Can combined virtual-real testing speed up autonomous vehicle testing? Findings from AEB field experiments

Proving ground testing has become a standard methodology for the development and validation of autonomous vehicles in the automotive industry. However, it suffers from inherent limitations in efficiency, cost, and scenario coverage. Combined virtual-real testing (CVRT) offers a promising alternative by integrating virtual scenarios with physical vehicles and the environment, enhancing scenario coverage and test flexibility. Nevertheless, few studies have systematically investigated its effectiveness and applicability. To address this gap, this study develops a digital-twin-based CVRT system and conducts consistency verification experiments, taking the autonomous emergency braking (AEB) system test as a case study. Four typical scenarios selected from C-NCAP (China New Car Assessment Programme) 2024 were tested at speeds of 30, 40, and 50 ​km/h, utilizing both real-world and CVRT methods, with each experiment repeated 15 times. Vehicle dynamics data were collected, and the Fréchet distance metric was used to quantify similarity, whereas statistical hypothesis testing was used to assess differences in time-to-collision (TTC) trigger times. The results show that the average Fréchet distance ratio between the CVRT and real-world tests almost approaches 1.0, and the differences in the TTC trigger times were not statistically significant. However, the results of the simulation experiments differed significantly from those of the real-world tests (0.528 ​m/s in speed and 1.150 ​m/s 2 in acceleration higher than the CVRT). Additionally, the data communication delay between the CVRT platform and the physical autonomous vehicle under test remained well below tolerable thresholds. These results indicate high consistency between CVRT and real-world testing. Furthermore, CVRT achieved considerable improvements in testing efficiency, saving approximately 40%–70% compared with real-world testing.