Autonomous driving test system under hybrid reality: The role of digital twin technology

Abstract

Autonomous vehicles have attracted attention as a result of enhancements in artificial intelligence, the Internet of Things, and communication technologies. There is a priority for new testing frameworks to keep up with the increasing complexity of decision-making, connectivity, data interchange, and data transmission speed. Traditional vehicle testing tools and methods cannot meet the new testing requirements imposed by the upgrading of autonomous driving (AD) technology. They are expensive, time-intensive, and present safety hazards during testing, as well as cannot simulate hybrid real-world situations and manage real-time data efficiently. Therefore, in terms of test efficiency, cost, and safety, a smart vehicle testing in hybrid reality and evaluation method based on a digital twin (DT) is presented to speed up the development and testing of AD functions. Our model uses three-dimensional coordinate mapping, a collision detection model, and virtual scene registration to plot the AD information in the actual environment to the virtual scenario. In addition, the consistent mixed reality-based AD test model is constructed at the same time. Using this model, we demonstrated that our proposal allows for better performance of an AD test. Furthermore, the collision test demonstrates that the mixed reality system has interactive features. The performance of the system under the sampling rate of 50 ms, 200 ms, and 800 ms is compared and analyzed. Also, the experiments show that the algorithm described in this paper works better when the sampling frequency is 200 ms or more.