Category: Uncategorized

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’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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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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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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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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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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Self-driving vehicles are a complex system that require a variety of sensors, algorithms, and hardware components to function properly. These components can be grouped into several different categories, including perception, localization, planning and control, and hardware. Perception Perception refers to the ability of the self-driving vehicle to understand and interpret its surroundings. This is typically…
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System trade-offs are an inherent part of the architectural design of self-driving systems. These trade-offs involve making decisions that prioritize certain system characteristics over others, based on the specific requirements and constraints of the application. In the context of self-driving systems, these trade-offs often involve balancing the competing demands of safety, performance, cost, and flexibility.…
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A distributed file system is a network-based file system that allows multiple computers to access and share data stored on a central server. This type of system is necessary for self-driving fleet infrastructure because it allows for seamless data sharing and collaboration among the various vehicles and support systems in the fleet. One key aspect…
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Introduction In modern computing systems, it is common to experience bottlenecks, which are points of congestion that limit the overall performance of the system. These bottlenecks can occur in various components of the system, including storage, compute, memory, and network. In this white paper, we will examine each of these components and discuss how bottlenecks…
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Stochastic modeling techniques are a powerful tool in traffic modeling, allowing for the simulation and analysis of various scenarios. One such technique is the use of discrete event simulators, which can leverage these techniques to model the behavior of roadways and their traffic. One example of a stochastic modeling technique that can be used in…
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Discrete event simulation (DES) is a mathematical model used to simulate the operation of a system over time. It is a popular technique in the development of self-driving cars because it allows engineers to test and evaluate the behavior of the vehicle in a simulated environment before implementing it in the real world. One of…
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Euler angles are a mathematical tool used to define rotations in three-dimensional space. They consist of three angles, typically referred to as pitch, roll, and yaw, which represent rotations about the x, y, and z axes, respectively. These angles can be used to create transformation matrices, which can be used to define the position and…
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Observing vehicle movements and traffic infrastructure to feed into a heuristic learning model as time series event data is an important aspect of autonomous vehicle development. Heuristic learning models are designed to improve the decision-making capabilities of autonomous vehicles by learning from past experiences and adapting to new situations. To do this, the model needs…
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A directed acyclic graph (DAG) is a type of graph in which edges have a direction and there are no cycles, meaning that a vertex cannot reach itself through a series of edges. DAGs are commonly used to represent complex relationships between tasks in a workflow. One example of a tool that uses DAGs is…
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Systems engineering is a field that involves the design, development, and maintenance of complex systems. These systems can be physical, such as an aircraft, or digital, such as a software application. Regardless of the type of system, one of the key goals of systems engineering is to ensure that the system is optimized for performance.…
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Forward and reverse transformations are techniques that can be used to analyze and understand the behavior of systems, including those involving artificial intelligence (AI). These techniques can be used to examine the impact of noise on a system and how the system responds to different inputs or perturbations. A forward transformation involves applying a set…
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Artificial intelligence (AI) is a rapidly growing field with the potential to revolutionize many aspects of our lives. One of the key factors driving the development of AI is the availability of high-quality data. Without large amounts of data, it is difficult for AI algorithms to learn and improve. However, simply having large amounts of…
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Introduction: Basic 2D and 3D annotation tools are commonly used in the field of computer vision and machine learning for tasks such as object detection and segmentation. These tools allow users to label and annotate images or videos with bounding boxes, masks, or points, which can then be used to train and evaluate machine learning…
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We are excited to announce the RoadMentor accelerator program, designed to support startups in the development of the next generation of artificial intelligence (AI) technologies. As the AI industry evolves, the quality and quantity of data and tooling will become increasingly important differentiators. Precise ground truth labeled data is essential for solving edge cases and…
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Self-supervised simultaneous localization and mapping (SLAM) is a technique for building a map of an environment and determining the location of a vehicle within that environment, using only onboard sensors and no prior knowledge of the environment. It is a key problem in robotics and autonomous systems, as it enables the system to navigate and…
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Neural radiance fields (NRFs) are a type of deep neural network (DNN) that can be used to estimate the pose of an object in 3D space. Pose estimation is the process of determining the position and orientation of an object in relation to a reference frame, and it is an important problem in many applications,…
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Stable diffusion is a concept in control theory that refers to the ability of a system to maintain a stable state despite external perturbations or changes in the environment. In the context of autonomous vehicles, stable diffusion can help improve the safety and reliability of the vehicle’s navigation system by allowing it to maintain a…
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High Definition (HD) maps are digital representations of the physical environment, typically used for autonomous vehicle navigation. They are a critical component of autonomous vehicle systems, as they provide precise, up-to-date information about the location and layout of the roads, lanes, intersections, and other features of the environment. HD maps are typically structured into several…
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Introduction: 2D and 3D annotation tools are commonly used in a variety of applications, including computer vision, video analysis, and robotics. These tools allow human annotators or machine learning frameworks to label and classify objects or events in images or videos, providing important information for training and improving machine learning models. However, manual annotation can…
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Introduction: RoadMentor’s cloud-based aerial to ground fusion technology is a revolutionary approach to improving the accuracy of ego-location in vehicles. By combining data from onboard sensors with airborne imagery and point clouds, RoadMentor’s technology is able to significantly improve the localization of ego-vehicles. This white paper will provide an overview of the RoadMentor technology and…
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Tesla and Waymo are two of the leading companies in the field of autonomous vehicles, and their approaches to achieving autonomy differ in some key ways. While both companies have made significant progress in developing autonomous driving technology, we believe that Tesla’s approach is ultimately the better one. Here’s why: Tesla is focused on deploying…
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Introduction: In the field of autonomous vehicles, data plays a crucial role in the development and deployment of machine learning models. These models must be trained on large datasets and constantly updated with new data in order to maintain their accuracy and performance. However, the process of collecting, cleaning, and preparing data for machine learning…
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