementor-post elementor-grid-item post-35276 post type-post status-publish format-standard has-post-thumbnail hentry category-12 tag-elpro tag-45\" role=\"listitem\">\n\t\t\t<div class=\"elementor-post__card\">\n\t\t\t\t<a class=\"elementor-post__thumbnail__link\" href=\"https:\/\/aiportek.com\/en\/cold-chain-data-logger-single-use-vs-multi-use-hongke\/\" tabindex=\"-1\" target=\"_blank\"><div class=\"elementor-post__thumbnail\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"536\" src=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/614e016b0b6e8_4_1_2-Making-the-Right-Cold-Chain-Logistics-and-Packaging-Choices-for-Your-Products-1024x536-1.jpg\" class=\"attachment-full size-full wp-image-35281\" alt=\"\" srcset=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/614e016b0b6e8_4_1_2-Making-the-Right-Cold-Chain-Logistics-and-Packaging-Choices-for-Your-Products-1024x536-1.jpg 1024w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/614e016b0b6e8_4_1_2-Making-the-Right-Cold-Chain-Logistics-and-Packaging-Choices-for-Your-Products-1024x536-1-300x157.jpg 300w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/614e016b0b6e8_4_1_2-Making-the-Right-Cold-Chain-Logistics-and-Packaging-Choices-for-Your-Products-1024x536-1-768x402.jpg 768w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/614e016b0b6e8_4_1_2-Making-the-Right-Cold-Chain-Logistics-and-Packaging-Choices-for-Your-Products-1024x536-1-18x9.jpg 18w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/614e016b0b6e8_4_1_2-Making-the-Right-Cold-Chain-Logistics-and-Packaging-Choices-for-Your-Products-1024x536-1-600x314.jpg 600w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/div><\/a>\n\t\t\t\t<div class=\"elementor-post__badge\">Hongke Dry Goods<\/div>\n\t\t\t\t<div class=\"elementor-post__text\">\n\t\t\t\t<h3 class=\"elementor-post__title\">\n\t\t\t<a href=\"https:\/\/aiportek.com\/en\/cold-chain-data-logger-single-use-vs-multi-use-hongke\/\" target=\"&quot;_blank&quot;\">\n\t\t\t\t[Hongke Insights] Single-Use vs. Reusable Cold Chain Data Loggers: A Guide to Pharmaceutical GDP Compliance and Selection for Transportation\t\t\t<\/a>\n\t\t<\/h3>\n\t\t\t\t<div class=\"elementor-post__excerpt\">\n\t\t\t<p>How to Choose the Right Temperature Data Logger for the Pharmaceutical Cold Chain? This article provides an in-depth comparison of the pros and cons of single-use and reusable data loggers, in accordance with GMP\/GDP compliance standards, to help pharmaceutical companies and logistics providers in Hong Kong and Southeast Asia optimize temperature control management in their supply chains and reduce compliance risks when expanding into international markets. Click to learn about expert selection solutions!<\/p>\n\t\t<\/div>\n\t\t\n\t\t<a class=\"elementor-post__read-more\" href=\"https:\/\/aiportek.com\/en\/cold-chain-data-logger-single-use-vs-multi-use-hongke\/\" aria-label=\"Read more about [Hongke Insights] Single-Use vs. Reusable Cold Chain Data Loggers: A Guide to Pharmaceutical GDP Compliance and Selection for Transportation\" tabindex=\"-1\" target=\"_blank\">\n\t\t\tRead more\t\t<\/a>\n\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-post__meta-data\">\n\t\t\t\t\t<span class=\"elementor-post-author\">\n\t\t\tHongKeTechnology\t\t<\/span>\n\t\t\t\t<span class=\"elementor-post-date\">\n\t\t\tJune 12, 2026\t\t<\/span>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/article>\n\t\t\t\t<article class=\"elementor-post elementor-grid-item post-35194 post type-post status-publish format-standard has-post-thumbnail hentry category-1 category-18 tag-vuzix tag-42\" role=\"listitem\">\n\t\t\t<div class=\"elementor-post__card\">\n\t\t\t\t<a class=\"elementor-post__thumbnail__link\" href=\"https:\/\/aiportek.com\/en\/hongke-vuzix-m400-ar-smart-glasses-telemedicine\/\" tabindex=\"-1\" target=\"_blank\"><div class=\"elementor-post__thumbnail\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"768\" src=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/vuzix_poc_3-1024x768-1.jpg\" class=\"attachment-full size-full wp-image-35201\" alt=\"\" srcset=\"https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/vuzix_poc_3-1024x768-1.jpg 1024w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/vuzix_poc_3-1024x768-1-300x225.jpg 300w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/vuzix_poc_3-1024x768-1-768x576.jpg 768w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/vuzix_poc_3-1024x768-1-16x12.jpg 16w, https:\/\/aiportek.com\/wp-content\/uploads\/2026\/06\/vuzix_poc_3-1024x768-1-600x450.jpg 600w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/div><\/a>\n\t\t\t\t<div class=\"elementor-post__badge\">Hongke Dynamic<\/div>\n\t\t\t\t<div class=\"elementor-post__text\">\n\t\t\t\t<h3 class=\"elementor-post__title\">\n\t\t\t<a href=\"https:\/\/aiportek.com\/en\/hongke-vuzix-m400-ar-smart-glasses-telemedicine\/\" target=\"&quot;_blank&quot;\">\n\t\t\t\t[Hongke News] Hongke AR Smart Glasses Drive a Comprehensive Upgrade in Telemedicine \u2013 Vuzix M400 Smart Healthcare Solution\t\t\t<\/a>\n\t\t<\/h3>\n\t\t\t\t<div class=\"elementor-post__excerpt\">\n\t\t\t<p>Hongke has partnered with Chunghwa Telecom to introduce the Vuzix M400 enterprise-grade AR smart glasses, helping to upgrade telemedicine services in remote areas! By breaking down geographical barriers through \"first-person view\" and hands-free collaboration, this initiative accelerates digital transformation and the implementation of smart healthcare applications for B2B medical institutions and care providers. Click to learn more about the full Proof of Concept (POC) solution.<\/p>\n\t\t<\/div>\n\t\t\n\t\t<a class=\"elementor-post__read-more\" href=\"https:\/\/aiportek.com\/en\/hongke-vuzix-m400-ar-smart-glasses-telemedicine\/\" aria-label=\"Read more about [Hongke News] Hongke AR Smart Glasses Drive a Comprehensive Upgrade in Telemedicine \u2013 Vuzix M400 Smart Healthcare Solution\" tabindex=\"-1\" target=\"_blank\">\n\t\t\tRead more\t\t<\/a>\n\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-post__meta-data\">\n\t\t\t\t\t<span class=\"elementor-post-author\">\n\t\t\tHongKeTechnology\t\t<\/span>\n\t\t\t\t<span class=\"elementor-post-date\">\n\t\t\tJune 9, 2026\t\t<\/span>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/article>\n\t\t\t\t<\/div>\n\t\t\n\t\t\t\t<div class=\"e-load-more-anchor\" data-page=\"1\" data-max-page=\"36\" data-next-page=\"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/posts\/35341\/page\/2\/\"><\/div>\n\t\t\t\t<nav class=\"elementor-pagination\" aria-label=\"Pagination\">\n\t\t\t<span class=\"page-numbers prev\">\"<\/span>\n<span aria-current=\"page\" class=\"page-numbers current\"><span class=\"elementor-screen-only\">Page<\/span>1<\/span>\n<a class=\"page-numbers\" href=\"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/posts\/35341\/page\/2\/\"><span class=\"elementor-screen-only\">Page<\/span>2<\/a>\n<a class=\"page-numbers\" href=\"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/posts\/35341\/page\/3\/\"><span class=\"elementor-screen-only\">Page<\/span>3<\/a>\n<span class=\"page-numbers dots\">...<\/span>\n<a class=\"page-numbers\" href=\"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/posts\/35341\/page\/5\/\"><span class=\"elementor-screen-only\">Page<\/span>5<\/a>\n<a class=\"page-numbers next\" href=\"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/posts\/35341\/page\/2\/\">\"<\/a>\t\t<\/nav>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Hongke has launched SimData, a high-fidelity virtual dataset built on the aiSim simulation platform and fully compatible with the nuScenes format. It provides high-quality multimodal training data for autonomous driving perception algorithms, LiDAR, and BEV models, effectively addressing the challenge of data collection in extreme scenarios (Edge Cases). Click now to learn about the development process and see real-world results!<\/p>","protected":false},"author":1,"featured_media":35345,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[18],"tags":[65,64],"class_list":["post-35341","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-18","tag-aimotive","tag-64"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/posts\/35341","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/comments?post=35341"}],"version-history":[{"count":16,"href":"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/posts\/35341\/revisions"}],"predecessor-version":[{"id":35364,"href":"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/posts\/35341\/revisions\/35364"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/media\/35345"}],"wp:attachment":[{"href":"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/media?parent=35341"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/categories?post=35341"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aiportek.com\/en\/wp-json\/wp\/v2\/tags?post=35341"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}