Due to the difficulty and high cost of conducting sufficient real-world road tests, it is widely accepted in the industry to use a digital twin testing system that combines virtual simulation testing with real-world road testing to test and evaluate autonomous driving systems. Digital twin testing can simulate a road test environment using technologies such as sensor simulation and vehicle dynamics simulation, advanced graphics processing, traffic flow simulation, digital simulation, and road modelling. We integrated simulation testing tools, V2X communication devices, and real testing vehicles to achieve digital twin autonomous driving testing based on virtual simulation and real environments. It generates scene data based on virtual scene simulation software, constructs simulated scenarios mapping to reality, and uses V2X communication devices to transmit simulated scene environment data in real time to the real testing vehicles. It also receives real-time status data from the vehicles and feeds them back to the scene controllers, enabling autonomous vehicles to test different scenarios on real roads. In active safety-related testing, virtual scenes can avoid accidents and save unnecessary equipment losses. The system has high repeatability, and after a large number of repetitive in-vehicle tests, it can significantly reduce functional defects in the tested system, reducing the workload of actual vehicle field testing and road testing and improving testing efficiency and cost savings.
Traffic congestion and delays are common problems in most urban areas, especially downtown, industrial and commercial distinct. The traffic condition affects economic development and people's quality of life in the distinct. Therefore, it is imminent to solve the road congestion problem. A significant factor in this problem is inefficient traffic signal timings for intersection, which is the hub of road traffic and plays a vital role in relieving the pressure of traffic. In this research, we propose a district-oriented traffic signal timing optimization algorithm that considers the characteristics of different districts and their traffic patterns. It improves traffic parameters of traffic control and improve road congestion problem by improving the traffic conditions of intersection. Our algorithm leverages real-world data from intersections and the type of transportation vehicles. We compare the performance of our algorithm with Webster classic algorithm using real-world data from smart town transportation system. The results show that our algorithm outperforms the other methods in terms that, the average green letter ratio increased by 7.77%, the saturation decreased by 67.6%, the delay time decreased by 59.08%, the traffic capacity increased by 6.1%, and the transportation capacity increased 68.51%.
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