International audienceMost recent crowd simulation algorithms equip agents with a synthetic vision component for steering. They offer promising perspectives through a more realistic simulation of the way humans navigate according to their perception of the surrounding environment. In this paper, we propose a new perception/motion loop to steering agents along collision free trajectories that significantly improves the quality of vision-based crowd simulators. In contrast with solutions where agents avoid collisions in a purely reactive (binary) way, we suggest exploring the full range of possible adaptations and retaining the locally optimal one. To this end, we introduce a cost function, based on perceptual variables, which estimates an agent's situation considering both the risks of future collision and a desired destination. We then compute the partial derivatives of that function with respect to all possible motion adaptations. The agent then adapts its motion by following the gradient. This paper has thus two main contributions: the definition of a general purpose control scheme for steering synthetic vision-based agents; and the proposition of cost functions for evaluating the perceived danger of the current situation. We demonstrate improvements in several cases

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Computer simulation based interactive systems have proven to be risk-free alternatives for training the police force. Also, in these kinds of simulations, the user can make errors without dangerous consequences, unlike real-world situations. In this sense, this paper proposes a system model, aimed at shooting training, using features of video sequences and three-dimensional computer graphics interactive environments. This proposal is initially based on the evaluation of a commercial system that uses immersive virtual reality. Then, it was defined a system model, based on which, two prototypes were developed. These prototypes are already in use in a police academy, and make use of audiovisual devices and laser shots emission.