Autonomous Driving
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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...