From training to testing, the virtual environment is making autopilots more and more perfect. In the training process, it can save us time, improve efficiency, and help us avoid the risks of testing in the real world. In the development of driverless, it is an indispensable means for human security personnel to test on the world road. When building a vehicle, whether it is driven by humans or computers, safety is a primary consideration. In the automatic driving development process, the unmanned driving technology needs to be continuously verified and tested under various driving conditions to ensure that the safety level is higher than that of the human driver. This means that at some point we have to test it on the actual road. Equally important, however, is simulation testing on virtual roads. Virtual testing is also an important means of accumulating test miles for driverless cars. Specifically, virtual road testing can effectively test dangerous or uncommon driving scenarios. The flexibility and versatility of virtual road testing make it an important player in the development of autonomous driving technology. If there is no simulated road test, it may be dangerous to observe the vehicle's response to real traffic scenarios. For example, when a child suddenly rushes from the back of a parked vehicle to the middle of the road, or another car is running a red light. Now thanks to the development of advanced graphics processing technology, engineers can simulate various real-life traffic scenarios and adjust them according to their needs. For example, when needed, we can simulate a blizzard scene, even if you are in a desert environment; in addition, we can simulate the midday glare at sunrise and sunset to test the driverless car here. The reaction under a variety of weather conditions. Second, there are scenarios that may put human testers at risk, and we can also reproduce them in virtual tests, such as simulating a highway that is covered with thin ice. Another advantage of simulation testing is the reproduction of multiple road conditions in a short period of time. In this year's keynote speech at the NVIDIA GPU Developers Conference in Europe, Nvidia CEO Huang Renxun said that using NVIDIA DGX and Tensor RT 3 for simulation, engineers can complete road tests of about 480,000 kilometers in 5 hours. At this rate, testing of all roads in the United States can be completed in just two days. In order for digital simulation technology to be used effectively in the training and testing of autonomous vehicles, we must ensure that the virtual world is infinitely close to the real world. Driven by the GPU, detailed imagery and a robust physics engine give engineers the advantages they need. When the simulation environment is successfully created, it must be connected to an automated driving system. NVIDIA's unified GPU architecture enables autopilot technology to migrate relatively easily between the real-loaded Drive PX platform and the simulation environment of a lab or big data center. Solutions from various companies DRIVE PX is an artificial intelligence in-vehicle computing platform that combines the data acquired by multiple sensors, runs complex software algorithms required for automated driving, and then sends decision commands to the vehicle for execution. After configuration, the DRIVE PX can also fuse the analog sensor data and output a simulated driving command. At the event of the NVIDIA Developers Conference Europe, some companies also demonstrated the method of automatic driving simulation test based on DRIVE PX. IPG uses Drive PX to test the ability to detect pedestrian targets in a simulated environment IPG AutomoTIve's Dominik D?rr introduces the concept of virtual prototypes and sensor models. The company's automated driving solutions provide unmanned engineers with a way to integrate multiple development efforts to test an overall driverless solution. According to D?rr, this solution allows engineers to test individual functions or deep neural networks early before a complete autonomous prototype is completed. These virtual prototypes are run on the DRIVE PX. The company configured DRIVE PX to work in a virtual environment. In the virtual environment, DRIVE PX performs the same analysis as in the real world, and then issues the corresponding driving instructions based on the analyzed results. In this process, engineers can evaluate the driverless solution to determine if it is working properly. VI-grade physics simulator can be used for virtual testing of low-level autopilot systems, which require human drivers to take over in an emergency. VI-grade's Roberto De Vecchi, together with its partner AddFor's Enrico Busto, discussed the accuracy of driving software and the impact of automotive software on people. They used a simulated driving device that combined the inputs of a human driver and autopilot software running on the DRIVE PX. When the car issues a takeover command, the human driver needs to take over the drive. The purpose of this test is to be able to judge whether the software is working properly and to evaluate the driving experience of the people inside the car. Data-driven autopilot simulation test Pro-SiVIC is ESI Group's simulation driving software. The company's Rodolphe Tchalekian introduced the software, which creates a real-time, physical reality 3D virtual environment that tests and trains machine learning algorithms. If you want to develop new machine learning algorithms for autonomous driving, companies need massive amounts of training data. If you collect this data from the real world, engineers need to spend a lot of time labeling the data before using it for algorithm training. And if it is virtual data, they are automatically added when they are created, which can save the company a lot of time and labor costs. After training the new algorithm with virtual data, ESI uses DRIVE PX to verify it and ensure that the software is working properly. TASS uses Drive PX to test the performance of lane keeping in virtual environments MarTIjn TIdeman from TASS InternaTIonal is demonstrating the company's PreScan simulation platform. PreScan is a simulation platform based on the physical world that evaluates autopilot and other vehicle functions. In the past, PreScan has been used to test driver assistance and V2X functions. Just recently, TASS began to use the PreScan data to train and validate the autopilot deep learning algorithm. TASS collaborated with the German Center for Artificial Intelligence and Siemens on a project that demonstrated the value of simulation data for deep learning algorithms. Tideman said that when training deep learning algorithms for autonomous driving applications, adding simulation data to real-world data can improve training efficiency. Cognata, a startup from Israel, has its own insights in the field of virtual testing for autonomous driving. The company used its proprietary algorithm to create multiple virtual cities with real vehicle and pedestrian behavior data. The company also added sensors in the virtual environment that use real-world data to ensure that they perform in a virtual environment, just like in the real world. From training to testing, the virtual environment is making autopilots more and more perfect. In the training process, it can save us time, improve efficiency, and help us avoid the risks of testing in the real world. In the development of driverless, it is an indispensable means for human security personnel to test on the world road. However, in addition to the actual road test, the virtual test can provide us with a good test supplement, and will not threaten the safety of the pedestrians and other vehicles.
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