general

Semantic Segmentation Datasets for Urban Driving Scenes

One of the major applications of machine learning in autonomous driving is semantic segmentation or scene parsing of urban driving scenes. Until a few years ago, semantic segmentation was one of the most challenging problems in computer. Nowadays deep learning has revolutionized computer vision, enabling accurate environment recognition beyond human Read more…

By Krunal, ago
Development

Dataset Management for Machine Learning

Previously, we have discussed how Machine Learning is fundamentally different from conventional software development. Machine Learning is data driven: performance doesn’t scale with the development effort, but with the amount of data used for training. Retraining with new data can even be automated, if data is recurrently updated. Surprisingly, training Read more…

By Felix, ago
Basics

The 6 levels of autonomous driving

The autonomy of cars is defined by six levels in the SAE standard J3016. SAE’s levels range from zero autonomy, where the car is operated solely by its human driver, to full autonomy, where the car is operating independent from human input. Level 0 – No autonomy A level 0 Read more…

By Jakob, ago
Development

The difference between conventional software development and Machine Learning

Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), play a fundamental role in SW stacks for autonomous driving. Machine Learning is essential for recognizing other traffic participants, can substantially improve localization and prediction, and even show great potential for decision-making and path planning. CNNs have already made it to series Read more…

By Felix, ago

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