Humans working with autonomous artificially intelligent systems may not be experts in the
inner workings of their machine teammates, but need to understand when to employ, trust …

Autonomous vehicles can behave unexpectedly, as automated systems that rely on data-
driven machine learning have shown to infer false predictions or misclassifications, eg, due …

In contemporary autonomous driving systems relying on sensor fusion, traditional digital
processors encounter challenges associated with analogue-to-digital conversion and …

In autonomous driving, complex traffic scenarios can cause situations that require human
supervision to resolve safely. Instead of only reacting to such events, it is desirable to predict …

As illustrations of what constitutes the Automated City, this chapter highlights (among many)
three types of technologies:(1) automated vehicles,(2) robots in indoor public spaces and …

Autonomous Cyber-Physical Systems must often operate under uncertainties like sensor
degradation and shifts in the operating conditions, which increases its operational risk …

Online corner case detection is crucial for ensuring safety in autonomous driving vehicles.
Current autonomous driving approaches can be categorized into modular approaches and …

In recent years, vehicular networks have seen a proliferation of applications and services
such as image tagging, lane detection, and speech recognition. Many of these applications …

We propose the concept of error-reversing autoencoders (ERA) for correcting pixel-wise
errors made by an arbitrary semantic segmentation model. For this, we reframe the …

With every passing day, robots either autonomous or having humans-in-the-loop, are
becoming ubiquitous property of this world. From deep-ocean exploration to space …