Quantifying the uncertainty of supervised learning models plays an important role in making
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …

Automated driving systems (ADSs) aim to improve the safety, efficiency and comfort of future
vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This …

As deep learning continues to dominate all state-of-the-art computer vision tasks, it is
increasingly becoming an essential building block for robotic perception. This raises …

While deep neural network (DNN) models have become extremely popular for object
detection in automated driving systems (ADS), the dynamic and varied nature of the road …

In this work, we present an uncertainty-based method for sensor fusion with camera and
radar data. The outputs of two neural networks, one processing camera and the other one …

The use of Deep learning in Cyber-Physical Systems (CPSs) is gaining popularity due to its
ability to bring intelligence to CPS behaviors. However, both CPSs and deep learning have …

Object detection in autonomous cars is commonly based on camera images and Lidar
inputs, which are often used to train prediction models such as deep artificial neural …

Object detection plays a critical role in autonomous driving, but current state-of-the-art object
detectors will inevitably fail in many driving scenes, which is unacceptable for safety-critical …

Using terrestrial laser scanning (TLS) technology, forests can be digitized at the centimeter-
level to enable fine-scale forest management. However, there are technical barriers to …

The majority of uncertainty quantification methods for deep object detectors are based on
the network output, such as sampling strategies like Monte-Carlo dropout or deep …