In this paper, we introduce the problem of enroute destination changing for a self-driving car, and we study the effectiveness of human-robot copilot systems as a solution. The copilot system is one in which the autonomous vehicle not only handles low-level vehicle control, but also continually monitors the intent of the human passenger in order to respond to dynamic changes in desired destination. We specifically consider a vehicle parking task, where the vehicle must respond to the user's intent to drive to and park next to a particular roadside sign board, and we study a copilot system that detects the passenger's intended destination based on gaze. We conduct a human study to investigate, in the context of our parking task, (a) if there is benefit in using a copilot system over manual driving, and (b) if copilot systems that use eye tracking to detect the intended destination have any benefit compared to those that use a more traditional, keyboard-based system. We find that the answers to both of these questions are affirmative: our copilot systems can complete the autonomous parking task more efficiently than human drivers can, and our copilot system that utilizes gaze information enjoys an increased success rate over one that utilizes typed input.

In Distributed Control Systems (DCS) the distribution of sensor data needs to be optimized in order to reduce the client process redundancy, i.e.: same sensor data processing in different clients. This paper presents an implementation of Embedded Networked Sensors as Smart Resources that allows to access to high-level information abstracting the user from low level mechanisms. The distributed services provided by Smart Resources implement self-aware adaptation mechanisms in order to suit the performance to the context. Information integrity must be also ensured during this adaptation. The Smart Resource implementation is based on a RGBD sensor. Through a set of experiments is evaluated the performance of the reconfiguration and adaptation mechanisms and detailed the influence of the data integrity according to the provided service.