Towards an enhanced driver situation awareness system

This paper outlines our current research agenda to achieve enhanced driver situation awareness. A novel approach that incorporates information gathered from sensors mounted on the neighboring vehicles, in the road infrastructure as well as onboard sensory information is proposed. A solution to the fundamental issue of registering data into a common reference frame when the relative locations of the sensors themselves are changing is outlined. A description of the vehicle test bed, experimental results from information gathered from various onboard sensors, and preliminary results from the sensor registration algorithm are presented. ©2007 IEEE

Realistic following behaviors for crowd simulation

International audienceWhile walking through a crowd, a pedestrian experiences a large number of interactions with his neighbors. The nature of these interactions is varied, and it has been observed that macroscopic phenomena emerge from the combination of these local interactions. Crowd models have hitherto considered collision avoidance as the unique type of interactions between individuals, few have considered walking in groups. By contrast, our paper focuses on interactions due to the following behaviors of pedestrians. Following is frequently observed when people walk in corridors or when they queue. Typical macroscopic stop-and-go waves emerge under such traffic conditions. Our contributions are, first, an experimental study on following behaviors, second, a numerical model for simulating such interactions, and third, its calibration, evaluation and applications. Through an experimental approach, we elaborate and calibrate a model from microscopic analysis of real kinematics data collected during experiments. We carefully evaluate our model both at the microscopic and the macroscopic levels. We also demonstrate our approach on applications where following interactions are prominent

The IRIS Network of Excellence:: Integrating Research in Interactive Storytelling

Abstract. Interactive Storytelling is a major endeavour to develop new media which could offer a radically new user experience, with a potential to revolutionise digital entertainment. European research in Interactive Storytelling has played a leading role in the development of the field, and this creates a unique opportunity to strengthen its position even further by structuring collaboration between some of its main actors. IRIS (Integrating Research in Interactive Storytelling) aims at creating a virtual centre of excellence that will be able to progress the understanding of fundamental aspects of Interactive Storytelling and the development of corresponding technologies

The orchestration of behaviours using resources and priority levels

Reproducing daily behaviours requires the ability to schedule behaviours depending on resources (body parts for example) and priority (intentions or physiological parameters) constraints. A simple way is to say that behaviours which are using the same resources are mutually exclusive. This approach is not sufficient to achieve realism purpose, as in real life, humans are able to combine them in a much microscopic way. All day long, humans mix different behaviours, as for example reading a newspaper while drinking a coffee and smoking a cigarette. If all behaviours using common resources were mutually exclusive, an agent could not reproduce this example, except if a specific behaviour is created. This solution becomes rapidly too complex and has motivated the work presented in this paper. It consists in an extension of HPTS, our behavioural model, by the introduction of resources and priority levels. In the contrary of some previous approaches, it is not necessary to specify exhaustively all behaviours that are mutually exclusive; this is done implicitely by attaching resources to nodes and a priority function to each state machine, and by using a scheduler