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At present, visual recognition systems have acquired wide employment in the autonomous-driving area. The lack of fully featured benchmarks that mimic scenarios faced by autonomous-driving system is the core factor limiting the visual understanding of complex urban traffic scenes. However, to establish a dataset adequately captures the complexity of real-world urban traffics consuming time and effort. In order to solve these difficulties, authors involve virtual reality to develop a largescaledoi:10.1049/joe.2019.1206 fatcat:3i42d67oprhuxalrliluit3a3q