Abstract: A computing system enhances the human-like realism of computer opponents in racing-type games and other motion-related games. The computing system observes multiple prescribed motion lines and computes switching probabilities attributed to switching of simulated motion of a racer from one prescribed motion line to another. A sampling module samples at random over the switching probabilities to select one of the switching probabilities. At least one control signal is generated to switch simulated motion of the entity in a virtual reality environment from the first prescribed motion line to one of the other prescribed motion lines, in accordance with the selected one of the switching probabilities.

Abstract: An automatic algorithm for finding racing lines via computerized minimization of a measure of the curvature of a racing line is derived. Maximum sustainable speed of a car on a track is shown to be inversely proportional to the curvature of the line it is attempting to follow. Low curvature allows for higher speed given that a car has some maximum lateral traction when cornering. The racing line can also be constrained, or “pinned,” at arbitrary points on the track. Pinning may be performed randomly, deterministically, or manually and allows, for example, a line designer to pin the line at any chosen points on the track, such that when the automatic algorithm is run, it will produce the smoothest line that still passes through all the specified pins.

Abstract: A target speed profile for a specified racer is computed at various points along a track. The calculation is based on the real world physics of the racing environment and incorporates physical characteristics of the track, including curvature, undulation, and/or camber. A lateral acceleration component is developed to limit the realistic maximum speed a racer may obtain at any given point along the track. Furthermore, differences in realistic maximum speeds at different points along the track can overwhelm a racer's braking capability. As such, braking capacity adjustments can be applied to decrease the maximum speed in the target speed profile, so that the overall target speed profile is more realistic and attainable.

Abstract: Improved human-like realism of computer opponents in racing or motion-related games is provided by using a mixture model to determine a dynamically prescribed racing line that the AI driver is to follow for a given segment of the race track. This dynamically prescribed racing line may vary from segment to segment and lap to lap, roughly following an ideal line with some variation. As such, the AI driver does not appear to statically follow the ideal line perfectly throughout the race. Instead, within each segment of the course, the AI driver's path may smoothly follow a probabilistically-determined racing line defined relative to at least one prescribed racing line.

Abstract: An automatic algorithm for finding racing lines via computerized minimization of a measure of the curvature of a racing line is derived. Maximum sustainable speed of a car on a track is shown to be inversely proportional to the curvature of the line it is attempting to follow. Low curvature allows for higher speed given that a car has some maximum lateral traction when cornering. The racing line can also be constrained, or “pinned,” at arbitrary points on the track. Pinning may be performed randomly, deterministically, or manually and allows, for example, a line designer to pin the line at any chosen points on the track, such that when the automatic algorithm is run, it will produce the smoothest line that still passes through all the specified pins.

Abstract: An automatic algorithm for finding racing lines via computerized minimization of a measure of the curvature of a racing line is derived. Maximum sustainable speed of a car on a track is shown to be inversely proportional to the curvature of the line it is attempting to follow. Low curvature allows for higher speed given that a car has some maximum lateral traction when cornering. The racing line can also be constrained, or “pinned,” at arbitrary points on the track. Pinning may be randomly, deterministically, or manually and allows, for example, a line designer to pin the line at any chosen points on the track, such that when the automatic algorithm is run, it will produce the smoothest line that still passes through all the specified pins.

Abstract: A target speed profile for a specified racer is computed at various points along a track. The calculation is based on the real world physics of the racing environment and incorporates physical characteristics of the track, including curvature, undulation, and/or camber. A lateral acceleration component is developed to limit the realistic maximum speed a racer may obtain at any given point along the track. Furthermore, differences in realistic maximum speeds at different points along the track can overwhelm a racer's braking capability. As such, braking capacity adjustments can be applied to decrease the maximum speed in the target speed profile, so that the overall target speed profile is more realistic and attainable.

Abstract: A target speed profile for a specified racer is computed at various points along a track. The calculation is based on the real world physics of the racing environment and incorporates physical characteristics of the track, including curvature, undulation, and/or camber. A lateral acceleration component is developed to limit the realistic maximum speed a racer may obtain at any given point along the track. Furthermore, differences in realistic maximum speeds at different points along the track can overwhelm a racer's braking capability. As such, braking capacity adjustments can be applied to decrease the maximum speed in the target speed profile, so that the overall target speed profile is more realistic and attainable.

Abstract: Improved human-like realism of computer opponents in racing or motion-related games is provided by using a mixture model to determine a dynamically prescribed racing line that the AI driver is to follow for a given segment of the race track. This dynamically prescribed racing line may vary from segment to segment and lap to lap, roughly following an ideal line with some variation. As such, the AI driver does not appear to statically follow the ideal line perfectly throughout the race. Instead, within each segment of the course, the AI driver's path may smoothly follow a probabilistically-determined racing line defined relative to at least one prescribed racing line.

Abstract: Improved human-like realism of computer opponents in racing or motion-related games is provided by using a mixture model to determine a dynamically prescribed racing line that the AI driver is to follow for a given segment of the race track. This dynamically prescribed racing line may vary from segment to segment and lap to lap, roughly following an ideal line with some variation. As such, the AI driver does not appear to statically follow the ideal line perfectly throughout the race. Instead, within each segment of the course, the AI driver's path may smoothly follow a probabilistically-determined racing line defined relative to at least one prescribed racing line.

Abstract: A computing system enhances the human-like realism of computer opponents in racing-type games and other motion-related games. The computing system observes multiple prescribed motion lines and computes switching probabilities attributed to switching of simulated motion of a racer from one prescribed motion line to another. A sampling module samples at random over the switching probabilities to select one of the switching probabilities. At least one control signal is generated to switch simulated motion of the entity in a virtual reality environment from the first prescribed motion line to one of the other prescribed motion lines, in accordance with the selected one of the switching probabilities.

Abstract: Improved human-like realism of computer opponents in racing or motion-related games is provided by using a mixture model to determine a dynamically prescribed racing line that the AI driver is to follow for a given segment of the race track. This dynamically prescribed racing line may vary from segment to segment and lap to lap, roughly following an ideal line with some variation. As such, the AI driver does not appear to statically follow the ideal line perfectly throughout the race. Instead, within each segment of the course, the AI driver's path may smoothly follow a probabilistically-determined racing line defined relative to at least one prescribed racing line.

Abstract: Improved human-like realism of computer opponents in racing or motion-related games is provided by using a mixture model to determine a dynamically prescribed racing line that the AI driver is to follow for a given segment of the race track. This dynamically prescribed racing line may vary from segment to segment and lap to lap, roughly following an ideal line with some variation. As such, the AI driver does not appear to statically follow the ideal line perfectly throughout the race. Instead, within each segment of the course, the AI driver's path may smoothly follow a probabilistically-determined racing line defined relative to at least one prescribed racing line.

Abstract: In a virtual reality environment, the behavior of the computer-controlled virtual vehicle may be made more human-like by increasing the AI driver's reaction time to environmental stimuli, such as physical stimuli (e.g., detecting a loss of tire traction, audio warning signals, smoke, virtual fatigue, weather changes, etc.) or “visual” stimuli (e.g., virtual visual detection by the computer driver of a turn or obstacle in its path, ambient lighting differences, etc.). Reaction time may be increased by introducing a delay in receipt of stimuli by the artificial intelligence motion control system, by introducing a delay in receipt of control signals by the physics engine, or by modifying the control signal to degrade their accuracy in approximating a prescribed racing line.

Abstract: Improved human-like realism of computer opponents in racing or motion-related games is provided. The computer driver may be “distracted” by various characteristics, such as nervousness caused by another competitor closing the gap behind a computer driver. The distraction effects may be reflected in the alteration of stimuli to represent the computer driver “missing” stimuli, as though the AI competitor has taken its virtual eyes away from the course in front of its vehicle for an extended time period in order to watch the racing vehicle behind it. In addition, some distractions may be caused by different directional stimuli. When it is determined that an AI driver has glanced into the rear view mirror, visual stimuli from in front of the vehicle may be skipped because a human driver would not be able to simultaneously process visual stimuli from both the front of the vehicle and the rear view mirror.

Abstract: An image super resolution system computes a high resolution image of a target from multiple low resolution images of the same target. Each low resolution image differs slightly in perspective from each of the other low resolution images. A coarse registration operation determines initial estimates of registration parameters (e.g., representing shifts and rotation in perspective) associated with each low resolution image. A fine registration operation improves the initial estimates using Bayesian analysis to infer the registration parameters and an acuity parameter. As such, a marginal likelihood of the low resolution images is optimized to determine the improved estimates of the registration parameters and the acuity parameter, which are used to solve for the high resolution image.

Abstract: Racing-based computer games typically include a mode in which one or more human players can compete against one or more computer-controlled opponents. For example, a human player may drive a virtual race car against a computer-controlled virtual race car purported to be driven by Mario Andretti or some other race car driver. Such computer controlled opponents may be enhanced by including a sampling of actual game behavior of a human subject into the opponent's artificial intelligence control system. Such a sampling can allow the game system to personalize the behavior of the computer control opponent to emulate the human subject.

Abstract: In a virtual reality environment, the behavior of the computer-controlled virtual vehicle may be made more human-like by increasing the AI driver's reaction time to environmental stimuli, such as physical stimuli (e.g., detecting a loss of tire traction, audio warning signals, smoke, virtual fatigue, weather changes, etc.) or “visual” stimuli (e.g., virtual visual detection by the computer driver of a turn or obstacle in its path, ambient lighting differences, etc.). Reaction time may be increased by introducing a delay in receipt of stimuli by the artificial intelligence motion control system, by introducing a delay in receipt of control signals by the physics engine, or by modifying the control signal to degrade their accuracy in approximating a prescribed racing line.

Abstract: Improved human-like realism of computer opponents in racing or motion-related games is provided. The computer driver may be “distracted” by various characteristics, such as nervousness caused by another competitor closing the gap behind a computer driver. The distraction effects may be reflected in the alteration of stimuli to represent the computer driver “missing” stimuli, as though the AI competitor has taken its virtual eyes away from the course in front of its vehicle for an extended time period in order to watch the racing vehicle behind it. In addition, some distractions may be caused by different directional stimuli. When it is determined that an AI driver has glanced into the rear view mirror, visual stimuli from in front of the vehicle may be skipped because a human driver would not be able to simultaneously process visual stimuli from both the front of the vehicle and the rear view mirror.

Abstract: Improved human-like realism of computer opponents in racing or motion-related games is provided by using a mixture model to determine a dynamically prescribed racing line that the AI driver is to follow for a given segment of the race track. This dynamically prescribed racing line may vary from segment to segment and lap to lap, roughly following an ideal line with some variation. As such, the AI driver does not appear to statically follow the ideal line perfectly throughout the race. Instead, within each segment of the course, the AI driver's path may smoothly follow a probabilistically-determined racing line defined relative to at least one prescribed racing line.