A false negative in object detection describes an object that was not correctly localised and
classified by a detector. In concurrent work, we introduced five'false negative mechanisms' …

Safety and performance are key enablers for autonomous driving: on the one hand we want
our autonomous vehicles (AVs) to be safe, while at the same time their performance (eg …

Conventionally, the methods used for the sorting of tomatoes are manual. These methods
are costly, non-productive, and their reliability is uncertain. With advancing technology, deep …

Recent work has focused on generating synthetic imagery to increase the size and
variability of training data for learning visual tasks in urban scenes. This includes increasing …

Reliable detection of various objects and road users in the surrounding environment is
crucial for the safe operation of automated driving systems (ADS). Despite recent progresses …

For a safe market launch of automated vehicles, the risks of the overall system as well as the
sub-components must be efficiently identified and evaluated. This also includes camera …

This letter proposes an edge learning-based offloading framework for autonomous driving,
where the deep learning tasks can be offloaded to the edge server to improve the inference …

Failures in autonomous driving caused by complex traffic situations or model inaccuracies
remain inevitable in the near future. While much research is focused on how to prevent such …

Trajectory prediction is one of the key components of the autonomous driving software stack.
Accurate prediction for the future movement of surrounding traffic participants is an important …

Performance monitoring of object detection is crucial for safety-critical applications such as
autonomous vehicles that operate under varying and complex environmental conditions …