True positive rates, or the proportion of positive cases that are correctly identified, are an important consideration in many areas. One way to boost true positive rates is to utilize independent combinatorics, a set of techniques that involve combining multiple independent pieces of information or evidence to make a decision. Here are some specific techniques…
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Artificial intelligence (AI) has come a long way in recent years, with many industries adopting it to improve efficiency and productivity. However, there is always room for improvement, and one area where AI can be further enhanced is in terms of accuracy. Currently, many AI systems have an accuracy rate of around 99.9%, which is…
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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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Message queues are a software component that allow different parts of a system, or different systems, to communicate with each other by passing messages. They are often used in architectures that are distributed, meaning that they consist of multiple independent systems that need to communicate with each other. One common use case for message queues…
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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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Noise is an inherent part of any machine learning (ML) model development process. It refers to any random or unpredictable variations in the data that can impact the accuracy of the model. Noise can come from a variety of sources, including measurement errors, data quality issues, and even the sampling process itself. In the process…
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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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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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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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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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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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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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