Miguel Leitão

Abstract
Driving simulators require extensive road environments, with roads correctly modeled and similar to those found in real world. The modeling of extensive road environments, with the specific characteristics required by driving simulators, may result in a long time consuming process. This paper presents a procedural method to the modeling of large road environments. The proposed method can produce a road network design to populate an empty terrain and produce all the related road environment models. The terrain model can also be edited to produce well-constructed road environments. The road and terrain models are optimized to interactive visualization in real time, applying all the stet-of-art techniques like the level of detail selection. The proposed method allows modeling large road environments, with the realism and quality required to the realization of experimental work in driving simulators.

Abstract
Virtual environments for driving simulation aimed to scientific purposes require three-dimensional models of realistic road networks. The generation of these networks according to the requirements, if done manually by road design specialists, results in a time consuming task. Procedural generation of road networks comes as a solution to this problem with the creation of complete road networks definition adequate to simulation. This paper proposes a method to automatically generate an optimized definition of very large roads network, in an integrated process, from the selection of nodes in a terrain area, to the network topological definition. The human supervisor can interact with this generation process at any stage, in order to obtain custom road networks definitions. The proposed method reduces the use of specialists for preparing large road networks definitions. These definitions are suitable to integrate into a broader process to create road environments, with different road types, appropriate to conducting scientific experiments in driving simulation.

Abstract
Adaptive Cruise Control (ACC) is a system that maintains driver-selected speed and headway to a preceding vehicle. The system presents some limitations that are, in part or totally, unknown to the users. Hence, many drivers exhibit a rudimentary mental model of the system and place excessive trust in the device. As a consequence, negative effects on road safety can easily occur. However, to date, many studies conducted on ACC have comprised participants who had never used ACC previously. Therefore, there is limited knowledge of how ACC affects the driving performance of experienced users of the system. To shed light on this point, twenty-six participants, divided into two groups (ACC users and non-users) drove twice in the simulated environment (once with the ACC and once manually). During both drives, the participants experienced a critical situation (stationary vehicle stopped in the cruising lane of the highway). The results show that negative behavioural adaptations to the ACC resulted from the usage of the system with regard to the critical situation: the risk of collision during the driving with ACC was increased compared with the manual driving for both groups of drivers. Besides, the research stresses the negative large correlation between the driver's mental model of ACC operation in the critical situation and the safety margins maintained by the ACC users during the same situation. Finally, it was found that the drivers' trust in the system does not have an influence on the drivers' behaviour during the trial with the ACC.

Abstract
Problem: The Adaptive Cruise Control is an Advanced Driver Assistance System (ADAS) that allows maintaining given headway and speed, according to settings pre-defined by the users. Despite the potential benefits associated to the utilization of ACC, previous studies warned against negative behavioral adaptations that might occur while driving with the system activated. Unfortunately, up to now, there are no unanimous results about the effects induced by the usage of ACC on speed and time headway to the vehicle in front. Also, few studies were performed including actual users of ACC among the subjects. Objectives: This research aimed to investigate the effect of the experience gained with ACC on speed and time headway for a group of users of the system. In addition, it explored the impact of ACC usage on speed and time headway for ACC users and regular drivers. Method: A matched sample driving simulator study was planned as a two-way (2 x 2) repeated measures mixed design, with the experience with ACC as between-subjects factor and the driving condition (with ACC and manually) as within-subjects factor. Results: The results show that the usage of ACC brought a small but not significant reduction of speed and, especially, the maintenance of safer time headways, being the latter result greater for ACC users, probably as a consequence of their experience in using the system. Summary: The usage of ACC did not cause any negative behavioral adaptations to the system regarding speed and time headway. Practical applications: Based on this research work, the Adaptive Cruise Control showed the potential to improve road safety for what concerns the speed and the time headway maintained by the drivers. The speed of the surrounding traffic and the minimum time headway settable through the ACC seem to have an important effect on the road safety improvement achievable with the system.

Abstract
This work is about return on investment (ROI) estimation based on a set of scenarios related with Cloud services adoption by Portuguese higher education (HE) institutions. The adopted methodology required the development of a survey and its distribution among institutions. The collected data allowed us to evaluate a set of indicators and metrics in order to design ROI models. With such models it was possible to estimate the cost benefit of Cloud Computing paradigm in the context of Portuguese HE infrastructures and services.