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Traffic simulations for virtual environments are concerned with the behavior of individual traffic participants. The complexity of behavior in these simulations is often rather simple to abide by the constraints of processing resources. In sophisticated traffic simulations, the behavior of individual traffic participants is also modeled, but the focus lies on the overall behavior of the entire system, e.g. to identify possible bottle necks of traffic flow [8].
Traffic simulations are typically concerned with modeling human behavior as closely as possible to create realistic results. In conventional traffic simulations used for road planning or traffic jam prediction only the overall behavior of an entire system is of interest. In virtual environments, like digital games, simulated traffic participants are merely a backdrop to the player’s experience and only need to be “sufficiently realistic”. Additionally, restricted computational resources, typical for virtual environment applications, usually limit the complexity of simulated behavior in this field. More importantly, two integral aspects of real-world traffic are not considered in current traffic simulations from both fields: misbehavior and risk taking of traffic participants. However, for certain applications like the FIVIS bicycle simulator, these aspects are essential.
Traditionally traffic simulations are used to predict traffic jams, plan new roads or highways, and estimate road safety. They are also used in computer games and virtual environments. There are two general concepts of modeling traffic: macroscopic and microscopic modeling. Macroscopic traffic models take vehicle collectives into account and do not consider individual vehicles. Parameters like average velocity and density are used to model the flow of traffic. In contrast, microscopic traffic models consider each vehicle individually. Therefore, vehicle specific parameters are of importance, e.g. current velocity, desired velocity, velocity difference to the lead vehicle, individual time gap.
We present our approach to extend a Virtual Reality software framework towards the use for Augmented Reality applications. Although VR and AR applications have very similar requirements in terms of abstract components (like 6DOF input, stereoscopic output, simulation engines), the requirements in terms of hardware and software vary considerably. In this article we would like to share the experience gained from adapting our VR software framework for AR applications. We will address design issues for this task. The result is a VR/AR basic software that allows us to implement interactive applications without fixing their type (VR or AR) beforehand. Switching from VR to AR is a matter of changing the configuration file of the application. We also give an example of the use of the extended framework: Augmenting the magnetic field of bar magnets in physics classes. We describe the setup of the system and the real-time calculation of the magnetic field, using a GPU.
Using virtual environment systems for road safety education requires a realistic simulation of road traffic. Current traffic simulations are either too restricted in their complexity of agent behavior or focus on aspects not important in virtual environments. More importantly, none of them are concerned with modeling misbehavior of traffic participants which is part of every-day traffic and should therefore not be neglected in this context. We present a concept for a traffic simulation that addresses the need for more realistic agent behavior with regard to road safety education. The two major components of this concept are a simulation of persistent agents which minimizes computational overhead and a model of cognitive processes of human drivers combined with psychological personality profiles to allow for individual behavior and misbehavior.
This paper compares the memory allocation of two Java virtual machines, namely Oracle Java HotSpot VM 32-bit (OJVM) and Jamaica JamaicaVM (JJVM). The basic difference of the architectures in both machines is that the JamaicaVM uses fixed-size blocks for allocating objects on the heap. The basic difference of the architectures is that the JJVM uses fixed size block allocation on the heap. This means that objects have to be split into several connected blocks if they are bigger than the specified block-size. On the other hand, for small objects a full block must be allocated. The paper contains both theoretical and experimental analysis on the memory-overhead. The theoretical analysis is based on specifications of the two virtual machines. The experimental analysis is done with a modified JVMTI Agent together with the SPECjvm2008 Benchmark.