Data quality is one of the most critical factors in algorithm training. What’s the impact data annotation quality has on an algorithm’s performance and output in particular? And what’s the price of not getting it right? I’ll point out the...
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The Impact of Annotation Errors on Neural Networks
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The Solid Facts of Ground Truth Annotations
What are ground truth labels?‘Ground truth’ represents the objective, humanly verifiable observation of the state of an object or an information that might be considered a fact. The term ‘ground truth’ has recently risen in popularity thanks to the adoption...
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A Case for Sensor Fusion for 3D Object Annotations
Achieving the highest quality of annotated perception data while increasing the automation level is a central challenge for computer vision AI. In our blog about the use of 2D and 3D sensor data for machine perception we’ve established that we’ll...
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A Matter of Perspective - 3D vs. 2D Sensor Data for Machine Perception
One of the biggest differences between humans and machines is in the way we perceive our surroundings. We both exist in a three dimensional world, but while humans are naturally capable of figuring out complex geometry, the effects of perspective,...
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Semantic Segmentation - The Misunderstood Data Annotation Type
We all strive towards what is considered ‘the best’. We want to provide the best service to our clients, to have the best product, the best team and the best software. In the case of Deep Learning solutions for computer...
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Large Scale Annotation for the Future of Autonomous Driving. A Testimonial.
With more AD/ ADAS platforms, sensors and self-driving algorithms trying to make it onto the market, the future of autonomous driving is being decided in large AI training and validation projects. To develop and test a self-driving AI requires more...
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Augmenting Real-world Road Scenarios
What does data augmentation mean in autonomous driving?Data augmentation in autonomous driving simulation, also called scenario fuzzing or scenario variation, is a method that creates small variations of the input data in a simulated environment. To cope with the infinite...
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A Feast of Sensor Data - Feeding Self-driving Algorithms
Training the algorithms of AI-based systems for autonomous or highly automated driving requires enormous volumes of data to be captured and processed. The algorithms must be able to master numerous challenges so that self-driving cars can detect all essential details...
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Teaching a self-driving AI to see, analyze and act. A case study.
Highly automated and autonomous driving places enormous pressure on the safety and reliability of its technology. Flawed reference data can mess up the entire training process of an autonomous driving algorithm, from perception and situation analysis to behavior planning. Bosch...
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Real-world Scenario Generation in Comparison with Object-list-based Scenarios
What we discussed so far in Scenario Generation In our last blog post, we described why and how we generate real-world scenarios from measurement data at large scale for testing and validation of automated driving functions. We also gave an...
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Real-world Scenarios — The Foundation to Develop and Test Autonomous Driving
Currently, there is a big shift in the automotive industry. Not only the shift from internal combustion engines to electrified powertrains or the shift from owning a car to shared mobility services but also the transition from manual assisted driving...
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Why Wheelchairs Are Not Trash Cans
Imagine self-driving cars participating in our daily traffic. While driving on the street, a person sitting in a wheelchair is contemplating if he/she should cross the road. The self-driving car is able to recognize the person and wheelchair – but it...
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Quality — The Next Frontier for Training and Validation Data
It is known bounding box annotations can be prone to noise. Depending on the raw data, bounding boxes can contain more objects inside their boundaries than the classified object. This noise influences the learnings of the algorithm.In this article, we...