Microsystem Technologies · 2018 · 26 citations · 12 references
EngineeringAutomotive EngineeringSystems EngineeringAutonomous VehiclesTransportationVehicle TestingAutomotive SensorsIntelligent Transportation SystemsSafety ValidationSimulation-based ValidationTraffic SafetyVehicle PerceptionAdvanced Driver Assistance SystemsAutonomous Vehicle Safety TestingDriver Assistance System TestingHuman Factors EngineeringSensor SimulationSensor-based ValidationSimulation-based TestingVehicle Safety
Abstract 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.
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Virtual reality and mixed reality for virtual learning environments
Zhigeng Pan, Adrian David Cheok, Hongwei Yang et al. · Computers & Graphics · 2005 · 604 citations
Mathias Kliem, Jan Høgsberg, Qian Wang et al. · Advances in Mechanical Engineering · 2017 · 143 citations · Full text
Natural Sciences, Materials Engineering, Materials Science +17