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Auto Labeling for Efficient Annotation Process

Auto labeling techniques have revolutionized the annotation process in various fields, enabling faster and more efficient data labeling. In this post, we will explore two popular auto labeling workflows: map projection and tracking. These techniques leverage existing information and object movement patterns to automate the annotation process. 1. Map Projection for Auto Labeling One effective…
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Hyperspec: Streamlined Data Management and Performance-Driven Models

Hyperspec’s offerings to customers encompasses various key features to help businesses accelerate adoption of autonomous systems. Data Management and Model Performance Improvement We provide a comprehensive data management system that enables efficient handling of data. This includes conducting annotations, performing training, and ensuring the verification and validation of model performance. Once the model has shown…
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The Impact of Autonomous Systems on Labor, Trade and Economic Growth

The rise of autonomous systems, particularly autonomous mobile robots (AMRs), is revolutionizing industries, impacting labor demand and trade deficits. Labor Shortages and Growing Demand From a macro perspective, deploying advanced technology solutions has significant market impact. As we analyze labor and demand, it becomes evident that while the demand for labor is steadily increasing year…
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Sensor Calibration Service using Fisheye GL and LiDAR Projection Mapping with HTML5 Canvas and JavaScript

Introduction: Sensor calibration is essential for accurate data interpretation and fusion in various applications, such as autonomous vehicles, robotics, and remote sensing. This blog post will provide a step-by-step guide on how to calibrate sensors using Fisheye GL and LiDAR projection mapping onto different camera perspectives using HTML5 Canvas and JavaScript. Additionally, we will discuss…
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RoadMentor Demo Day March 7

The world of artificial intelligence is rapidly evolving, and as AI performance improves from 95% to 99.99999%, traditional annotation methodologies are no longer sufficient to keep up with the changing AI landscape. Organizations that fail to adapt risk falling behind the competition. To address this issue, Hyperspec AI has launched RoadMentor, a SaaS platform designed…
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Orchestrating ML Success on Your Own Terms: The Benefits of RoadMentor’s Customer Infrastructure

The RoadMentor platform is an innovative machine learning operations (MLOps) platform specifically designed for advanced driver assistance systems (ADAS). This platform leverages the latest in infrastructure orchestration technologies to provide customers with a powerful and flexible solution for managing their ML models and workflows. In this blog, we will delve into the key features of…
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The Benefits of a Unified Product Experience

The market for machine learning algorithms for autonomous driving is still relatively young and, as a result, remains highly fragmented. With so many companies and organizations developing their own unique solutions and technologies, one must go to one company for data collection, another for management, and others for model development, MLOps, and even deployment. It…
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Navigating the World with Words: How Large Language Models Power Autonomous Systems

HD maps are a critical component of autonomous vehicle technology, providing information on the physical environment in which the vehicle operates. The semantic layer of an HD map is a critical component of this information, representing the meaning and context of the physical environment. One of the most effective ways to encode this information is…
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How to conquer distortion and converge towards higher quality sensor fusion.

Distortion is an important factor to consider in image feature tracking, image stitching, point cloud registration, and fusing data from multi-modal sensors. Distortion can occur due to various factors such as lens imperfections, camera calibration errors, and atmospheric effects. It can cause image features to appear misaligned, leading to inaccuracies in the final results. Image…
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How to stitch multiple cameras together on a moving vehicle

Image stitching is the process of combining multiple images together to create a seamless panorama or a large-scale image. This technique is commonly used in photography, virtual reality, and mapping applications. In this blog, we will take a deep dive into the concepts of image stitching, including sensor and vehicle frame of reference, origin point,…
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Down in the trenches with Robert, a Geospatial Ops Manager for a large ADAS team

As a Geospatial Ops Manager, Robert is responsible for running product operations for a large Advanced Driver Assistance Systems (ADAS) team at an Original Equipment Manufacturer (OEM). This includes managing multiple teams that handle different tasks such as lane segmentation, free space detection, map creation, and map updating. However, Robert has been facing several challenges…
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How to use The Potential of Unsupervised Auto-Labeling: A Guide to Building Scalable MLOps Pipelines

Building a scalable MLOps pipeline for unsupervised machine learning can be a challenging task for engineers, but with the right approach, it can be done efficiently. One key aspect of building a scalable pipeline is to use unsupervised machine learning algorithms to auto-label images and point cloud data. This can be done by segmenting the…
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Unlock the Power of Vision Analytics with Fleet Queries: A Guide to Optimizing Your ADAS Fleet

Advanced Driver Assistance Systems (ADAS) are becoming increasingly prevalent in today’s vehicles, and with that comes the need for efficient and effective data management. One powerful tool for managing data from a fleet of ADAS-equipped vehicles is the fleet query. A fleet query is a command that is sent to a fleet of ADAS vehicles,…
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Revolutionizing L3 ADAS: Harnessing the Power of Test Harnesses and MLOps for Safe and Reliable Automotive Technology

Regression testing is a crucial aspect of developing L3 Advanced Driver Assistance Systems (ADAS) technology. It involves re-testing the entire system or parts of it, after changes or modifications have been made, to ensure that the changes did not introduce any new bugs or defects. This is particularly important for ADAS systems, which rely on…
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What is ADAS (Level 2 and below)?

Advanced Driver Assistance System (ADAS) is a technology that helps drivers to drive safely and efficiently. It is a combination of sensors, cameras, and other technologies that work together to help drivers monitor their speed, maintain their lane, and avoid collisions. ADAS can be found in many modern cars, and it is becoming increasingly popular…
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Revolutionizing the Road: How Hyperspec AI’s Fleet Learning Approach is Changing the Game for Detecting Lane Merges on the Highway

Automating the process of data annotation has been a long-standing challenge in the field of machine learning, especially when it comes to detecting lane merges on the highway. The traditional approach of manually labeling data is not only costly in terms of time and resources, but it also hinders the ability to quickly detect lane…
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Collaboration vs Consolidation: Navigating the Evolving Autonomous Driving Industry

The evolution of the Autonomous Driving (AD) industry has been rapid and dynamic, As the industry has grown, we have seen a wide variety of approaches to the development of Autonomous Vehicles (AVs). Some companies have focused on developing specific subunits, such as perception or planning, while others have attempted to develop a full autonomous…
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Navigating the Road to Success: How Abstracting Maps for Self-Driving Cars Ensures Safe and Efficient Operation

Self-driving cars rely heavily on maps to navigate and make decisions while on the road. These maps must be accurate, detailed, and up-to-date in order for the car to operate safely and efficiently. However, as self-driving cars become more prevalent, it has become clear that the way maps are created and used needs to change.…
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Uncovering ADAS Failure Cases: The Importance of Edge Vision Analytics and Fleet Queries in Vehicle Safety

Collecting failure cases from a fleet of L3 ADAS vehicles is a crucial task in ensuring the safety and reliability of these systems. With the increasing use of advanced driver assistance systems (ADAS) in vehicles, it is important to have a reliable and efficient method for collecting and analyzing data from these systems. One way…
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Drive smarter, not harder: The OODA loop’s way to advanced driving assistance

The OODA loop, or Observe, Orient, Decide, and Act loop, is a decision-making framework that was developed by military strategist and United States Air Force Colonel John Boyd. The OODA loop is based on the idea that in any situation, the side that can cycle through the loop faster and more effectively will have a…
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Navigating the complexities of coordinate frames: A guide to understanding the differences in Three.js, ROS, and Unreal Engine

Coordinate frames are an important aspect of robotics and computer graphics, as they determine the position and orientation of objects in 3D space. However, different platforms and software libraries use different conventions for their coordinate frames, which can lead to confusion and errors when working with multiple systems. Three.js, ROS, and Unreal Engine are all…
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Jane, a superstar ADAS engineer

As the sun rose over the bustling city, Jane, an Adas engineer at a leading autonomous vehicle company, woke up to the familiar sound of her alarm clock. She knew that today would be a challenging day, filled with long hours and intense problem-solving. Jane arrived at the office and immediately got to work on…
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Unsupervised Machine Learning and Speed Ups in Labeling

Unsupervised machine learning is a type of machine learning that involves training a model on a dataset without providing it with labeled examples. Instead, the model is asked to discover the underlying structure of the data on its own. One popular technique for unsupervised machine learning is clustering, which involves grouping similar data points together.…
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The Purpose of the Map

There’s a scene from The Office (American) where Michael so literally follows the instructions from his GPS that he drives into the lake while screaming ‘THE MACHINE KNOWS’. While it might seem like this was written for TV, it actually happened to a woman in Tobermory, ON and it’s easy to find other unbelievable driving…
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Javascript, Caching to Optimize Network Traffic

Browsers use caching to temporarily store resources locally on the device, such as HTML, CSS, and JavaScript files, images, and videos. This allows the browser to quickly access the resources without having to make additional requests to the server. One way browsers can enable device-side caching is through the use of local storage. Local storage…
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Training PackNet-SFM with RGB + PointCloud Data

Fusing time synchronized RGB camera images with point cloud data is a powerful approach for training a PackNet-SFM (Structure from Motion) model in PyTorch. This technique combines the benefits of both RGB images and point cloud data to improve the accuracy and robustness of the SFM model. In this article, we will discuss the various…
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Roadmap Compression via Streamlining MLOps Infrastructure and its Impacts on ADAS programs

Advanced Driver Assistance Systems (ADAS) are becoming increasingly important in the automotive industry as they offer a wide range of features that enhance the safety and comfort of the driving experience. However, the development and deployment of ADAS systems can be a complex and time-consuming process. Traditional hand-coded algorithms and feature engineering, while they have…
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Market size of ADAS development tools. Bottoms up and top down calculations

ADAS: 20.73 billion in 2021 to 74.57 billion by 2030 with a CAGR of 14.2% AI: 119.78 billion in 2022 to $ 1.6 trillion by 2030 with a registered CAGR of 38.1% AI within ADAS: $6 billion in 2022 to $600 billion by 2032, at a CAGR of 55% The market size of Advanced Driver Assistance…
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Why Overfitting Data is a Handicap for ADAS Deployment

ADAS enabled vehicles are most commonly driven on highways and main roads for a number of reasons. One reason is that these types of roads tend to have less complex driving scenarios compared to residential areas or side roads. Highways and main roads often have fewer intersections and more predictable traffic patterns, which can make…
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Integrating Federated Learning into CVAT & MLFlow

Federated learning is a machine learning technique that enables the training of models on decentralized data, without the need for the data to be centralized in one location. Instead, data is distributed across a number of different devices or edge devices, such as smartphones or IoT devices, and the model is trained by aggregating updates…
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