Category: ADAS

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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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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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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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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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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Dimensionality reduction and how it helps reduce the search space by leveraging known information

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 to remove data bias, do a deep dive on different approaches

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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Loose Coupling vs Tight Coupling; Best of Both Worlds

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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