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 …

While the design of autonomous robots often emphasizes developing proficient robots,
another important attribute of autonomous robot systems is their ability to evaluate their own …

Given the increasing prevalence of intelligent systems capable of autonomous actions or
augmenting human activities, it is important to consider scenarios in which the human …

The Multi-Agent Path Finding (MAPF) problem, ie, the scheduling of multiple agents to reach
their destinations, has been widely investigated. Testing MAPF systems is challenging, due …

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 …

When humans and autonomous systems operate together as what we refer to as a hybrid
team, we of course wish to ensure the team operates successfully and effectively. We refer to …

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 …

Machine Learning (ML) models, such as deep neural networks, are widely applied in
autonomous systems to perform complex perception tasks. New dependability challenges …

With increasing progress in autonomous driving, the human does not have to be in control of
the vehicle for the entire drive. A human driver obtains the control of the vehicle in case of an …

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 …