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
The increasing number of vehicles and mobile users has led to a huge increase in the development of Advanced Driver Assistance Systems (ADAS). In this paper we propose a multi-agent-based driving simulator which integrates a testbed that allows ADAS developers to compress testing time and carry out tests in a controlled environment while using a low-cost setup. We use the SUMO microscopic simulator and a serious-game-based driving simulator which has geodata provided from standard open sources. This simulator connects to an Android device and sends data such as the current GPS coordinates and transportation network data. One important feature of this application is that it allows ADAS validation without the need of field testing. Also important is the suitability of our architecture to serve as an appropriate means to conduct behaviour elicitation through peer-designed agents, as well as to collect performance measures related to drivers' interaction with ADAS solutions.

Abstract
We propose a new paradigm for public participation in urban planning, a field which presents significant challenges for public understanding and participation. Our approach is based on leveraging the rich diversity of meaning associated with cultural gestures, traditions, folklore, and rituals, and using them in augmented reality systems, in order for citizens' to explore, understand, and communicate the complex, systemic ideas and concepts associated with urban planning. At an immediate level, this approach holds the potential for enabling increased public awareness of what is at stake in urban planning - both on the part of citizens and on the part of public officials, policy-makers, and decision-makers - and consequently enhancing understanding and improving participation in public life and citizenship. It may also open up a new field of research and development in human-computer interaction, to leverage the richness of meaning and modes of expression which exist in various cultures and societies, rather than ignoring them and imposing dumbed-down or prescribed command methods. Thus, it aims to facilitate new levels of empowerment of users in the use of digital systems and data. The active utilization of cultural meaning in gestures, rituals, and social practices may also support and promote better inclusion and participation of minority groups and migrant communities in contemporary, technology-rich life. (c) 2015 The Authors. Published by Elsevier B.V.

Abstract
Recently, Serious Games (SG) achieved a recognized position as a learning tool in several contexts. SG provide constructive learning environments in which errors can be made without real life penalties and where students get instant feedback from their actions when facing challenges. These challenges should be in accordance with the intended learning goals and they should adapt and/or be repeated according to the learner's level. This aspect is decisive in the acquisition of knowledge, experience and professional skills through the simulation of different situations and contexts. The effectiveness of competences' training is directly related to the success in their acquisition but, above all, it is related to the ability to apply them to successfully perform a given task. However, an optimal game design methodology for competence training is yet to be created. This article presents a study that identifies the most appropriate game categories to develop specific skills and competences. It considers a taxonomy with eight game categories (Action, Strategy, Playing, Sports, Management Simulation, Adventure, Puzzle and Quiz) that were matched with the Education Competences and Educational Competency Wheel. Analysing 116 serious games allowed identifying which categories were more efficient in the training of a specific competence and therefore should be reused in the same scope.

Abstract
Massive procedural content creation, for example, for virtual urban environments, is a difficult, yet important challenge. While shape grammars are a popular example of effectiveness in architectural modeling, they have clear limitations regarding readability, manageability, and expressive power when addressing a variety of complex structural designs. Moreover, shape grammars aim at geometry specification and do not facilitate integration with other types of content, such as textures or light sources, which could rather accompany the generation process. We present procedural content graphs, a graph-based solution for procedural generation that addresses all these issues in a visual, flexible, and more expressive manner. Besides integrating handling of diverse types of content, this approach introduces collective entity manipulation as lists, seamlessly providing features such as advanced filtering, grouping, merging, ordering, and aggregation, essentially unavailable in shape grammars. Hereby, separated entities can be easily merged or just analyzed together in order to perform a variety of context-based decisions and operations. The advantages of this approach are illustrated via examples of tasks that are either very cumbersome or simply impossible to express with previous grammar approaches.

Abstract
Virtual environments for driving simulations aimed to scientific purposes require three-dimensional road models that must obey to detailed standards of specification and realism. The creation of road models with this level of quality requires previous definition of the road networks and the road paths. Each road path is usually obtained through the dedicated work of roadway design specialists, resulting in a long time consuming process. The driving simulation for scientific purposes also requires a semantic description of all elements within the environment in order to provide the parameterization of actors during the simulation and the production of simulation reports. This paper presents a methodology to automatically generate road environments suitable to the implementation of driving simulation experiences. This methodology integrates every required step for modelling road environments, from the determination of interchanges nodes to the generation of the geometric and the semantic models. The human supervisor can interact with the model generation process at any stage, in order to meet every specific requirement of the experimental work. The proposed methodology reduces workload involved in the initial specification of the road network and significantly reduces the use of specialists for preparing the road paths of all roadways. The generated semantic description allows procedural placing of actors in the simulated environment. The models are suitable for conducting scientific work in a driving simulator. Copyright