Areas Of Simulation 561a4v

Simulation Definition: The term "simulation" has, like the term "theory," come to be used broadly and in a variety of ways. Simulation is usually equated with roletaking, or imaginatively "putting oneself in the other's place." This metaphor is understood to embrace adoption of different spatial and temporal perspectives as well as other shifts in indexical specified situations (e.g., in social role, office, or kinship relations); and further, adoption of alternative character traits and similar exercises of dramatic impersonation. However, one may also conceive simulation as including simple "projection," without adjustments in imagination; e.g., where there is no need to put oneself in the other's place, as one is, in all relevant respects, already there: e.g., the tornado is approaching not just you or me but us.

Along with this person-level characterization of simulation, simulation is also conceived by most proponents in cognitive-scientific . It is assumed that in role-taking, one's own behavior control system is employed as a manipulability model of other such systems. The system is first taken off-line, so that the output is not actual behavior but only predictions or anticipations of behavior, and inputs and system parameters are accordingly not limited to those that would regulate one's own behavior. Although this sometimes results in vicarious decision-making, more typically it stops at the more modest goal of establishing which options would be attractive and which unattractive

Modern Areas of Simulation: 1.National Budget Simulation The new President of the United States has been elected on the promise of fiscal responsibility. He has promised the voters he will not raise taxes, and he will not reduce Social Security or Medicare. He has promised interest groups that he will not reduce Commerce

Suddenly, the United States is subject to military attack -- a turn of events not anticipated in the current budget. At the same time, a lingering recession reduces the government's tax revenues and forces the government to increase its spending on unemployment benefits, welfare, housing assistance, food stamps, and other need-based programs. Because of the increased spending and reduced revenues, the nation falls into a projected deficit of nearly $185 billion. Then Congress es legislation to increase military spending by 20 percent, to pay for increased security within the U.S. and to pay for a prolonged military response against the attacking country and other potential threats. The President signs this bill into law, increasing the projected deficit to nearly $254 billion. The President is committed to keeping his campaign promises, in order to maintain for his reelection. He must protect the programs he promised to protect, and he cannot raise taxes, so he must cut spending on other programs to stay within his new guideline to keep the deficit below $150 billion. The President turns to you, his trusted economic advisor, for help.

Process: To represent the 20 percent increase in military spending, the spending levels have automatically been changed. You can see how this affects the total spending at the bottom of the column. Scroll to the bottom of the page to see the effect of the increase in military spending on the "New Surplus" (a negative surplus is a deficit). that you need to get this figure below $150 billion. Make note of the relative amounts of the budget spent on each area listed in the table, so that you can decide where cuts might be effective to reduce the deficit. Now begin cutting the program budgets as a tradeoff for the increased defense spending. , for political reasons or by law, you cannot make any changes in these areas: Commerce and housing credit, Medicare, Social Security, Net interest, Allowances, and Undistributed offsetting receipts. You can click on the names of the spending areas to see the programs in the respective spending areas. Keep cutting programs until you have reached your $150 billion deficit limit. Hint: You will have to cut most programs by at least 10 percent to reach your target. When cutting programs, keep in mind that program cuts could seriously affect citizens’ daily lives. Also keep in mind people who may be so angered by program cuts that they will take action to prevent the President’s reelection. When you have reached your target, print out your results. Consider which programs you have cut, to help you answer the reflection questions on the Worksheet. When you are finished with the lesson, hand in the paper that you printed along with this worksheet.

Assessment Activity: You should now write an explanation of the decisions that you made and the justifications for those decisions. If you have individually completed the activity, with your classmates you can discuss the decisions and justifications.

Also hand in the printout of the "Your New Budget" page as a way to assess your decision-making skills in this activity.

2. Simulation and Measurement of Driver and Vehicle Performance: This paper gives a brief review of the state of the art and future potential in technical areas of interest to the Committee on Simulation and Measurement of Driver and Vehicle Performance. These technical areas are associated with vehicles and vehicle operators and include simulation, modeling, measurement, and instrumentation. Technology in the core areas of electronics, computation, processing, and sensors has been advancing, and costs have been declining rather dramatically in the last decade, and this trend shows no sign of abating in the near future. These technology trends have, in turn, dramatically increased the capability and decreased the cost of applications in simulation and instrumented vehicles. Increased capability of desktop computers and workstations has also permitted a significant increase in the amount and detail of computer modeling and data processing that can be undertaken. This paper will summarize various applications and their future trends as we enter the new millennium. SIMULATION

National Advanced Driving Simulator (NADS) The National Highway Traffic Safety istration (NHTSA) is using high-end technology to develop a driving simulator that will rival the most sophisticated aerospace device and that will represent the premier simulator application in the next decade. NADS will advance highway safety through a better understanding of the complex interaction among the driver, the vehicle, and the roadway environment, particularly during impending crash situations. Rather than using expensive test tracks, trained test drivers, and potentially expensive test vehicles, NHTSA will provide itself, academia, and industry researchers with a national facility to conduct studies using drivers from the general public riding in real vehicles in a virtual driving environment. To do this, the NADS will provide accurate, high-fidelity, correlated driving cues to immerse participants in a realistic driving environment. Subjects will drive the cabs of real vehicles selected from four typical vehicle types currently in production: a large family sedan, a sport-utility vehicle, a small family sedan, and a heavy truck. NADS is nearing completion and is expected to be deployed in mid-2000 at a facility located at the University of Iowa. An artist’s conception of the current NADS configuration is illustrated in Figure 1. This motion will be complemented by correlated 360-degree visual and audio cues, also under computer control. The photorealistic visual scenes provided by a high-end Evans and Sutherland image generator will include moving vehicle and pedestrians to complete the driver’s perception of being immersed in urban and rural traffic situations. The audio system will provide appropriate sounds

internal and external the cab, including Doppler and side-to-side directional effects. The design of NADS allows for a wide range of potential applications, including new cockpit intelligent vehicle systems (ITS) technology, control and instrument layout, vehicle control systems, driving while impaired, and problems with novice and elderly drivers. NADS virtual driving experience is intended to be a complete sensory environment that allows drivers to be immersed in realistic tasks under real-world motivations. The simulation environment will permit roadway hazards and traffic conflict situations to be

FIGURE 1 Artistes conception of the National Advanced Driving Simulator. Presented that are impractical to control on test tracks or public roads but can be experienced in the NADS without safety consequences in the event of accidents.

Moderate- to Low-Cost Simulation A range of driving simulations are based on silicon graphics and high-end PC technology (1,2). The graphics capabilities of these systems have increased dramatically in the last decade, permitting visually complex scenes including texture. Most of these devices have a fixed base and include relatively restricted fields of view, although virtual reality head- mounted displays allow for a low-cost wide field of view. Relatively low-cost electromechanical sixdegree-of-freedom limited-motion systems are now available that allow for moderately priced moving-base simulations (3). New graphics accelerator cards for PCs permit the deployment of quite low-cost aeronautical and driving simulations with very realistic visual displays. This technology has been used for simulations of parachute handling and table top driving (4), and for animation and visualization systems to illustrate proposed project designs (5). PC-based systems are capable of presenting relatively high-fidelity visual, auditory, and control-feel sensory to the operator. Continued technology improvement and decreasing costs are anticipated over the next

decade. As capability increases and costs decline, increased use of simulators is projected for applications such as training and licensing of novice and professional vehicle operators.

VISUALIZATION Given increasing capabilities and decreasing costs of three-dimensional (3D) computer graphics, visualization is now commonly used in a number of fields to review designs and proposed developments, portray aeronautical and highway traffic flow, and reconstruct accidents, as well as other applications. At the recent TRB 3D in Transportation Symposium and Workshop (6), several trends in visualization and animation were apparent. First, the use of moderate to low-cost PC platforms is increasing. A second trend is development of simplified 3D visual database modeling procedures that are reducing the effort required to produce visualizations. A third trend is the ability to move through models in real time so that viewers can determine their own trajectory and point of view in reviewing proposed designs and developments. This last development is akin to real-time simulation, as discussed earlier, and portends the merging of visualization and simulation technology.

3.DRIVER AND VEHICLE MODELING Interactive Highway Safety Design Model (IHSDM) FHWA has undertaken a multiyear project to develop IHSDM, which is a set of software tools, to analyze candidate highway geometric designs from a safety standpoint (15,16). IHSDM will include a computational driver-vehicle model that will simulate the moment-

FIGURE 2 Instrumented vehicles with eye movement measurement. Vehicle operator modeling has been and will be a matter of continuing interest in regard to safety, performance, and comfort and convenience. Building on several decades of modeling development, ideas such as optimal control and preview (prediction) were introduced and have been discussed by Levisohn (17,21), and these ideas have been incorporated into IHSDM driver model. In

1998 this committee sponsored a session at TRB annual meeting that resulted in six papers covering areas such as driver-vehicle system performance in the longitudinal control of headway range, an interactive highway safety design model, driver mental work load, visual information processing, and human movement and posture (22). Two years before that, the committee sponsored a session in which driver modeling in general, as well as microscopic aspects of traffic flow, were discussed. [A compendium of traffic flow information and modeling was published in 1991 (23).] Recently, researchers in cognitive psychology have combined cognitive behavior models with a perception and motion model to produce a simulation known as ACT-R/PM (24). Significant strides have also been made in kinematics and biodynamic modeling, which is useful for the design of work spaces for ride- and crashworthiness (25).

4. Simulation of Traffic Systems: In general, simulation is defined as dynamic representation of some part of the real world achieved by building a computer model and moving it through time. Computer models are widely used in traffic and transportation system analysis, but only those with dynamic approach are in the focus of this paper. The use of computer simulation started when D.L. Gerlough published his dissertation: "Simulation of freeway traffic on a general-purpose discrete variable computer" at the University of California, Los Angeles, in 1955. The five driving forces behind this development are the advances in traffic theory, in computer hardware technology and in programming tools, the development of the general information infrastructure, and the society's demand for more detailed analysis of the consequences of traffic measures and plans. An example demonstrating the great advances in hardware and software technology is presented in

Figure 1 Graphic presentation of simulation results in late 60's

Traffic as a simulation object

Road transportation, that is, efficient movement of people and goods through physical road and street networks is a fascinating problem. Traffic systems are characterized by a number of features that make them hard to analyze, control and optimize. The systems often cover wide physical areas, the number of active participants is high, the goals and objectives of the participants are not necessarily parallel with each other or with those of the system operator (system optimum vs. optimum), and there are many system inputs that are outside the control of the operator and the participants.. Transportation systems are typical man-machine systems, that is, the activities in the system include both human interaction and man-machine-interactions In addition, the laws of interaction are approximate in nature; the observations and reactions of drivers are governed by human perception and not by technology based sensor and monitoring systems(Figure2).

Figure 2a Basic Driver Perception- Figure 2b The Vehicle Object's action Process (Häkkinen and Luoma Interactions in a Simulation System 1991). (Kosonen 1996). In all, traffic systems are an excellent application environment for simulation based research and planning techniques, an application area where the use of analytical tools, though very important, is limited to subsystem and sub problem level. The reasons to use simulation in the field of traffic are the same as in all simulation; the problems in analytical solving of the question at hand, the need to test, evaluate and demonstrate a proposed course of action implementation, to make research (to learn) and to train people.

Areas and approaches in traffic simulation The applications of traffic simulation programs can be classified in several ways. Some basic classifications are the division between microscopic, macroscopic and macroscopic, and between continuous and discrete time

approach. According to the problem area we can separate intersection, road section and network simulations. Special areas are traffic safety and the effects of advanced traffic information and control systems. A newly emerged area is that of demand estimation through microscopic simulation. Most traffic system simulation applications today are based on the simulation of vehicle-vehicle interactions and are microscopic in nature. Traffic flow analysis is one of the few areas, where macroscopic simulation has also been in use.

Traffic safety related questions have been quite a hard problem for simulation. In traditional simulation programs the drivers are programmed to avoid collisions. Thus, they do not exist. Some trials for analysis of conflict situations through simulation can be found, but a general approach to the problem and widely used safety simulation tools are still missing. Traffic safety simulation belongs to the field of human centered simulation where the perceptionreaction system of drivers with all its weak points has to be described.

Trends in traffic simulation The development in traffic simulation from the early days in the 1950's and 1960's has been tremendous. This, of course, is partly related to the development of computer technology and programming tools. On the other hand, the research in traffic and transportation engineering has also advanced during this 40-year period. Simulation is now an everyday tool for practitioners and researchers in all fields of the profession.. The applications are growing in size, that is, we are moving from the quite well covered local or one facility type applications to network wide systems where several types of facilities are integrated in one system. Another trend that increases the need of computing power is the more and more precise description of the physical road and street environment, especially in local applications, like in simulation of intersections. In both these cases the use of graphic interfaces and integration to GIS and CAD systems TRANSIMS is an example of still another change in the approach. The traditional traffic flow descriptions are based on continuous speed and distance variables. TRANSIMS, in turn, uses a discrete approach where the road and street network is build from elements that can accommodate only one vehicle at a time unit. In this cellular automata approach the vehicles move by "jumping" from the present element to a new one according to rules that describe the driver behavior and maintain the basic laws of physics at present in vehicle movements (Figure 3).

Figure 3 Principle of a Cellular Automaton. Virtual reality systems and programming tools become in common use, especially in simulations where the driver reactions and behavior must be analyzed in great detail. Traffic safety related simulation will therefore probably be an area that greatly benefits from VR technology. There is, of course, no reason why VR tools could not be used in more traditional simulation tasks, as well. In planning applications VR gives new possibilities for the planning work and for the demonstration of plans to decision-makers and public.

Figure 4 A Proposals for an Open Traffic Modeling Environment The combination of traditional driving simulators and traditional traffic flow simulation systems becomes possible through virtual reality techniques. In traditional driving simulator the test driver has to react to the fixed traffic that he/she sees on the display. A more natural situation is achieved if the traffic also reacts to the test driver behavior, that is, the vehicle with the test driver comes an interactive part of the simulated traffic flow. The simulation of travel demand will grow up rapidly. The basic research in time-use studies and trip chaining of individuals combined with disaggregate modeling form a theoretical basis for this new methodology. Demand simulation will also use GIS databases and tools for basic data input and demonstration of the results. The simulation approach will be useful not only in

the analysis of peak hour traffic in congested urban areas but also in the planning of special low demand transport services  like  demand  responsive  public transport.

5.Manufacturing by Computer Simulation Recent advances in factory simulation are pushing the technology beyond its core use for modeling automation to also provide help in areas ranging from training and product design to warehouse management and supply chain planning. The role of simulation in manufacturing has expanded in recent years. Some manufacturers are using simulation to make business decisions, since the production numbers in the simulation have become more reliable. One company even used simulation to close a sale with General Motors Corp. “The biggest simulation we’ve done has been a feasibility study for GM,” says Murray Fulmer, corporate simulation specialist at Flex-N-Gate, an automotive supplier in Urbana, Ill. “We had to build GM’s entire Oklahoma Ultra Paint system and process every part that’s currently painted there.” The challenge for Flex-N-Gate was to double the line speed GM was using to apply paint to plastic parts, a specialty at Flex-N-Paint. GM wanted to award the contract to Flex-N-Gate, but the automaker wasn’t convinced the supplier could handle the increased line speed. “We had to prove that the robot controller could do the job,” says Fulmer. “The simulation showed the paint being applied, and it showed the robot’s speed. It wasn’t just math. The line was moving at the speed that we said we could move.”

6. Areas of Empirical Investigation: Four main areas of empirical investigation have been thought especially relevant to the debate: •

False belief. Taking into another's ignorance or false belief when predicting or explaining their behavior requires imaginative modifications of one's own beliefs, according to the simulation theory. Thus the theory offers an explanation of the results of numerous experiments showing that younger children fail to take such factors into . It would also explain the correlation, in autism, of failure to take into ignorance or false belief and failure to engage in spontaneous pretend-play, particularly role play. Although these results can also be explained by certain versions of theory, the simulation theory offers a new interpretation

•

Priority of self- or other-ascription. A second area of developmental research asks whether children ascribe mental states to themselves before they ascribe them to others. Versions of the simulation theory committed to the view that we recognize our own mental states as such and make analogical inferences to others' mental states seem to require an affirmative answer to this question; other versions of the theory seem to require a negative answer. Some experiments suggest a negative answer, but debate continues on this question.

•

Neural Simulation. For most versions of the simulation theory, a relevant empirical question, perhaps even the crucial question, is whether the neural mechanisms and processes employed in understanding and anticipating others' responses to the world significantly resemble those called on in our own "first person" responses to the world. There is now converging evidence that the human brain has systems that do double duty of the following kind: they may be activated either endogenously. For example, the visceral responses characteristic of various emotions — the internal changes that give rise to the corresponding "gut feelings" — normally occur as the output of the processing of emotional stimuli. However, the same responses are also elicited when another's face is seen expressing the corresponding emotion.

•

Cognitive impenetrability. Stitch and Nichols suppose simulation to be "cognitively impenetrable" in that it operates independently of any general knowledge the simulator may have about human psychology. Yet they point to results suggesting that when subjects lack certain psychological information, they sometimes make incorrect predictions, and therefore must not be simulating .Because of problems of methodology and interpretation, as noted by a number of philosophers and psychologists, the cogency of this line of criticism is unclear.

Some philosophers think the simulation theory may shed light on issues in traditional philosophy of mind and language concerning intentionality, referential opacity, broad and narrow content, the nature of mental causation, Twin Earth problems, the problem of other minds, and the peculiarities of selfknowledge. Several philosophers have applied the theory to aesthetics, ethics, and philosophy of the social sciences. Success or failure of these efforts to answer philosophical problems may be considered empirical tests of the theory, in a suitably broad sense of "empirical."

Main Advantages and Disadvantages of Simulation: Main advantages of simulation include: 1. Study the behavior of a system without building it. 2. Results are accurate in general, compared to analytical model. 3. Help to find un-expected phenomenon, behavior of the system. 4. Easy to perform ``What-If'' analysis. Main disadvantages of simulation include: 1. Expensive to build a simulation model. 2. Expensive to conduct simulation. 3. Sometimes it is difficult to interpret the simulation results.

Another Advantage of Computer Simulation: 1. The apparatus necessary to be able to carry out an experiment in reality is too some advantages of computer simulation as an educational tool or for training are expensive and often specialists can only operate this apparatus, if it can be obtained at all. In some vocational training courses e.g. the subject 'robotics' is taught in which attention is paid to the functions and use of a robot. Not every training department however, can afford to buy a robot. But a computer simulation program can imitate the behavior of a robot. The student or trainee can now exercise as much as necessary. After sufficient exercise the student or trainee may be given the opportunity to handle a real robot in an actual setting. Owing to the practice beforehand precious time and apparatus can be put to optimum use. 2.The process to be investigated takes place so quickly in reality that it cannot be examined through the traditional experiment, e.g. certain chemical processes. Changes in a chemical reaction should be presented at such a pace in educational situations that observation is possible. In reality those changes can hardly be noticed and they are not interesting for calculations, but only for the acquisition of insight. 3.The process to be examined can proceed too slowly in reality, e.g. biological growing processes. 4.The system to be examined can be too complex for traditional research, e.g. economical systems. 5.The system to be examined can be on too large a scale, e.g. planetary movements in space. 6.The system to be examined can be too small, e.g. molecular movements.

7. The system to be examined can be dangerous to manipulate, e.g. a nuclear reactor, a ship or a human body. 8.It can be irresponsible from an ethical point of view to do research through traditional experiments as e.g. with certain diseases. 9. Simulation experiments can be used prior to a course for students or trainees as an introduction to a new subject or certain parts of it. 10. Simulation often goes hand in hand with visualization. The results of changes that a student puts into a model are directly shown on the screen. This generally appeals to students. 11. Simulation can be very purposive and for certain students very useful, such as students who need some insight before they are able to learn and understand a new concept. 12. The student can insert those parameter values that he or she thinks will produce a result, which is of interest to him. The student can devote his attention to parts that interest him. The student can skip other parts or aspects. This way he or she learns how to experiment systematically. 13. A student can choose how he or she wants to approach a simulation experiment, how often he or she wants to repeat the experiment and to which degree he or she wants to intervene. In computer simulation there are usually many ways to achieve the goals the student has set himself. 14. If well designed, learning how to operate a computer simulation program generally requires little effort. A short introduction by the teacher is often sufficient to enable the student to work with the program. 15. It can be an advantage that the student perceives that not everything can be used as input. The student realizes that variables and parameters have their limits, and learns what input is reasonable for a particular variable and what input yields relevant information.

Another Disadvantage of Computer Simulation: There are not only advantages connected with the use of computer simulation programs in education and training. Limitations are in some cases the result of the wrong or inappropriate use of such programs. Possible limitations of a general and educational kind are: 1. Simulation concerns the manipulation of a number of variables of a model representing a real system. However, manipulation of a single variable often means that the reality of the system as a whole can be lost. Certain systems or components of a realistic situation are not transparent. Some factors have a lot of influence on the whole, but they have indistinct relations in the whole and can therefore not be represented in a model. These factors, however, cannot be forgotten in the learning process. 2. A computer simulation program cannot develop the students' emotional and intuitive awareness that the use of simulations is specifically directed at establishing relations between variables in a model. So this intuition has to be developed in a different way. 3. Computer simulation cannot react to unexpected 'sub-goals’, which the student may develop during a learning-process. These sub-goals would be brought up during a teacher-student interaction but they remain unsaid during the individual student use of a simulation. 4. Computer simulation programs may function well from a technical point of view, but they are difficult to fit into a curriculum. 5. Often a computer simulation program cannot be adapted to take into different student levels into within a group or class. A computer simulation program can certainly be made to adapt to different circumstances if the designer bears that in mind; however, for many computer simulation programs this has not happened. 6. During the experience of interaction with a computer simulation program, the student is frequently asked to solve problems in which creativity is often the decisive factor to success. The fact that this creativity is more present in some pupils than in others is not taken into by the simulation. Mutual collaboration and discussion among students while using the software could be a solution for this.

REFERENCES: 1. Burnette, C., and S. Moon. Developing Highway Driving Simulations Using the Virtual Reality Modeling Language (VMRL). In Transportation Research Record, TRB, National Research Council, Washington, D.C., to be published (1999). 2. Klee, H., C. Bauer, A. E. Radwan, and H. M. Al-Deek. Experimental Validation of a Driving Simulator. In Transportation Research Record, TRB, National Research Council, Washington, D.C., to be published (1999). 3. Hogue, J. R., R. W. Allen, T. J. Rosenthal, et al. Applying Low-Cost Virtual Environments to Simulation-Based Vehicle Operator Training. Presented at Simulation Technology and Training Conference (SimTecT 99), Melbourne, Australia, 1999 (also Systems Technology, Inc. Paper 549). 4. Adolphs, R. et al., 2000, "A Role for Somatosensory Cortices in the Visual Recognition of Emotion as Revealed by Three-Dimensional Lesion Mapping," Journal of Neuroscience 20 (7), 2683-2690. 5. Gallese, V., 2001, "The ‘shared manifold’ hypothesis: from mirror neurons to empathy," Journal of Consciousness Studies, 8, 33-50. 6. Gallese, V., & Goldman, A., 1998, "Mirror neurons and the simulation theory of mind-reading," Trends in Cognitive Sciences, 2, 493-501. 7.Nagel, K., and Schleicher, A. (1994) Microscopic traffic modelling on parallel  high performance computers. Parallel Computing, 20, 125­146. 8.Payne, H. (1971) Models of freeway traffic and control. Mathematical Models  of Public Systems. Simulation Council Proceedings Series, vol. 1, no 1, 51­61. 9.Rekersbrink, A. (1995) Mikroskopische Verkehrssimulation mit Hilfe der  Fuzzy­logic. Strassenverkehrstechnik 2/95, 68­74.