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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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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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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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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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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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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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 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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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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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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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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CVAT (Computer Vision Annotation Tool) is an open source tool developed by Intel that allows users to label and annotate images and video data for training machine learning models. MLFlow is an open source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiment runs, organizing code, and reproducing runs, among…
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CVAT (Computer Vision Annotation Tool) is an open-source annotation tool for computer vision tasks that allows users to label and manage large datasets quickly and efficiently. Integrating CVAT with an MLFlow framework can streamline the data labeling process and make it easier to track and analyze the performance of your machine learning models. Here is…
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Removing dimensions or making invariant features is a technique used to reduce the search space in a problem by eliminating certain variables or making them irrelevant. This can be especially useful in LiDAR slam, which has a 6Dof search space (x, y, z, roll, pitch, yaw). By reducing the dimensions, the number of permutations in…
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Data balancing is the process of ensuring that a machine learning dataset is representative of the real-world population from which it is drawn. This is important because if a dataset is biased, then the machine learning model that is trained on that dataset will also be biased. Bias in machine learning models can lead to…
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Data structures are the foundation of efficient algorithms and play a crucial role in determining the time complexity of a piece of code. Time complexity refers to the amount of time it takes for an algorithm to complete, and it is a measure of how the runtime of an algorithm grows as the input size…
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Loose coupling and tight coupling refer to the degree of interdependence between different components in a system. In software development, loose coupling refers to the design of components that can operate independently of one another and do not rely heavily on the internal details of other components. Tight coupling, on the other hand, refers to…
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In the field of autonomous systems, the choice between real-time and cloud-based architectures can have significant consequences for the performance, reliability, and cost of a system. In this article, we will explore the key differences between these two approaches and the trade-offs involved in choosing one over the other. We will also discuss how cloud-based…
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Brute forcing is a method of searching through a search space by testing every possible solution. This method can be useful for validating search algorithms, as it provides a means of comparing the results of the search algorithm to the true solution. One way to validate search algorithms using brute forcing is to quantify the…
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Wheel odometry is a method used to measure the distance traveled by a vehicle by tracking the rotations of its wheels. This can be done through various methods such as using encoders, sensors, or by measuring the speed of the wheels using an OBD (on-board diagnostics) sensor. One of the main applications of wheel odometry…
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ROS (Robot Operating System) is a popular open-source robotics framework that provides libraries and tools for building robot applications. There are currently two versions of ROS: ROS 1 and ROS 2. In this article, we will explore the tradeoffs and advantages of both versions and discuss the tools available for converting between them. We will…
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Autoencoders are neural network architectures that are used to learn a compact representation of input data, called the encoding, and then reconstruct the input data from this encoding. Autoencoders can be used to process point cloud data, which is a set of points in space that represent the surface of an object, as well as…
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Camera calibration is the process of determining the intrinsic and extrinsic parameters of a camera. Intrinsic parameters are properties of the camera itself, such as its focal length and principal point, while extrinsic parameters describe the position and orientation of the camera in the world. Accurate calibration is important for tasks such as 3D reconstruction,…
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Self-driving cars have the potential to revolutionize the transportation industry by providing a low-cost, efficient, and safe alternative to traditional ride-hailing services like Uber and Lyft. In this article, we will estimate the unit economics of a self-driving car compared to an Uber driver to better understand the potential cost savings and benefits of this…
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