The PreScan was used to construct and test environmental simulation sections for a vehicle sensor. It was employed to build simulation road sections or import real road sections and build virtual infrastructures, streets, and scenes, where vehicles, unexpected road incidents, traffic signals, human factors, light sources, and simulated weather changes were considered, which served as the basis for testing the vehicle sensor value using PreScan. The built-in PreScan vehicle modules were imported to the built virtual road sections and scenes, and cameras were installed at appropriate positions to measure the perspective of a moving vehicle. Furthermore, Simulink was used to model the vehicle sensors to be tested. Globally, tens of thousands of people have died in traffic accidents wherein more than half of the deaths were caused by drivers’ negligence. Therefore, traffic safety-related Advanced Driver Assistance Systems or autopilot systems have received increasing attention from vehicle manufacturers, with vehicle sensor performance being the key to safety. Previously, physical vehicles were mostly employed to test the vehicle sensors, which entailed high costs and risks. However, by employing PreScan in the tests, highly reliable real physical data can be obtained and high costs and risks associated with the use of physical vehicles could be avoided.

Chromatics provides a theoretical basis for the production of visual effects that is crucial to design education. As one of the pillars supporting chromatics, knowledge about color mixing is thus necessary for students who plan to design products. However, students tend to be bored by traditional learning methods in this regard. To remedy this deficiency, this study designed a mobile color mixing game to assist students in learning color mixing. On a more specific basis, this game not only enables students to learn color mixing anytime and anywhere but also provides them with an effective mechanism for exploring the potentials of this technology. Using the technology acceptance model, this study developed a questionnaire concerning the subjects’ perceptions of the mobile game, according to which the effectiveness of the game was evaluated. The research findings of this study showed that most of the subjects found the game not only easy to use but also of significant help for them to learn color mixing.

This paper adopts a reinforcement learning control using the frequency domain method for a SISO system with friction and input saturation process. The proposed method, which is based on two-dimensional system theory, investigates the robust stability criteria of the SISO iterative learning control system ({ILCS}) using frequency domain methods. The restrictive conditions for the stability of the {ILCS} are also derived. A design procedure for the {ILCS} is outlined. A numerical simulation example is given to demonstrate the utilization of obtained results.

Augmented reality technology is applied so that driving tests may be performed in various environments using a virtual reality scenario with the ultimate goal of improving visual and interactive effects of simulated drivers. Environmental conditions simulating a real scenario are created using an augmented reality structure, which guarantees the test takers security since they are not subject to real-life elements and dangers. Furthermore, the accuracy of tests conducted through virtual reality is not influenced by either environmental or human factors. Driver posture is captured in real time using Kinects depth perception function and then applied to driving simulation effects that are emulated by Unity3Ds gaming technology. Subsequently, different driving models may be collected through different drivers. In this research, nearly true and realistic street environments are simulated to evaluate driver behavior. A variety of different visual effects are easily available to effectively reduce error rates, thereby significantly improving test security as well as the reliability and reality of this project. Different situation designs are simulated and evaluated to increase development efficiency and build more security verification test platforms using such technology in conjunction with driving tests, vehicle fittings, environmental factors, and so forth. Augmented reality technology is applied so that driving tests may be performed in various environments using a virtual reality scenario with the ultimate goal of improving visual and interactive effects of simulated drivers. Environmental conditions simulating a real scenario are created using an augmented reality structure, which guarantees the test takers security since they are not subject to real-life elements and dangers. Furthermore, the accuracy of tests conducted through virtual reality is not influenced by either environmental or human factors. Driver posture is captured in real time using Kinects depth perception function and then applied to driving simulation effects that are emulated by Unity3Ds gaming technology. Subsequently, different driving models may be collected through different drivers. In this research, nearly true and realistic street environments are simulated to evaluate driver behavior. A variety of different visual effects are easily available to effectively reduce error rates, thereby significantly improving test security as well as the reliability and reality of this project. Different situation designs are simulated and evaluated to increase development efficiency and build more security verification test platforms using such technology in conjunction with driving tests, vehicle fittings, environmental factors, and so forth.

A virtual reality (VR) driver tracking verification system is created, of which the application to stereo image tracking and positioning accuracy is researched in depth. In the research, the feature that the stereo vision system has image depth is utilized to improve the error rate of image tracking and image measurement. In a VR scenario, the function collecting behavioral data of driver was tested. By means of VR, racing operation is simulated and environmental (special weathers such as raining and snowing) and artificial (such as sudden crossing road by pedestrians, appearing of vehicles from dead angles, roadblock) variables are added as the base for system implementation. In addition, the implementation is performed with human factors engineered according to sudden conditions that may happen easily in driving. From experimental results, it proves that the stereo vision system created by the research has an image depth recognition error rate within 0.011%. The image tracking error rate may be smaller than 2.5%. In the research, the image recognition function of stereo vision is utilized to accomplish the data collection of driver tracking detection. In addition, the environmental conditions of different simulated real scenarios may also be created through VR.